Competing Health Risks Associated with the COVID-19 Pandemic and Early Response: A Scoping Review BackgroundCOVID-19 has rapidly emerged as a global public health threat with infections recorded in nearly every country. Responses to COVID-19 have varied in intensity and breadth, but generally have included domestic and international travel limitations, closure of non-essential businesses, and repurposing of health services. While these interventions have focused on testing, treatment, and mitigation of COVID-19, there have been reports of interruptions to diagnostic, prevention, and treatment services for other public health threats.

ObjectivesWe conducted a scoping review to characterize the early impact of COVID-19 on HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition.

MethodsA scoping literature review was completed using searches of PubMed and preprint servers (medRxiv/bioRxiv) from January 1st to October 31st, 2020, using Medical Subject Headings (MeSH) terms related to SARS-CoV-2 or COVID-19 and HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition. Empiric studies reporting original data collection or mathematical models were included, and available data synthesized by region. Studies were excluded if they were not written in English.

ResultsA total of 1604 published papers and 205 preprints met inclusion criteria, including 8.2% (132/1604) of published studies and 10.2% (21/205) of preprints: 7.3% (68/931) on HIV, 7.1% (24/339) on tuberculosis, 11.6% (26/224) on malaria, 7.8% (13/166) on sexual and reproductive health, and 9.8% (13/132) on malnutrition. Thematic results were similar across competing health risks, with substantial indirect effects of the COVID-19 pandemic and response on diagnostic, prevention, and treatment services for HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition.

DiscussionCOVID-19 emerged in the context of existing public health threats that result in millions of deaths every year. Thus, effectively responding to COVID-19 while minimizing the negative impacts of COVID-19 necessitates innovation and integration of existing programs that are often siloed across health systems. Inequities have been a consistent driver of existing health threats; COVID-19 has worsened disparities, reinforcing the need for programs that address structural risks. The data reviewed here suggest that effective strengthening of health systems should include investment and planning focused on ensuring the continuity of care for both rapidly emergent and existing public health threats.
Prevalence of long-term effects in individuals diagnosed with COVID-19: an updated living systematic review ObjectivePost COVID-19 condition refers to persisting or recurring symptoms weeks after acute COVID-19 illness which can significantly impact quality of life and health systems. It is important to understand the manifestation and magnitude of this condition. The objective of this living systematic review is to summarize the prevalence of symptoms and sequelae reported by people [&ge;]4 weeks after COVID-19 diagnosis.

DesignSystematic review, meta-analysis and narrative synthesis.

Data sourcesEmbase, Medline, PsychInfo, Cochrane Central and select grey literature up to April 14, 2021.

MethodsWe adapted a previous search strategy used by the U.K. National Institute for Health and Care Excellence and updated it to search for new literature. Two reviewers screened references independently; one extracted data and assessed risk of bias and certainty of the evidence while another verified them. Prevalence data from laboratory-confirmed populations were meta-analyzed using a random effects model and synthesized separately in the short-term (4-12 weeks) and long-term (>12 weeks) periods after diagnosis. Data from clinically-diagnosed populations were synthesized narratively.

ResultsOf the 4444 unique citations, 84 observational studies met our inclusion criteria. Over 100 post COVID-19 symptoms and sequelae were reported. Sixty-one percent (95% CI: 44-76%, low certainty) and 53% (95% CI: 41-65%, low certainty) of laboratory-confirmed individuals reported persistence or presence of one or more symptoms in the short- and long-term periods, respectively. The most prevalent symptoms in both periods included: fatigue, general pain or discomfort, shortness of breath, cognitive impairment and mental health symptoms.

ConclusionsA substantial proportion of individuals reported a variety of symptoms [&ge;]4 weeks after COVID-19 diagnosis. Due to gaps in the research base, and the low certainty of the evidence currently available, further research is needed to determine the true burden of post COVID-19 condition in the general population and in specific subgroups.

