COVID-19 poses a global health challenge with more than 325 million cumulative cases and above 5 million cumulative deaths reported till January 17, 2022, by the World Health Organization. Several potential treatments to treat COVID-19 are under clinical trials including antivirals, steroids, immunomodulators, non-specific IVIG, monoclonal antibodies, and passive immunization through convalescent plasma.The need to produce anti-COVID-19 IVIG therapy must be continued, alongside the current treatment modalities, considering the virus is still mutating into variants of concern. In this context, as the present study will exploit pooled diversified convalescent plasma collected from recovered COVID-19 patients, the proposed hyperimmune Anti-COVID-19 intravenous immunoglobulin (C-IVIG) therapy would be able to counter new infectious COVID-19 variants by neutralizing the virus particles. After the successful outcome of the phase I/II clinical trial of C-IVIG, the current study aims to further evaluate the safety and efficacy of single low dose C-IVIG in severe COVID-19 patients for its phase II/III clinical trial.This is a phase II/III, adaptive, multi-center, single-blinded, randomized controlled superiority trial of SARS-CoV-2 specific polyclonal IVIG (C-IVIG). Patients fulfilling the eligibility criteria will be block-randomized using a sealed envelope system to receive either 0.15 g/Kg C-IVIG with standard of care (SOC) or standard of care alone in 2:1 ratio. The patients will be followed-up for 28 days to assess the primary and secondary outcomes.This is a phase II/III clinical trial evaluating safety and efficacy of hyperimmune anti-COVID-19 intravenous immunoglobulin (C-IVIG) in severe COVID-19 patients. This study will provide clinical evidence to use C-IVIG as one of the first-line therapeutic options for severe COVID-19 patients.clinicaltrial.gov with NCT number NCT04891172 on May 18, 2021.Registered at  The order of the items has been modified to group similar items  in severe COVID-19 patients after observing safety and efficacy of C-IVIG in phase I/II clinical trial of severe and critical COVID-19 patients .This trial aims to investigate the safety and clinical efficacy of hyperimmune anti-COVID-19 Intravenous Immunoglobulin (C-IVIG: 5% liquid formulation), on severe COVID-19 patients.This is an adaptive, phase II/III, multi-center, randomized controlled, single-blinded superiority trial with parallel group design to assess the safety and efficacy of single dose of C-IVIG in patients with symptoms of severe COVID-19. Eligible participants will be randomly assigned in 2:1 ratio (206 tests: 103 controls) by block randomization method to receive C-IVIG plus standard of care (intervention group) or only standard of care (control group). The adaptive study design refers to adapting the strategy of trial based on interim analysis at three particular time points . OutcomeThe trial will be conducted in Karachi, Pakistan at Dow University of Health Sciences (Ojha Campus) and Sindh Infectious Disease Hospital and Research Center (SIDH).A total of 309 participants from both male and female gender will be recruited according to the following inclusion and exclusion criteria.Study participants should be above 18 years of ageHave positive SARS-CoV-2 PCR on nasopharyngeal and/or oropharyngeal swabsClassified as severe COVID-19 according to WHO guideline (5L-15L of oxygen requirement on face mask (FM), Non-rebreather mask (NRM)Have signed the consent to participate in the studyInclusion criteria for participant:Participants will be excluded if they have critical COVID-19 [non-invasive ventilation  and invasive ventilation]Pregnant femalesPrevious allergic reaction to immunoglobulin treatmentKnown case of any autoimmune disorderChronic renal diseaseKnown case of thromboembolic disorderAseptic meningitisExclusion criteria for participants:Consent will be taken by the Clinical Research Associate, or the treating physician on a document designed as per guidelines of Drug Regulatory Authority of Pakistan (DRAP). The participant itself or first-degree relative is eligible to provide consent.Provisions to collect and use participant’s data and biological specimens are mentioned in informed consent form.The intervention group will receive a single dose of C-IVIG (0.15 g/Kg) along with standard hospital care and participants will be assigned in a sequential manner. C-IVIG is a 5% concentrated preparation of anti-SARS-CoV-2 antibodies purified from fractionation of pooled convalescent plasma of COVID-19 survivors . HyperimIntervention group will receive a single dose of 0.15 g/kg body weight C-IVIG along with standard care. Control arm will receive standard hospital care only. The dose will be administered intravenously with a flow rate of 20 ml/h in the first hour and 25 ml/h till the dosage end. Standard hospital care includes airway support, anti-viral medication, antibiotics, fluid resuscitation, hemodynamic support, steroids, painkillers, and anti-pyretic. Standard of care will ensure blinding because as part of the usual hospital care severe COVID-19 patients is given multiple infusion, and hence, it will not be possible for patients in intervention group and control group to discriminate between standard of care and C-IVIG infusion.Modification of the allocated intervention will be based on the planned interim analysis. Outcome of the interim analysis will lead to making decision regarding selection of higher C-IVIG dose in case of currently selected 0.15 g/kg dose not found efficacious. The higher dose of C-IVIG will be selected from the dosage investigated in previous phase I/II clinical trial . The C-IIt is not applicable because the drug will be given as a single dose intravenously by respective healthcare professionals.Standard hospital care including all concomitant care and interventions are permitted.No provisions are made for ancillary and post-trial care.28-day mortality [time frame: 28 days]All-cause mortality of participants will be monitored for 28 days to assess the safety and efficacy of C-IVIGPrimary outcome:Immediate (within 24 h) or serious adverse events  [time frame: 28 days]Not hospitalized and no limitations of activitiesNot hospitalized, with limitation of activities, home oxygen requirement, or bothHospitalized, not requiring supplemental oxygenHospitalized, requiring any supplemental oxygenHospitalized, requiring noninvasive ventilation or use of high-flow oxygen devicesHospitalized, receiving invasive mechanical ventilationDeathClinical status according to 7 category ordinal Scale [time frame: 28 days 2/FiO2 ratio use to assess lung function) during hospital stay [time frame: 28 days]Change in Horowitz index (PaOChange in C-reactive protein (CRP) levels, radiological findings, IL-6 level, and anti-SARS-CoV-2 antibody levels [time frame: 5 days]Secondary outcomes:The timeline of enrollment and intervention is given in Table www.riskcalc.org) taking 99% confidence level (11).Three hundred nine participants will be enrolled in the study. Sample size was determined by taking proportion of survival in intervention group (P1) = 75%, proportion of survival in control group (P2) = 40%, power of test (1-β) = 90%, with the sample ratio 2:1 (test to control), superiority margin 0.15, and drop rate 30%. The calculated total sample size is 309 patients, 206 tests and 103 control. The calculated sample size was determined with the help of online sample size calculator  by sequentially numbered opaque sealed envelopes using block randomization method, either to receive C-IVIG plus SOC (intervention group) or only SOC (control group). A randomization list is generated by a statistician unrelated to this study, while the study personnel will be unaware of the sequence of assignments. At the time of randomization, the study personnel will receive a sealed opaque envelope with assignment to intervention or control group.Participants will be enrolled by the clinical research associate or by treating physicians of each study site. Participants will be assigned to interventions by investigators according to a randomization list.This is a single-blinded trial in which trial participants are blinded.Unblinding is not needed because the trial is single blinded and only patients will be blinded. Unblinding will only be permissible by study investigators when patient selects to leave against the medical advice (LAMA) to seek treatment in another hospital and the information is important for the safe management of the patient.Outcome, baseline, and other trial data will be collected by the study investigators. The clinical research associate will check the data. Nasopharyngeal/oropharyngeal swabs and blood samples will be sent to DUHS laboratory, where tests will be performed according to laboratory standard operating procedures (SOPs). Data reports can be found on HMIS  and will be collected on clinical research forms (CRF).Designated personnel from the trial staff will be appointed to ensure the participant retention and complete follow-up during the participant’s hospital stay and after discharge as well. After a participant’s hospital discharge, participants will be followed on call. The participant can withdraw from the trial at any point. The decision will be communicated with the study investigator; the clinical research associate should be explaining the importance of staying in the trial for the full duration of follow-up. For withdrawal, there is a form in CRF that will be filled by the clinical research associate with reasoning why the participant is withdrawing from the trial. If the participant will be lost to follow-up, the data that will be collected till the time of loss to follow-up will be assessed and used in the analysis.If the patient will withdraw due to AEs, the clinical associate or the treating physicians will closely monitor the follow-up of AEs until the participants return to the baseline state or the participant condition returns to normal. Then the AEs will be reported to the study investigators.Data will be collected from the online Hospital Management Information System (HMIS) and hospital record files from all clinical trial sites. All relevant data from hospital files will be manually entered in record sheets and CRFs by designated project personnel. The data from record sheets and HMIS will then be transferred to MS Excel sheets. The data files will be accessible to the appointed statistician for data analysis and interpretation.The hospital record files and HMIS software will be accessible to designated members of the clinical trial team only. All data files will be secured at the office of Clinical Trial Unit (CTU), Dow University of Health Sciences.Biological specimens of hospitalized patients will be collected and sent to the laboratory by on-duty staff according to the given schedule and as recommended by the treating physician. Discharged patients will be contacted via phone call and their samples will be collected by the study investigators. All samples will ultimately be sent to the laboratory or at predetermined storage conditions.U test or independent t-test. Categorical data will be compared with chi-square test, and value of the two-sided Fisher exact test will be recorded. An estimate of the relative risk and 95% confidence interval will also be reported. Hypothesis testing will be conducted at a significance level of 0.05.Patients will be randomized at a ratio of 2:1 to receive either C-IVIG with standard of care or standard of care only. The data to be analyzed will be blinded using the SPSS software 24.0. All randomized study participants will be included in the intention-to-treat population and all participants completing the study period will be analyzed . Normality of continuous data will be checked by the Shapiro-Wilk test, and parametric or non-parametric tests will be applied accordingly. Continuous variables will be presented as mean (± SD) or median (interquartile range), and categorical variables will be presented as percentages. In the primary analysis strategy, we will use the Kaplan–Meier curve (Breslow test) that compares the time to reach the primary end point in the trial groups. Hypothesis testing of outcomes for the assessment of safety and efficacy parameters will be conducted by Mann-Whitney An interim analysis will be performed after recruitment of 15%, 30%, and 60% participants in the trial. It will assess primary outcomes of the study particularly focusing on the safety and efficacy of the infused drug. Safety of the intervention will be monitored in terms of any immediate or serious adverse event that can be observed after the administration of the drug. The efficacy of the intervention will be noted in terms of the patient's improvement in clinical status as compared to the control group.No subgroup analyses have been planned for this trial.To handle the missing data multiple imputation will be used or complete case analysis will be used. The primary analysis will use the intention-to-treat principle.The datasets analyzed during the current study and statistical codes are available from the corresponding author on reasonable request, as is the full protocol.The study will be conducted in Dow University Hospital, Karachi, Pakistan, and Sindh infectious disease Hospital, Karachi, Pakistan.Adverse events (AE) and serious adverse events (SE) will be observed and reported from the time of patient’s enrollment in the study till the outcome day, i.e., 28 days. AE/SE will be reported in the clinical research forms which include the time of the AE/SE medications, severity, duration of the AE, and the outcomes after AE. The relevant information of adverse events, after investigating if it is drug related or not, will be reported within 24 h to the principal investigator and to the National Drug Authorities.The Clinical Research Organization (CRO) “Institute of Biological and Biochemical and Pharmaceutical Sciences (IBBPS), DUHS, Karachi” will be responsible for the auditing and monitoring of the trial. Audits may be conducted at any time during or after the study completion.clinicaltrial.gov and will be communicated to the journal where trial protocol has been published. After any protocol amendment, the informed consent form and any other written information provided to the subjects will be updated as necessary.The modification/amendment in the protocol of this trial will be informed to the national authorities and regulators by the principal investigator. The changes will be then implemented after the approval from the authorities. Protocol amendments will also be done on trial registries such as The results will be reported to the national authorities such as National Bioethics Committee (NBC) and Drug Regulatory Authority of Pakistan (DRAP) and will be published in the reputed scientific journals, to communicate trial results to participants, healthcare professionals, the public, and other relevant groups.This phase II/III clinical trial is an adaptive, randomized controlled, multi-center, single-blinded superiority trial of SARS-CoV-2 specific polyclonal IVIG (C-IVIG) versus standard of care for patients hospitalized with severe COVID-19. Single dose of C-IVIG in combination with the standard of care was found to be both safe and efficacious in our previous clinical trial phase I/II while increasing the survival rate and reducing the risk of disease progression . There wStudy participant selection for this study is based on the results of our previous clinical trial where severe patients had 100% survivability and early hospital discharge than critical patients and comparator group. We evaluated that hospital stay, reliance on mechanical ventilation, and burden on hospital could be reduced if C-IVIG is administered to the severe COVID-19 patients by delaying the disease progression.This is an adaptive clinical trial to investigate the risk-benefit analysis of C-IVIG administration for severe COVID-19 at first and second interim of the study. Major outcome variables to be assessed for interim analysis are adverse events, 28-day mortality, and change in clinical status of the patients. Fate of this clinical trial is based on whether the C-IVIG is safe and efficacious. If the treatment modality is found to be safe but not efficacious as compare to comparator group, we might select different dosage of C-IVIG for further study participants; if interim analysis indicate association of serious adverse events with administration of C-IVIG or with significant high mortality ratio in intervention group than comparator group or if incidence rate of COVID-19 is low till the study completion date, then this clinical trial would be early terminated due to aforementioned reasons.Study start date: August 1, 2021Protocol version: 3Anticipated completion date: December 2, 2022


The Positive Memory Training (PoMeT) trial demonstrated reduced depression symptoms at 3 months for schizophrenia, but its longer-term outcome and cost impacts remain unknown. This study is a within-trial cost-utility analysis with quality-adjusted life years (QALYs) as outcome based on health-related quality of life (HRQoL) measurement and secondary outcome analyses of capability well-being. The incremental cost-effectiveness of PoMeT was compared to Treatment As Usual only (TAU) over 9 months from the ‘health and social’ care and ‘societal’ perspectives. Uncertainty was explored using bootstrapping and sensitivity analyses for cost outliers and outcome methods. HRQoL improvement was observed for both PoMeT and TAU at 3 months, but reached statistical significance and was sustained only for TAU. There was no change in capability well-being and no significant group difference in QALYs gained over 9 months. Mean intervention cost was GBP 823. Compared to TAU, PoMeT had significantly higher mental health care costs  during the trial, but ‘health and social care’ and ‘societal’ cost differences were non-significant. Compared to the before-trial period, psychiatric medication costs increased significantly in both groups. The probability of PoMeT being cost-effective in the given format over 9 months was <30% and decreased further in sensitivity analyses.. Generalizability remains limited since the before-after cost analysis revealed additional treatment effects also in the TAU group that likely diminished the incremental impacts and cost-effectiveness of PoMeT. It is not clear whether an active post-intervention follow-up could result in sustained longer-term effects and improved cost-effectiveness. Schizophrenia occurs in approximately 1% of the UK population and accounts for 3% of the NHS budget, or approximately GBP 2.4 billion per year in direct health and social care [Positive Memory Training (PoMeT) for the treatment of depression in schizophrenia is a short, structured cognitive therapy intervention. A previous form of the intervention has been used successfully to treat auditory hallucinations in people with schizophrenia . In thisThis study aimed to evaluate the health-related quality-of-life (HRQoL) and capability well-being outcomes, the cost impacts, and the cost-effectiveness of PoMeT, in comparison to Treatment As Usual only (TAU) delivered by mental health professionals from within the NHS Trusts as per local protocols over 9 months, based on evidence from the PoMeT trial ,7. It isThis within-trial economic evaluation was conducted prospectively alongside the PoMeT trial . The PoMThe primary outcome of the health economics analysis was the Quality-Adjusted Life Year (QALY). Health-related quality-of-life (HRQoL) was assessed using the standardized, self-reported EQ-5D-5L questionnaire . This geSome studies suggest that for some severe mental health disorders, including schizophrenia, the EQ-5D may not be sensitive enough and may be a too narrow measurement of QoL. Instead, the use of broader well-being measures should be considered . In thisMissing data in all outcome measures were under 5% and were balanced between the groups, therefore, multiple imputation was deemed unnecessary. Instead, imputation of the few missing values was based on last-value carried forward in the main analysis and then subjected to sensitivity analysis.Resource use data were obtained for four consecutive three-monthly periods (3 months prior to baseline (-M3) to baseline (M0), baseline (M0) to 3 months follow-up (M3), 3 months (M3) to 6 months (M6) follow-up, and 6 months (M6) to 9 months (M9) follow-up) using an amended version of the patient self-completed Client Service Receipt Inventory (CSRI) , and then = 10) who had both self-reported and electronic records-based data available and showed high levels of agreement. The data collection error was equally distributed between the two groups.There was no randomly missing resource use information. However, due to a data collection error, 17% of the three-monthly resource use data had to be collected retrospectively from electronic patient records. A positive reliability check of the different data collection methods was carried out based on a sample of patients  in terms of the difference on costs (GBP) divided by the difference in QALYs between the PoMeT and the TAU groups from a ‘health and social care’ perspective and from a broader ‘societal’ perspective . OrdinarThe effect of cost outliers in each cost category was explored in a sensitivity analysis. Sensitivity analysis was conducted also on the method of missing outcome data imputation using average before and after values at individual patient level instead of last value carried forward. Furthermore, EQ-5D-5L utilities were recalculated using the UK cross-walk value set .t-test, chi-square test, or Fisher’s exact test, depending on the type of the variable. For comparisons of the before and during trial periods, paired t-tests were used. Means with standard deviations (SD) or mean differences with 95% confidence intervals (95% CI) were reported. A p-value of < 0.05 was considered statistically significant. All analyses were conducted using the STATA/MP version 14.2  and Microsoft Excel 365 ProPlus.All analyses were carried out according to the intention-to-treat principle. Potential differences in patient characteristics were investigated using There were no statistically significant differences between the two groups in terms of any of the investigated characteristics . The majMain