PROSPERO registration numberCRD42021231476.
CD19+IgD+CD27- naive B Cells as predictors of humoral response to COVID 19 mRNA vaccination in immunocompromised patients Immunocompromised patients are considered high-risk and prioritized for vaccination against COVID-19. We aimed to analyze B-cell subsets in these patients to identify potential predictors of humoral vaccination response. Patients (n=120) suffering from hematologic malignancies or other causes of immunodeficiency and healthy controls (n=79) received a full vaccination series with an mRNA vaccine. B-cell subsets were analyzed prior to vaccination. Two independent anti-SARS-CoV-2 immunoassays targeting the receptor-binding domain (RBD) or trimeric S protein (TSP) were performed three to four weeks after the second vaccination. Seroconversion occurred in 100% of healthy controls, in contrast to 67% (RBD) and 82% (TSP) of immunocompromised patients, while only 32% (RBD) and 22% (TSP) achieved antibody levels comparable to those of healthy controls. The number of circulating CD19+IgD+CD27- naive B cells was strongly associated with antibody levels ({rho}=0.761, P<0.001) and the only independent predictor for achieving antibody levels comparable to healthy controls (OR 1.07 per 10-{micro}l increase, 95%CI 1.02-1.12, P=0.009). Receiver operating characteristic analysis identified a cut-off at [&ge;]61 naive B cells per {micro}l to discriminate between patients with and without an optimal antibody response. Consequently, measuring naive B cells in immunocompromised hematologic patients could be useful in predicting their humoral vaccination response. Statistical modeling for quality risk assessment of clinical trials: follow-up at the era of remote auditing BackgroundAs investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote Quality Assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology.

MethodsWe modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 - 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints.

ResultsWe could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk.

ConclusionContinuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times. COVID Outcome Prediction in the Emergency Department (COPE): Development and validation of a model for predicting death and need for intensive care in COVID-19 patients Background and aimCOVID-19 is putting extraordinary pressure on emergency departments (EDs). To support decision making in the ED, we aimed to develop a simple and valid model for predicting mortality and need for intensive care unit (ICU) admission in suspected COVID-19 patients.

MethodsFor model development, we retrospectively collected data of patients who were admitted to 4 large Dutch hospitals with suspected COVID-19 between March and August 2020 (first wave of the pandemic). Based on prior literature we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. Logistic regression analyses with post-hoc uniform shrinkage was used to obtain predicted probabilities of in-hospital death and of the need for ICU admission, both within 28 days after hospital admission. We assessed model performance (Area Under the ROC curve (AUC); calibration plots) with temporal validation in patients who presented between September and December 2020 (second wave). We used multiple imputation to account for missing values.

ResultsThe development data included 5,831 patients, of whom 629 (10.8%) died and 5,070 (86.9%) were discharged within 28 days after admission. ICU admission was fully recorded for 2,633 first wave patients in 2 hospitals, with 214 (8%) ICU admissions within 28 days. A simple model - COVID Outcome Prediction in the Emergency Department (COPE) - with age, respiratory rate, C-reactive protein, lactic dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well-calibrated and showed good discrimination in 3,252 second wave patients (AUC in 4 hospitals: 0.82 [0.78; 0.86]; 0.82 [0.74; 0.90]; 0.79 [0.70; 0.88]; 0.83 [0.79; 0.86]). COPE was also able to identify patients at high risk of needing IC in 706 second wave patients with complete information on ICU admission (AUC: 0.84 [0.78; 0.90]; 0.81 [0.66; 0.95]). The models are implemented in web-based and mobile applications.

ConclusionCOPE is a simple tool that is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

CONTRIBUTION TO THE LITERATUREO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIPrediction models have the potential to support decision making about hospital admission of patients presenting to the emergency department with suspected COVID-19
C_LIO_LIMost currently available models that were independently assessed contain a high risk of bias
C_LIO_LIPromising models were developed in different patient selections and included predictors that are not quickly and objective obtainable in emergency departments
C_LI

What this study addsO_LIA simple and objective tool ("COPE") is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19
C_LIO_LICOPE may support ED physicians to identify high-risk patients - i.e. those at high risk of deterioration and/or death - requiring treatment in the ICU, intermediate-risk patients requiring admission to the clinical ward, and low-risk patients who can potentially be sent home
C_LI Estimating Vaccination Effects and Variant Strains on COVID-19 outbreak course in Japan, as of August, 2021 BackgroundAs of the end of 2021 year, the rate of completion for second-dose administration was almost 80% in Japan.

ObjectWe evaluated waning of vaccine effectiveness for COVID-19 in Japan, controlling for mutated strains, the Olympic Games, and countermeasures.

MethodThe effective reproduction number R(t) was regressed on current vaccine coverage and data of a certain number of days prior, as well as shares of mutated strains, and an Olympic Games dummy variable along with data of temperature, humidity, mobility, and countermeasures. The study period was February, 2020 through December 7, as of December 28, 2021.