HRQoL results (EQ-5D-5L and EQ VAS) showed improvement for both groups at 3 months, however, the initial utility improvement (+0.036 for PoMeT and +0.059 for TAU) reached statistical significance and was sustained at 6 and 9 month follow-ups only in the TAU group in comparison to baseline . No signComparing costs incurred during the trial period with those extrapolated costs that were incurred before the trial period, significant increases were observed in the cost of psychiatric medications in both groups . On averRegression adjusted incremental cost, QALY and cost-utility results are reported in No difference to the above conclusions was found in the sensitivity analyses where outcome imputation was based on the before and after periods rather than the last value carried forward method, or when EQ-5D-5L utilities were based on the UK cross-walk value set. In the outlier sensitivity analysis, 10 participants were found with cost items that were considered as extreme outliers. All but one outlier cost occurred in the TAU group. For example, one participant in the TAU group frequently visited an occupational therapist with a relevant cost of GBP 3748 over 9 months, and one TAU participant had extreme non-mental health inpatient cost of GBP 39,717, which was more than double that of any other participant. Adjusting for these cost outliers increased the cost difference between the PoMeT and the TAU groups showing PoMeT significantly more expensive from the health and social care perspective, with a tendency of being more expensive also from the societal perspective . Cost-efIn a previous review, the average minimal clinically important difference for EQ-5D across all diseases was found being 0.18 . SimilarOn the other hand, the economic evaluation found evidence of increased psychiatric medication taking during the trial period in both groups, indicative of a confounding trial effect in the form of increased treatment also in the TAU group. In addition, the TAU group had on average much higher non-mental health care costs during the study period with some high-cost outliers indicative of intensive alternative service use. These factors potentially explain the reasons behind the sustained average HRQoL improvement observed in the TAU group likely contributing also to the almost GBP 2000 per person reduction in informal care costs in this group, revealed in the before-after analysis. Overall, these trial effects likely diminished the incremental impacts and cost-effectiveness of PoMeT in the current study.Unexpected clinical improvements have been reported within the control group of a number of trials of psychological therapies for severe mental health problems , though This study has several limitations. Firstly, it had a relatively small sample size. This poses a problem in that some participants may significantly influence the overall costs and do not allow all cost impacts of the intervention to be detected fully. Although outliers were adjusted for in a sensitivity analysis, a considerable amount of uncertainty remains about these cost impacts, especially for cost categories where only a few participants had actual resource use, or even for outcomes. Another limitation is the necessary use of information from electronic patient records to augment non-randomly missing data on health care resource use. While reliability of this method was tested on a sample for those where information was available from both sources with positive results, the true comparability of these data to those obtained by self-report remains unknown. Furthermore, all cost data refer to year 2016/17. Although this aspect does not impact the validity of the current cost-effectiveness results and conclusions, these costs are not fully reflective of current unit prices. Finally, the study had an overall follow-up of only 9 months that does not allow drawing long-term cost-effectiveness conclusions. Nevertheless, the declining clinical and quality-of-life impacts beyond the initial treatment period suggest that a longer follow-up would have not resulted in different conclusions.It is important that any conclusions from these findings take into consideration the above limitations as well as the given trial context. The current intervention is based on a specific protocol that targets specific cognitive mechanisms over 3 months. Therefore, the outcome associated with PoMeT is not indicative of those that may be associated with a wider range of therapies, such as generic CBT and third wave cognitive behavioral interventions, such as mindfulness and acceptance-based therapies. Several of these have been included in a recent systematic review of interventions for treating depression and anxiety symptoms in patients with schizophrenia . HoweverIn conclusion, although the economic evaluation of the PoMeT trial showed an overall negative result, this study shed light on many potential aspects of the PoMeT trial that could influence both the main clinical and the cost-effectiveness results, and would have not been revealed from the clinical analysis alone. It further highlights the importance of considering clinical evidence together with health economic evidence when making health care decisions on new treatment options from trial-based evidence, or deciding on future research priorities and optimizing research designs, especially in the mental health field.


BackgroundRecommendations to balance cancer care with patient and hospital staff safety have been issued to hospitals during the coronavirus disease (COVID-19) pandemic. Concerns have been raised that service restrictions could jeopardize effective cancer management. Thus, this study aimed to conduct an audit to verify this proposition.Methods2). A p-value of <0.05 at 95% CI was considered significant.We conducted an audit comparing two-week wait (2ww) breast cancer referrals in our center between May and July 2019 and 2020. The primary endpoints were changes in the overall referral rates, differences in the waiting time, and breast cancer diagnosis rates between the two cohorts. Group differences were evaluated using the chi-square test  in 2020 . Referrals in 2020 were associated with a higher rate of two-week specialist consultation than referrals in 2019 (p<0.05). The 2020 patient cohort was associated with a higher rate of breast cancer diagnosis than the 2019 cohort . Of the 521 patients who had telephone consultations, 29.2% were discharged, and 367 (70.4%) had post-telephone one-stop clinic visits, of which 9.0% had breast cancer.ConclusionsThe audit provided evidence of effective breast cancer services during the COVID-19 pandemic restrictions. The study results could inform patients and the general public at large that the waiting time and breast cancer diagnosis are not compromised during COVID-19 pandemic management. The high rates of post-telephone one-stop clinic visits and cancer diagnosis may indicate weakness in triage and difficulties in diagnosing nonspecific presentation of cancer over the telephone. The coronavirus disease COVID-19) pandemic presents novel challenges to the regular operation of healthcare delivery services worldwide 9 pandemi. HospitaThe configuration of services to manage COVID-19 has been viewed as skeptical. Concerns have been expressed regarding service restrictions owing to the management of COVID-19, which may lead to delays in cancer diagnosis in general and worsen survival -10. A suWe used a prospective breast database to audit the management of 2ww breast referrals during the COVID-19 pandemic. The 2ww is a UK cancer initiative that ensures that all suspected cancer cases are seen by the specialists within two weeks of referrals by the general practitioners (GPs). We compared the overall 2ww breast referrals, waiting times to clinics, and rate of breast cancer suspicion and diagnosis between May-July 2019 and May-July 2020. The relationship between patient demographics and cancer diagnosis was investigated. Additionally, we examined the rate of telephone consultations, the difference in waiting time between telephoned and face-to-face patients, the rate of breast cancer diagnosis among telephoned patients, and the clinic follow-up rate after a telephone consultation.SettingSpecialist breast clinics in our trust have been running for more than two decades. GPs refer patients to breast clinics with a designed pro forma, with or without free text, and approximately 300 patients are referred to our breast clinic every month.The patients were seen in one-stop breast clinics following the GP’s referrals. All investigations, including ultrasonography, mammography, and core or punch biopsies, were performed at the first clinic visit. Within two weeks of the clinic visit, the biopsy results are discussed at our weekly multidisciplinary team meeting, and the patients are seen at the clinic or telephoned and discharged if all issues are resolved. All symptomatic breast cancer referrals are seen with 2ww priority in our center since an increasing number of breast cancer cases are diagnosed among routine breast cancer referrals .Response to the COVID-19 pandemicThe consultants triaged referrals to the one-stop clinic for suspicious cases or telephone clinics for low-risk cases. Telephoned patients were advised to self-manage and were discharged from the breast service if their issues were resolved or offered a face-to-face follow-up appointment for physical review at the one-stop clinic if further information was required. Some patients were sent for investigations at telephone clinics.Breast registrars performed telephone consultations, which did not follow a uniform model, and no training in telephone consultation was provided. During the lockdown, surgeries for cancer and postoperative complications were performed, while cosmetic and reconstructive procedures were suspended. The selected patients were managed with hormone therapy as a temporary measure.ParticipantsEligible patients were new symptomatic referrals identified on the prospective breast cancer database during the following study periods: May 1, 2020, to July 31, 2020, and May 1, 2019, to July 31, 2019. The breast cancer database’s information included GP suspicion of cancer, patients’ age and sex, date of referral receipt, date of specialist consultation, consultation mode (face-to-face or telephone), clinical outcome, and diagnosis. The patients’ medical records, including corresponding letters and the results of the investigations, were maintained on the trust website. Patients with new symptoms were included in the analysis. In contrast, rereferrals, dermatological lesions, and referrals from other specialties were excluded.Data collectionInformation collection from the breast cancer database and patient medical records was performed using a data-extraction sheet. The extracted information included patient demographics (age and sex), waiting time to clinic, reason for referral, consultation mode, telephone consultation outcome, month and year of referral, and diagnosis.EndpointsPrimary endpoints: The primary endpoints include a change in the overall referral rate, difference in the waiting time to clinic, and rate of breast cancer diagnosis between the two cohorts of patients.Secondary endpoints: The secondary endpoints include the percentage of referrals who underwent telephone consultation in 2020, the difference in the waiting time to the one-stop clinic between telephoned patients and patients who underwent face-to-face consultation, the proportion of patients who underwent clinical follow-up after the telephone consultation, and prevalence of breast cancer diagnosis among the telephoned patients.Defining the variablesWaiting time to the clinic (days): This was defined as the time from when a referral letter was received to the time of consultation with a breast specialist (face-to-face or telephone). Information was retrieved from the breast cancer database. The length of waiting time was dichotomized to 1 to 14 days (≤ 2 weeks) and 15 days and above (> 2 weeks).GP diagnosis: This refers to the suspected diagnosis by the GP, described as suspected or unsuspected breast cancer. Information was retrieved from the GP referral letter.Specialist diagnosis: A benign diagnosis was characterized by normal or hormonal breast changes and benign breast lesions on imaging or pathology. Breast cancer was defined as an invasive carcinoma of the breast, established on imaging and histology.Consultation mode: This refers to either face-to-face or telephone consultations.Telephone consultation outcome: Telephone consultation outcome refers to whether the patient was discharged to the GP, scheduled for a different face-to-face clinic after a telephone consultation, or referred for further investigation.A follow-up one-stop breast clinic: This refers to the face-to-face follow-up consultation offered to patients after the initial telephone consultation.Statistical analysisDescriptive statistics are used to describe patient and referral characteristics, and categorical data are reported as percentages. We used the chi-square test (χ2) to evaluate the differences between categorical variables. Differences between variables were considered significant at a p-value lower than 0.05 (two-tailed) at the 95% confidence interval (CI). Statistical analysis was performed using the Statistical Software for Social Sciences (SPSS) version 26 .Ethics and confidentialityThis audit study was approved by the Trust Audit Department, and informed consent was not required, as anonymized data were used.The results of the 2ww referrals are first presented, followed by the results of the telephone consultations.Characteristics of two-week wait referralsParticipant demographics: Of the 2,686 referrals during the study period 2019-2020), 2,516 (93.7%) were women, and 170 (6.3%) were men. There was no sex difference in referral rates between the 2020 and 2019 cohorts . We noted a slight increase in the number of referrals in patients under 30 years of age but a decrease or no increase in the older groups  referrals in 2019 and 1,122 (41.8%) referrals in 2020. The number of referrals was reduced by 442 (28.3%) in 2020.Waiting time in 2019 versus 2020: Patients referred in 2020 achieved higher consultation rates within two weeks than those referred in 2019 .Breast cancer diagnosis: Of the 153 patients diagnosed with cancer, 76 (4.9%) were diagnosed in 2019, and 77 (6.9%) were diagnosed in 2020. There was a higher rate of breast cancer in the 2020 cohort than in the 2019 cohort . No case of cancer was diagnosed in patients aged ≤30 years.Secondary endpointsTelephone consultation rate: The telephone consultation was initiated during the COVID-19, coronavirus infection. Of the 1,122 referrals in 2020, 521 (46.4%) were triaged for telephone consultation and 601 (53.6%) for one-stop clinics .Breast cancer diagnosis: No significant difference in the rate of breast cancer diagnosis was observed between the one-stop clinic and telephone consultation patients . The stage of the breast cancer was not assessed in this study.Follow-up rate after telephone consultation: Of the patients who underwent telephone consultations, 152 (29.2%) were discharged, 367 (70.4%) underwent clinical follow-up, and two (0.4%) underwent further investigations. Ninety-one percent (334/367) of patients who underwent follow-up did not have breast cancer, and 33 (9.0%) had a cancer diagnosis.Overall, the 2ww referrals reduced by 28.3% in 2020 compared with those in 2019. Other studies have recorded fewer urgent and symptomatic referrals during the pandemic -18. We nThere is no statistically significant difference in the wait time and cancer diagnosis rate between the telephone and one-stop clinic patients, suggesting that telephone consultation is not inferior to face-to-face consultation. Several studies have documented the effectiveness of telephone consultation within  and outsThe improvement in breast cancer survival is linked to screening programs and early diagnosis of symptomatic cases via a 2ww pathway . The COVWe have used robust referral and diagnostic data from the breast cancer prospective dataset. The audit results accurately reflect the policy of COVID-19 management on breast cancer referrals and diagnoses in our center. However, our study has limitations. The audit represents the experience of a single specialist breast center, and the results may not be generalizable to other centers. This study has not reported the number of missed cancers among the telephoned and discharged patients, as with previous studies , 27. ThiThe COVID-19 pandemic exerted profound health care and socioeconomic effects worldwide. The study used a prospectively collected dataset to demonstrate the results of our approach to the management of 2ww referrals during the COVID-19 restrictions. The audit results suggest a future increase in the referral rate and the incidence of advanced breast cancer. This also indicates that telephone consultation is as effective as the one-stop clinic in terms of waiting time and cancer diagnosis. The effectiveness of the triage system during the pandemic is partly related to the resilience of breast services and the diligence of clinicians. The COVID-19 pandemic will continue to be the subject of research in the future, and extensive learning during the pandemic will help improve our service organization and delivery in the future. It is expected that the telephone consultation approach may witness the development of best practices and ethical guidelines agreed upon by all stakeholders.


Clear health disparities have emerged in the rates of COVID-19 exposure, hospitalization, and death among Black, Hispanic, and American Indian (BHAI) individuals, relative to non-Hispanic White (NHW) individuals. BHAI populations have been disproportionately affected by lower behavioral health access and heightened negative mental health outcomes during the pandemic.This project directly addresses health disparities in access to behavioral health care during the COVID-19 pandemic among BHAI populations via an adaptation of the established, initially validated, low-cost, mobile app Easing Anxiety Sensitivity for Everyone (EASE) among individuals with symptoms of elevated anxiety or depression or both.The EASE trial is a 2-arm, prospective, randomized, blinded-assessor study with intention-to-treat analysis. Participants  are randomized to receive either EASE or an active comparison condition for anxiety and depression. Participants compete an online prescreener, an enrollment call to provide informed consent, a baseline survey, a 6-month intervention period, and 3- and 6-month postbaseline assessments. Select participants also complete a 3- and 6-month postbaseline qualitative interview via phone or an online platform . Participants complete 2 scheduled daily ecological momentary assessments (EMAs) during the 6-month study period. These twice-daily EMAs guide a just-in-time approach to immediate, personalized behavioral health care.Outcomes include reductions in anxiety and depressive symptoms and functional impairment at 3 and 6 months postrandomization. We also will examine putative mechanisms  of the intervention effects. Further, as treatment effects may differ across sociocultural factors, perceived discrimination, social support, and socioeconomic status (SES) will be evaluated as potential moderators of treatment effects on the primary outcomes. Process evaluation using data collected during the study, as well as individual interviews with participants, will complement quantitative data.Data from this efficacy trial will determine whether EASE successfully improves symptoms of anxiety and depression and whether these improvements outperform an active comparison control app. If successful, findings from this study have the potential to decrease anxiety and depression symptoms among vulnerable populations determined to be most at risk of exacerbated, long-lasting negative health sequelae. Data from this study may be used to support an implementation and dissemination trial of EASE within real-world behavioral health and social service settings.ClinicalTrials.gov NCT05074693; https://clinicaltrials.gov/ct2/show/NCT05074693DERR1-10.2196/40713 Significant racial/ethnic health disparities have been identified in the United States  related The ongoing COVID-19 pandemic has led to increases in stress, including stress related to unemployment, homelessness, food insecurity, and lack of adequate health care resources. The COVID-19 stress burden may have a particularly pernicious influence in persons already struggling with greater stress exposure and pre-existing psychopathology . TheoretAnxiety sensitivity (AS), defined as the fear of anxiety symptoms, including bodily sensations , has beeImportantly, AS is malleable in response to psychosocial interventions , making A recent meta-analytic review found that mHealth interventions for anxiety and depression reduction may lead to significant within-person reductions in anxiety and depression, and the reduction in anxiety was greater than what was reported by control participants . The majThis study capitalizes on our expertise in mHealth interventions, mental health treatments, and health disparities research. We propose to test an accessible mHealth AS reduction treatment that incorporates a just-in-time treatment approach to address the mental health sequelae resulting from the COVID-19 pandemic. The primary aim of this study is to test the effects of a novel, smartphone-delivered intervention (Easing Anxiety Sensitivity for Everyone [EASE]) aimed at reducing mental health disparities across racial/ethnic minority groups that have been exacerbated by the COVID-19 pandemic in a large, racially/ethnically diverse sample of adults with clinically significant anxiety or depression. The comparison condition is a time-matched real-world treatment (mindfulness/relaxation-based therapy). Our primary outcomes are reductions in anxiety and depression symptoms and functional impairment at 3 and 6 months postrandomization. Recognizing the clinical importance of mechanistic research to optimize interventions , we willAll participants provide written informed consent electronically after reviewing consent documents with research staff. To protect participant privacy and confidentiality, phone-based appointments are completed by research staff in a secure office at 1 of the 2 collaborating sites. Additionally, participants are assigned an ID number that is used to identify their data throughout the study. Only trained research staff have access to the key that can match participant data to the participant’s name. The key is password-protected on a secure server housed at the collaborating institutions. Participants are compensated up to US $410 for participating in the study and receive compensation via a GreenPhire  MastercaAdults who report clinically significant anxiety or depression  are being recruited and enrolled to participate in a trial on the effects of a novel, smartphone-delivered intervention to address negative mood symptoms and reduce COVID-19–related mental health disparities. Participants are recruited via community organizations and social media and internet outlets . Interested individuals complete an online prescreener assessment, where they are asked to answer basic demographic questions, questions about their phone use, and levels of anxiety and depression they are experiencing via the Overall Anxiety Severity and Impairment Scale (OASIS)  and the This study has 2 specific aims:To use a randomized controlled trial (RCT) design to compare the effects of EASE to a mindfulness/relaxation-based comparison group on anxiety and depression symptoms, and functional impairment at 3-month and 6-month follow-up. We hypothesize that those assigned to EASE will show greater reductions from baseline to follow-ups in OASIS  and ODSITo examine the mechanisms of action at 3- and 6-month follow-ups (H3) and moderators of treatment outcomes at 3- and 6-month follow-ups (H4). In testing the putative mechanisms of action, we hypothesize that the intervention effects on study outcomes will be mediated by reductions in AS and changes in COVID-19–related stress and fear. To determine possible moderators, we will test whether treatment effects vary as a function of perceived discrimination (worse intervention outcomes), social support (better intervention outcomes), or SES (lower SES associated with worse outcomes).This study has 2 exploratory aims:To identify opportunities to improve the efficacy, reach, and adoption of EASE through qualitative interviewsTo use daily EMAs to obtain a granular understanding of the course  and sequelae  of the COVID-19 pandemic and health behaviors affected by COVID-19  among those who do and do not contract the virusParticipants will be 800 adults  who report current clinically significant anxiety or depression. All participants must meet the following eligibility criteria: (1) have clinically significant anxiety or depression symptoms, as evinced by a score of 8 or higher on OASIS  or a scoEnrollment to the RCT began in December 2021 and, as of this writing, is in the recruitment phase. Enrollment, randomization, intervention delivery, and assessments (EMAs and follow-ups) are completed remotely via a smartphone or phone call with trained research staff. Both study conditions receive the same set of questionnaires at each assessment  to assess age, racial/ethnic identity, clinical anxiety and depression, state of residence, and willingness to complete study assessments. Those deemed eligible at the online screener complete an enrollment call with research staff that lasts approximately 30 minutes. During the enrollment call, participants provide informed consent and are informed of the purpose, goals, and procedures of the study. Participants are also further assessed for eligibility . Those found eligible are instructed on how to download the smartphone-based app that will administer assessment and intervention content   during tResearch staff are automatically notified via an encrypted email once a participant completes the baseline assessment. Once notified, research staff call the participant and complete the randomization process with the participant. During the randomization call, participants are randomly assigned to an intervention condition: (1) EASE or (2) mindfulness-based comparison; see Following randomization, research staff provide participants with a code that enables them to access condition-specific intervention materials. Next, trained research staff provide a brief app orientation to the participant on how to use the app and its components, and answer any questions the participant may have. Research staff also provide a brief orientation on how to report distress and COVID-19 symptoms via the app (both groups). The day after the randomization call, all participants begin to receive 2 prompts to complete daily EMAs, one 30 minutes after waking and one 75 minutes before bedtime. Daily EMAs take approximately 2 minutes to complete. Research staff monitor participant EMA completion rates weekly. When a participant’s completion rate significantly decreases, a staff member contacts the participant via text, phone, or email to remind them of the importance of completing the daily EMAs. Participants are informed that they can contact study staff at any point during the trial by pressing the “Call Staff TX” button at the top of the app home screen if they experience technical difficulties see . ParticiParticipants complete follow-up smartphone-based, self-report assessments at 3 and 6 months postrandomization. Additionally, research staff contact participants selected through a modified quota sampling strategy  at 3 andRegarding compensation, all participants who enroll in the study receive a GreenPhire  MastercaEvent-sampling EMAs are self-initiated by the participant. Specifically, all participants are instructed to self-initiate an event-sampling EMA to record distress and new COVID-19 symptoms. Participants are instructed to answer questions based on immediate thoughts/feelings when they initiate an event-sampling EMA. EASE participants receive unique intervention content when they report elevated depression or anxiety symptoms. Further, our previously developed COVID-19 risk assessment and detection algorithm  automatiEASE integrates standard CBT with AS reduction. EASE provides (1) standard treatment on a schedule, (2) scheduled and cued interoceptive exposure sessions, (3) on-demand intervention content , (4) tailored treatment messages based on responses to EMA items, and (5) additional resources. All interactions with the app are date-, time-, and location-stamped for future analyses.EASE includes 18 four- to six-minute intervention videos , fear avoidance hierarchy for specific stressors , interoceptive exposure techniques, the relationship between COVID-19 and anxiety/depression, and use of AS reduction strategies to mitigate the impact of COVID-19–related stress on mental health. Videos also present known disparities regarding COVID-19 and the potential impact on mental health disparities. Finally, several videos focus on relaxation training to help mitigate the impact of stress related to discrimination, general life events, and COVID-19–specific stressors on heightened symptoms of anxiety and depression. See To target AS, graduated exposure to anxiety and distress-provoking situations and response prevention is introduced, reviewed, and practiced via EASE videos and the “Stress Management Trainings” button. Participants learn to manage negative affect, stress, and uncomfortable physiological symptoms during exposure exercises without acting on acute motivation to suppress distress. These exposure exercises were created and pilot-tested as part of our previous work on smartphone-delivered AS reduction . The exeParticipants have access to a “Coping Toolkit” menu of on-demand intervention content see . SpecifiDuring the 6-month intervention period, a tailored message is delivered at the end of each scheduled EMA (2 per day). Specifically, when participants provide a pattern of responses that are suggestive of heightened emotional distress , they receive a coping message that attempts to address current symptoms .Participants randomized to the mindfulness/relaxation-based comparison condition have access to some app components that are similar to EASE. The comparison intervention emphasizes the importance of mindfulness in the larger context of mental and physical health and specific skills to improve mindfulness and promote relaxation/stress reduction -78. The Prescreen and enrollment call data are collected via REDCap. Baseline, EMA, and follow-up data are collected using smartphones through the Insight mHealth platform app software . QualitiParticipants are asked to provide standard demographic information   is a 5-iThe Fear of COVID-19  scale isThe Everyday Discrimination Scale (EDS)  is a 6-iThe 6-Item Perceived Social Support Questionnaire (F-SozU K-6)  is a selThe EMA methodology used for this study is similar to that used in our previous studies and by other researchers ,75,85-95Hypotheses for aims 1 and 2 will be examined using latent growth models (LGMs) , multileH1: Participants randomized to the EASE intervention will have greater reductions in anxiety, depression, and functional impairment at 3- and 6-month follow-ups compared to those randomized to the mindfulness/relaxation-based comparison intervention. Multiple approaches to modeling changes in primary outcomes will be used to evaluate our hypothesis that the EASE app will result in greater reductions in the primary outcomes relative to the comparison app and that reductions will be similar across racial/ethnic groups. We will first estimate between-group differences by calculating Cohen d effect sizes (with a 95% CI) for anxiety, depression, and functional impairment at each major follow-up assessment. We will then conduct a series of conditional LGMs to examine the impact of the treatment condition on the trajectories of primary outcomes from baseline to the end of the follow-up period. First, an LGM will be specified using the assessments of primary outcomes at major assessments: baseline, 3 months, and 6 months. A dummy code representing the treatment condition will be included in the model as a predictor of the intercept and slope factors to quantify the effect of the EASE program on the longitudinal course of outcomes relative to the comparison group. The statistical significance (and magnitude of the effect size) of the main effect of treatment condition will test H1 . Separate models will be specified for anxiety, depression, and functional impairment.H2: Intervention effects of EASE relative to mindfulness/relaxation-based comparison will be equivalent across racial/ethnic groups. We will next conduct similar effect size and LGM analyses within each of the 4 racial/ethnic groups in order to test H2 . We will then use MSEMs to examine the effects of the EASE app on the longitudinal course of outcomes, as measured by the EMA daily diary data. MSEM analyses will also be conducted both across and within the racial/ethnic groups to evaluate how effects vary across groups.H3: Intervention effects on study outcomes listed in aim 1 will be mediated by reductions in AS and changes in COVID-19–related stress and fear. Similar modeling procedures will be used to determine the impact of the treatment condition on AS and COVID-19–related stress and fear and whether improvements in primary outcomes are mediated by reductions in AS and the secondary mechanisms. We will first conduct separate LGMs for AS and the secondary mechanisms using methods such as those described for the primary outcomes. After conducting univariate LGMs to explore changes in AS and secondary mechanisms as a function of treatment condition, we will conduct a series of parallel process LGMs to examine how changes in AS and secondary mechanisms relate to changes in the primary outcomes. The effects of changes in AS and secondary mechanisms on changes in the primary outcomes will be examined by specifying the slope factor for each potential mechanism as a predictor of the slope factor for each outcome. Separate parallel process LGMs will be conducted for each primary outcome, and a series of parallel process LGMs will be evaluated to determine the unique effect of each mechanism when modeled simultaneously. The indirect effects of treatment on primary outcomes via AS and other potential mechanisms will be evaluated by calculated bootstrapped CIs of the indirect effect using the MODEL indirect command in MPlus. These parallel process LGMs will not provide formal tests of mechanisms of change, as these models will be examining concurrent changes across the 6 months of assessment, but if the results from these models are promising, they will be followed by analyses that include the EMA measures of outcomes and mechanisms and will use models that permit more fine-grained examinations of temporal dependencies of change .H4: Perceived discrimination (worse intervention outcomes), social support (better intervention outcomes), or the SES (lower SES associated with worse outcomes) will moderate intervention effects on study outcomes listed in aim 1. The moderating influence of discrimination, social support, and SES will be evaluated using conditional LGMs in which condition, moderator, and condition×moderator interaction terms will be specified as predictors of the slope factor for primary outcomes using the XWITH command in MPlus. Separate models will be specified for each hypothesized moderator, and conclusions will be based on the CIs of the interaction terms.The brief qualitative interviews will be transcribed following completion and read to ensure accurate transcription. The transcribed interviews will be coded using NVivo v.12 . The study team (2-3 project staff) will create the initial codebook and then code 2 interviews together. The codebook will then be revised (if needed), and a third interview will be coded as a team. If there is high coding agreement between team members, the remainder of the interviews will be coded independently by the staff and then checked by author MC. The team will meet to discuss any disagreements in coding and to create a final coded version for thematic analysis. Themes will be determined within and across codes. Comparisons will also be made across racial/ethnic groups and between the intervention and comparison groups. An additional team member will then review the coding and analysis for confirming and disconfirming evidence for the themes . ParticiWe anticipate some participant attrition during the trial. Missing data will be handled in all analyses using direct ML techniques within MPlus under a missing-at-random (MAR) assumption . Modern We calculated the sample size required to detect the treatment effect of EASE on the primary outcomes of anxiety, depression, and functional impairment at 3 and 6 months both across and within the racial/ethnic groups, the treatment effect of EASE on the longitudinal course of AS and secondary mechanisms, and the indirect effects of EASE on primary outcomes via the hypothesized mechanisms. Based on our pilot data and prior published research, we hypothesize moderate-to-large (d>.50) effects of EASE on primary outcomes and the hypothesized mechanisms and moderate-to-large associations (r>.30) between changes in mechanisms and primary outcomes. Power determinations based on these hypothesized effect sizes were conducted using GPower, RMASS , and simThe study was funded by the National Institute of Mental Health (NIMH) in May 2021. The smartphone app was finalized in December 2021, and data collection began on December 22, 2021. Study staff have since been engaged in activities associated with study enrollment and data collection, including ordering and monitoring of gift cards and supplies; screening, consenting, and enrolling participants; dispersing intervention incentives; follow-up tracking/calls; maintaining an up-to-date Institutional Review Board (IRB) protocol and associated documents; and monitoring incoming data to identify and resolve problems early . As of June 17, 2022, 217 participants have been recruited into the study.Mental health disparities across racial/ethnic groups are projected to become more pronounced as a result of the COVID-19 pandemic . IntegraNotably, all study procedures are administered remotely. As such, this study may serve as a clinically important example for how to implement remote/virtual mental health research with BHAI individuals. Indeed, such flexible platforms for treatment delivery may have greater potential to reach and impact people of color, who constitute an underserved, hard-to-reach population. By providing a remote means of care, our study may be able to assess and assist a larger group of individuals who identify as BHAI and remove potential barriers to treatment access, such as money needed to commute into the office/clinic or the need to take time off from work to attend appointments.One notable limitation of our study is the reliance on Android smartphones to complete assessments and access app features. Study-provided Android smartphones will be loaned to eligible participants upon request; however, those who are unwilling to use a study-provided smartphone or their own personal Android smartphone will be unable to participate in the study. Additionally, recruitment of older populations may be hindered due to the lack of appropriate knowledge and familiarity with mHealth apps . This stUltimately, the goal of this study is to target and reduce emerging and likely exacerbated mental health disparities among BHAI individuals. We aim to fill a void surrounding mental health treatment among all groups but particularly BHAI individuals by developing a digitally delivered, mobile, easy-to-use treatment for anxiety and depression. By addressing comorbid anxiety and depression symptoms through an intervention that targets an underlying transdiagnostic vulnerability factor , we hope that this intervention will help adults with anxiety or depression to reduce their symptoms.


Background: Healthcare-associated SARS-CoV-2 infections need to be explored further. Our study is an analysis of hospital-acquired infections (HAIs) and ambulatory healthcare workers (aHCWs) with SARS-CoV-2 across the pandemic in a Belgian university hospital. Methods: We compared HAIs with community-associated infections (CAIs) to identify the factors associated with having an HAI. We then performed a genomic cluster analysis of HAIs and aHCWs. We used this alongside the European Centre for Disease Control (ECDC) case source classifications of an HAI. Results: Between March 2020 and March 2022, 269 patients had an HAI. A lower BMI, a worse frailty index, lower C-reactive protein (CRP), and a higher thrombocyte count as well as death and length of stay were significantly associated with having an HAI. Using those variables to predict HAIs versus CAIs, we obtained a positive predictive value (PPV) of 83.6% and a negative predictive value (NPV) of 82.2%; the area under the ROC was 0.89. Genomic cluster analyses and representations on epicurves and minimal spanning trees delivered further insights into HAI dynamics across different pandemic waves. The genomic data were also compared with the clinical ECDC definitions for HAIs; we found that 90.0% of the ‘definite’, 87.8% of the ‘probable’, and 70.3% of the ‘indeterminate’ HAIs belonged to one of the twenty-two COVID-19 genomic clusters we identified. Conclusions: We propose a novel prediction model for HAIs. In addition, we show that the management of nosocomial outbreaks will benefit from genome sequencing analyses. During the first wave of the pandemic, approximately 10 to 15% of hospitalized COVID-19 cases were hospital-acquired infections (HAIs) ; front-lHowever, SARS-CoV-2 kept evolving. A few variants of concern (VOCs) have been related to more severe infections, high viral loads , vaccineMany articles have described community-associated COVID-19 (CAI) patients from the beginning of the pandemic onwards . Fewer hOur study was conducted in Belgium from 6 March 2020 until 2 March 2022, spanning 2 years of the SARS-CoV-2 pandemic at ‘Universitair Ziekenhuis Brussel’, a 721-bed Belgian tertiary care center. Infection control measures against SARS-CoV-2 in our hospital followed the national guidelines and changed over time. So did the testing strategy in our center: PCR tests became available in early March 2020; from 16 May 2020, all patients were screened with a PCR upon hospitalization; from 29 April 2021, an extra PCR screening of all patients was performed at day 4 and day 7 after admission and every week thereafter (until the end of the study period). Five different epidemic waves were identified by our national public health institute. The vaccine rollout started in our hospital in January 2021; the uptake was extremely high, reaching >95% of the hospital staff.A case of a CAI was an adult with a community-acquired infection admitted for >24 h. A healthcare-associated COVID-19 infection is a general term, and included hospitalized patients with a nosocomial COVID-19 infection and healthcare workers diagnosed with COVID-19 whilst in service . A case of an aHCW was an ambulatory healthcare worker from our hospital with a diagnosis of COVID-19. A case of an HAI was an adult with a hospital-acquired COVID-19 infection during a hospitalization for another indication admitted >24 h following the COVID-19 diagnosis. To identify an HAI in our study, we used the European Centre for Disease Control (ECDC) case source definitions of an HAI, as described in All the subjects included in the study  had a COThe objective of this study was to better characterize HAIs in our university hospital. For this purpose, we combined a multiple logistic regression on retrospective data with genomic analyses. As such, we could identify the predictive factors associated with having an HAI versus a CAI. To give a better idea of the distribution of the HAI and CAI cases over time, we computed all of our cases on epidemiological curves alongside the genomic trees. We also showed the added value of the genomic cluster analysis to the ECDC source definitions.Nasopharyngeal samples from HAIs and aHCWs were systematically stored at −80 °C. All samples with sufficiently high  viral loads and enough remaining volume were included in the whole genome sequencing (WGS) analysis of the current study. We adapted a SARS-CoV-2 WGS protocol . AmplicoThe WGS data were processed with the SARS-CoV-2 plug-in of BioNumerics v.8.1 . The sub-sequences of the Wuhan-Hu-1 (NC 04551219) reference genome were used as the reference sequences for a BLAST search . After tA similarity matrix was then calculated based on the remaining SNP experiments and minimal spanning trees (MSTs) were constructed. Genomic clusters were defined as genomes with a difference of ≤2 SNPs and were marked with a grey contour. Therefore, every visually distinct gray contour represented a separate genomic cluster. Twenty-two clusters involving at least one HAI patient were identified. We looked at the HAI within each ECDC category and checked if they were part of a cluster or not.t-test or a Mann–Whitney test . The categorical data were expressed in absolute numbers and percentages; the univariate statistical analysis was performed by a Fisher’s exact test. A p-value < 0.05 was considered to be statistically significant. We used descriptive statistics to report on the demographics as well as the clinical and laboratory data of the patients and then compared the continuous and categorical variables between the HAI and CAI patients. To obtain numerically comparable groups for the comparative analyses, a random selection of the CAI patients was made. For the continuous data, the median values and interquartile ranges were shown and a univariate statistical analysis was performed by an unpaired We used the parameters shown to be statistically significant in the univariate analysis to perform the multiple logistic regression analyses by means of GraphPad Prism 8.4.3 software . Positive and negative predictive values were calculated as well as the area under the ROC curves.We showed all of the cases over time on epidemiological curves, hereafter called epicurves, using STATA scripts and Excel software and reported them by the week of diagnosis . All of The hospital wards were anonymized; ward names were replaced by numbers, themselves corresponding with the floor on which the ward could be found.The results of the univariate analyses of HAIs versus CAIs are described in n = 874) over time on the epicurve, color-coded according to the viral strain. n = 262), color-coded by the type of SARS-CoV-2 strain. The genomic clusters are highlighted by the contoured circles in grey. We classified our hospitalized HAI patients into subgroups according to the case source definition used by the ECDC . Figure Our study is one of few addressing the complex issue of healthcare-associated COVID-19 infections comprehensively and across the whole pandemic (up to March 2022). We describe a novel approach in the differentiation between HAIs and CAIs. We obtained a positive predictive value (PPV) of 83.6% and a negative predictive value (NPV) of 82.2%, when computing death and LOS into the model. From the genomic analyses we learned that infections amongst HAIs and aHCWs were successively due to the pre-VOC virus, and then the Alpha, Delta, and Omicron VOCs; this was consistent with the SARS-CoV-2 variant waves observed in the CAIs in Belgium. This finding may seem logical, but it is still noteworthy: having sicker patients (HAIs) or well-vaccinated aHCWs did not change the type of virus with which they were infected. We found that larger proportions of patients who had been classified as having an HAI in the higher levels of certainty (using the ECDC rules) were part of one of the twenty-two COVID-19 genomic clusters . Whether a patient belonged to a cluster or not added an extra dimension to the ECDC definitions and also confirmed the validity of the ECDC criteria. Our proportion of HAIs seemed comparable with that of other centers ,2. It waUntil wave 3, many clusters were identified on the epicurve  as well Generalizing the results of a single-center study is never easy, but the advantage of a monocentric study in this context is the more uniform management of infection prevention strategies , thus limiting the introduction of other biases. Another important limitation was the proportion of samples that we were able to sequence. Only samples with a high enough viral load could be sequenced. This reflected the technical reality worldwide. We fully acknowledge that this may have introduced an element of selection bias, possibly toward other (and maybe more) pathogenic viruses, but we still think that our results gave a very good idea of the genomic dynamics amongst HAIs and aHCWs in a large academic hospital and in a real-life setting. The issue of HAIs will sadly remain topical until we manage to improve their management by combining several approaches. Our study contributes to the body of knowledge toward that goal.


COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as ‘long-COVID’). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID.We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups: (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID.The EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID. We will use national data on ~99% of the Scottish population using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 platform.Our study will be unable to identify long-COVID patients who have not been in contact with healthcare services in Scotland.Identifying long-COVID using routinely collected electronic health records may be challenging due to the lack of a standardised definition and variation in coding practices across healthcare systems.To improve the identification of long-COVID , we intend to use free text in addition to the coded data available in electronic health records.We are actively involving individuals who have experienced long-COVID to shape the research and ensure relevance to patients and the public.In December 2019, an outbreak of a novel coronavirus was reported in Wuhan, China. The WHO declared the outbreak a global pandemic named COVID-19 caused by the SARS-CoV-2 coronavirus. By November 2021, the WHO had reported over 240 million confirmed cases and at least five million deaths worldwide,with more than nine million confirmed cases and over 140 000 deaths reported in the UK.2The severity and duration of the acute SARS-CoV-2 infection varies widely. Most people are asymptomatic or experience mild-to-moderate symptoms, while a smaller proportion (10%–15%) of cases experience more severe illness.3 7 8 15–18Unified guidance to manage the long-term effects of COVID-19 in the UK has been developed by the National Institute for Health and Care Excellence (NICE), Scottish Intercollegiate Guidelines Network (SIGN) and the Royal College of General Practitioners (RCGP).16In Scotland, patients with symptoms suggestive of long-COVID are advised to seek medical care from their general practitioner (GP).10.1136/bmjopen-2021-059385.supp1Supplementary dataDespite the progress in diagnostic coding, the prevalence and risk factors associated with long-COVID remain poorly understood, reflecting the lack of an agreed operational definition, the absence of diagnostic tests and the considerable variation in presentation. Reviews on the long-COVID literature have found that it was difficult to estimate the prevalence of persistent COVID-19 symptoms with certainty.We will undertake a national prospective population-based cohort study using the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform.Since the baseline population for this study is everyone registered with a GP, those who are not registered with a GP in Scotland will be excluded from the analyses.We are using the whole population of Scotland and therefore sample size calculations are not applicable.The EAVE II platform links a wide range of routine healthcare datasets using pseudonymised identifiers of National Health Service (NHS) Scotland’s Community Healthcare Index. We will use these routinely collected data sources (described below) to identify individuals with long-COVID and to determine their characteristics in the EAVE II cohort.Primary care data will be extracted from GP practices via EAVE II’s trusted third party Albasoft Ltd.26Activity in hospital-based care will be extracted from the Scottish Morbidity Record (SMR) 01 which holds detailed information on hospital admissions, such as the specific area of clinical activity , the facility of care, patient management and new diagnoses.28All COVID-19 testing will be obtained from the Electronic Communication of Surveillance in Scotland dataset. This surveillance data contains all reverse transcriptase PCR (RT-PCR) tests, carried out in Scotland.COVID-19 vaccination data, including vaccination type and number of doses administered, will be available from two sources: GP records and the Turas Vaccination Management Tool, a web-based tool used to record community vaccinations in Scotland.29Telehealth in Scotland is operated by NHS 24 Scotland, which delivers telephone and online services.30Prescription data relating to all medications prescribed and dispensed in the community in Scotland will be extracted from the prescribing information system.26Mortality data will be taken from death registry data within the National Records of Scotland. These records hold information included on the death certificate, including cause(s) of death which are recoded using ICD-10 codes.26We will explore the use of other linkages available within the EAVE II platform. These include Scotland’s Census 2011 from NHS Research Scotland for information on ethnicity, disability and occupation as part of the EAVE II substudy for ethnic and social inequalities in COVID-19 outcomes in Scotland.33We will base our operational definition on the case definitions for the effects of COVID-19 illness at different time periods developed by NICEAcute COVID-19 infection: individuals with signs and symptoms of COVID-19 for up to 4 weeks.Ongoing symptomatic COVID-19: individuals with signs and symptoms of COVID-19 from 4 to 12 weeks.Post-COVID-19 syndrome: individuals with signs and symptoms that develop during or following an infection consistent with COVID-19, continue for more than 12 weeks and are not explained by an alternative diagnosis. The post-COVID-19 syndrome usually presents with clusters of symptoms, often overlapping, which can fluctuate and change over time and can affect any organ system.Long-COVID commonly refers to those who continue to present with signs and symptoms four or more weeks after acute COVID-19 infection, therefore, our primary outcome will include both ongoing symptomatic COVID-19 and post-COVID-19 syndrome. Our secondary outcome will focus on the clinical encounters suggestive of the post-COVID-19 syndrome. Further details are in the statistical analyses.Population characteristics will be explored to assess the risk factors for developing long-COVID and to account for any confounding in our analyses.Age will be determined based on the available GP data and will be available as a continuous and categorical variable. Those aged over 100 will be truncated into the one group to overcome low sample size issues. Sex at birth will be included as a binary variable . Deprivation status will be derived from the Scottish Index of Multiple Deprivation 2020 quintile of the resident’s postcode associated with their GP registration. Ethnicity data will also be included if completeness and quality of data is adequate. We will also consider other available information such as body mass index (BMI) and smoking status .Area of residence in terms of NHS Scotland Health Boards and local authorities will be considered. Settlement type will be determined by the urban/rural sixfold classification (UR6). Type of residence will also be considered such as private residence, care home and social/council housing if data are available.Using diagnostic codes from the QCOVID algorithm,34Admission to hospital, any requirement for treatment in the ICU, and death will be used to categorise the severity of COVID-19 infection. We will define a COVID-19 hospitalisation as an RT-PCR confirmed positive test for SARS-CoV-2 in the 28 days prior to admission, or admission with an ICD-10 code for COVID-19. A COVID-19 ICU admission will be defined as a RT-PCR confirmed positive test for SARS-CoV-2 in the 28 days prior to ICU admission. A COVID-19 death will be defined as dying within 28 days of confirmed or probable COVID-19.The amount of missing data will be examined for each variable of interest. Continuous variables, for example, BMI, will be imputed using predictive mean matching or imputation by chained equations if appropriate. Categorical variables with missing data will have a distinct group of ‘Unknown’. We will consider dealing with these missing categorical variables by either keeping the distinct group, imputing them using chained equations or removing them from the analysis. The latter will be a complete-case analysis, which will reduce the total sample size.We will first derive an operational definition for long-COVID by identifying patterns in clinical interactions within NHS Scotland services that may suggest long-COVID. A visual illustration of our intended methods is shown in We are interested in (1) GP interactions; (2) hospital admissions; (3) outpatient attendances; (4) Accident and Emergency visits; (5) OOH encounters; (6) NHS 24 telehealth interactions; (7) medications (from GP prescribing and primary care pharmacy dispensing data) and (8) all-cause mortality. Our primary focus will be on the GP data, with other healthcare data providing corroborative information step 1, . InformaFor the healthcare services (sources a to f), we will investigate the frequency of interactions and the reasons for each interaction which will include any new diagnoses (categorised by body system), treatments, tests or procedures related to long-COVID. For medications (7), we will investigate the frequency and type of new prescriptions using BNF chapters. For all-cause mortality (8), we will record the causes of death using ICD-10 codes. GP records provide a rich source of primary care data, with the coded data providing additional context to interactions such as the type of interaction , referrals to specialty care and sick notes. For more detailed information on signs and symptoms that are indicative of long-COVID, we intend to use written free text available from GP records. We will use natural language processing (NLP) to identify key words or phrases of signs and symptoms relating to long-COVID. This NLP model will be applied to all written free text using Computer-Assisted (diagnostic) Coding. This will create derived codes associated with the key words and phrases .To initially explore potential long-COVID indicators, we will obtain summary level counts of all codes of interest within these healthcare datasets on individuals who tested positive and negative for COVID-19 using an RT-PCR test. We will count the frequency of these data ≥4 weeks after the date of the test. This will inform which codes relating to potential long-COVID indicators will be extracted on a patient level for further analysis.To identify which of these long-COVID indicators are most important, we will perform a matched analysis step 2, . The expWe will use risk-set matching in a 3:1 ratio by time-varying propensity score matching. This will be based on the likelihood of testing positive for COVID-19 and will consider incorporating the following characteristics: sex, age, geography, comorbidities, risk factors, number of previous SARS-CoV-2 tests, deprivation status and urban–rural settlement. The adequacy of the matching will be assessed by checking for imbalance of the individual covariates across exposure groups.For the matched analysis, each potential long-COVID indicator will be treated as its own dependent variable in turn. Follow-up will begin from 4 weeks after the exposed tested positive for COVID-19. Follow-up will end on either the date of event (if indicator is a binary variable), the control testing positive for COVID-19, death from any cause or the end of the follow-up period. Controls who have a positive test will be eligible to be included in the exposed group.The long-COVID indicators will be compared between the exposed and control groups using statistical tests such as two-sample proportions test (for binary indicators), two-sample t-tests (for continuous indicators), Kaplan-Meier curves to inspect cumulative incidence, and survival analysis to look at the potential impact of interventions on long-COVID symptoms.We also plan to conduct similar analyses on the whole cohort without propensity score matching. We will consider stratifying by age and sex if numbers allow.Clusters of long-COVID presentations in the exposed group will be investigated further, using the long-COVID indicators as our clustering input step 3, . The indWe will explore both hierarchical clustering and k-means clustering, using distance measurements such as the Gower Distance, which is a suitable measurement of similarity for mixed categorical and numeric data.We will perform a variety of sensitivity analyses to test the robustness of our long-COVID definition. This includes evaluating the start of follow-up to 12 weeks, to explore whether the alternative outcome definition of ‘post-COVID-19 syndrome’ display different clinical pathways. We will also investigate the patterns in the long-COVID indicators associated with the diagnostic long-COVID codes. To capture those who may be suffering from long-COVID but did not formally test positive for COVID-19 , we will investigate the long-COVID indicators in the general population. We will also stratify by time period, for example, during the different peaks of positive cases in Scotland .We will use the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines to report the derivation and validation of the long-COVID prediction model  cluster(s) (1) or otherwise (0).Predictors for the risk prediction model will consist of the patient characteristics, including information on sociodemographics, geographical, clinical comorbidities and severity of COVID-19 infection. These will be a mixture of continuous, binary and categorical variables. Continuous variables will be tested for linearity and for more flexible relationships (using smooth splines). Groupings of continuous variables will be explored if necessary.We intend to use a multivariable logistic regression model.We will build our model using stepwise selection based on the Akaike’s information criterion and Bayesian information criterion. To assess the fit of the model parameters, the maximum likelihood ratio test will also be used.To evaluate the model’s goodness of fit, we will use appropriate performance evaluation metrics such as the area under the Receiver Operating Characteristic (ROC) curve (captures the accuracy of the model discriminating between the outcome), and the calibration plot and slope . Other evaluation measures such as the specificity (true negative rate), sensitivity (true positive rate) and accuracy will be considered if appropriate. We will also directly compare the predicted and observed values.The model will be internally validated using k-fold cross validation. We will validate using different time periods as specified by one of the discussed sensitivity analyses in the clustering analysis. We will explore opportunities for external validation, comparison and meta-analysis with other long-COVID initiatives.To categorise the output of the model for further use by clinicians and COVID-19 patients, we will consider stratifying patients into risk groups based on the predictive probabilities in the multivariable model, for example, three groups of low, moderate and high risk of long-COVID.Sensitivity analyses for the risk prediction algorithm will depend on the outcomes from the sensitivity analyses from Objective 1 of developing an operational definition for long-COVID.Advances in machine learning will be utilised to enhance the development and validation of the prediction model. Specifically, we will systematically explore the use of supervised learning algorithms such as penalised models  regression), naïve Bayes classifier, gradient boosting decision trees and random forests to further improve the prediction model developed with traditional statistical methods. We will also consider using ensemble learning methods to strategically combine multiple models to obtain better predictive performance.Lay input has shaped the development of this research and will continue throughout the project through the patient and public involvement (PPI) co-applicant, the EAVE II Public Advisory Group (PAG) and long-COVID Scotland. PPI members will collaborate with the research team to provide real-world perspectives when analysing and interpreting study findings ensuring that the work considers the needs, interests and concerns of patient and public members.This study forms part of the EAVE II project which is investigating epidemiological risk factors of COVID-19 disease. All data will be anonymised before being made available to the research team. EAVE II has already obtained permissions from the Research Ethics Committee (REC reference: 12/SS/0201), NHS Research and Development, GPs, NHS Health Boards and the Public Benefit and Privacy Panel (PBPP) for Health and Social Care (reference number: 1920-0279).To ensure the greatest impact of our findings, we will actively disseminate to three key audiences: policy/public health, academic and community based.Findings from this project will provide evidence to help NHS Scotland and other international policy-makers identify groups of the population who are at most risk of long-COVID and related complications. We will work with partners in NHS Scotland, PHS and the Scottish Government to establish the best ways of disseminating these results and influencing policy. We also plan to disseminate findings through the National Core Studies programme, a UK government initiative supported by Health Data Research UK, in partnership with the Office for National Statistics.https://github.com/EAVE-II).We will communicate our findings through presentations at major national and international scientific meetings and through publications in relevant peer-reviewed journals. We will publish our code and data dictionary on the EAVE II’s GitHub repository (https://www.ed.ac.uk/usher/EAVE-ii).We will write lay summaries of publications and create infographics to further communicate our findings via press releases, public and patient engagement events, social media and the EAVE II website (