ResultsEstimation results indicate that the past vaccine coverage of older than 60 days prior raises R(t) significantly and cancelled vaccine effectiveness completely.

Discussion and ConclusionResults indicate significant waning of vaccine effectiveness from 60 days after the second dose. Evaluating a first fully automated interview grounded in Multiple Mini Interview (MMI) methodology: results from a feasibility study ObjectivesGlobal, Covid-driven restrictions around face-to-face interviews for healthcare student selection have forced admissions staff to rapidly adopt adapted online systems before supporting evidence is available. We have developed, what we believe is, the first fully automated interview grounded in Multiple Mini-Interview (MMI) methodology. This study aimed to explore test re-test reliability, acceptability and usability of the system.

Design, setting and participantsmixed-methods feasibility study in Physician Associate (PA) programmes from two UK and one US university during 2019 - 2020.

Primary, secondary outcomesFeasibility measures (test retest reliability acceptability and usability) were assessed using intra-class correlation (ICC), descriptive statistics, thematic and content analysis.

MethodsVolunteers took (T1), then repeated (T2), the automated MMI, with a seven-day interval (+/- 2) then completed an evaluation questionnaire. Admissions staff participated in focus group discussions.

ResultsSixty-two students and seven admission staff participated; 34 students and four staff from UK and 28 students and three staff from US universities.

Good-excellent test-retest reliability was observed with T1 and T2 ICC between 0.62-0.81 (p<0.001) when assessed by individual total scores (range 80.6-119), station total scores 0.6-0.91, p<0.005, individual site (all ICC[&ge;] 0.76 p<0.001) and mean test retest across sites 0.82 p<0.001 (95% CI 0.7-0.9).

Admissions staff reported potential to reduce resource costs and bias through a more objective screening tool for pre-selection or to replace some MMI stations in a  hybrid model. Maintaining human interaction through  touch points was considered essential.

Users positively evaluated the system, stating it was intuitive with an accessible interface. Concepts chosen for dynamic probing needed to be appropriately tailored.

ConclusionThese preliminary findings suggest that the system is reliable, generating consistent scores for candidates and is acceptable to end-users provided human touchpoints are maintained. Thus, there is evidence for the potential of such an automated system to augment healthcare student selection processes. The Association of Opening K-12 Schools with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis AO_SCPLOWBSTRACTC_SCPLOWThis paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States. Using data on foot traffic and K-12 school opening plans, we analyze how an increase in visits to schools and opening schools with different teaching methods (in-person, hybrid, and remote) is related to the 2-weeks forward growth rate of confirmed COVID-19 cases. Our debiased panel data regression analysis with a set of county dummies, interactions of state and week dummies, and other controls shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the positive association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These results have a causal interpretation in a structural model with unobserved county and time confounders. Sensitivity analysis shows that the baseline results are robust to timing assumptions and alternative specifications. Effectiveness of a short term induction regimen of a biosimilar Adalimumab in the long term management of symptomatic Ankylosing Spondylitis Symptomatic Ankylosing Spondylitis: A Community Based Proof of Concept Observational Study IntroductionCost and drug toxicity often deter prolonged therapeutic use of anti-TNF agents in ankylosing spondylitis (AS). A planned study was completed to endorse our clinic based observation of long term relief following short-term administration of anti-TNF agent.

Methods50 consenting patients with symptomatic active chronic AS under rheumatology care in a community clinic were enrolled. They had no past history of anti-TNF use. 40 mg standard biosimilar adalimumab (BS-ADA, Exemptia trade name)was injected subcutaneously every fortnight for six injections (10 weeks). Patients were monitored at several time points. Improvement was assessed at regular interval as per protocol which included standard indices (Bath and Assessment Spondyloarthritis International Society /ASAS). An intention to treat analysis with significant p<0.05 was carried out.

ResultsPatients showed early and substantial significant improvement in pain, NSAID requirement, function and in several indices (ASAS 20 & 40, ASAS partial remission, BASDAI, BASFI, ASDAS) which persisted after stopping injections. 84% and 52 % of patients respectively showed ASAS 20 improvement at week 12 and 48: corresponding ASAS partial remission 34% and 24%. Over 50% patients maintained prolonged improvement and provided proof of concept (defined a-priori). Serum Interleukin-6 assay showed a sharp reduction at 24 weeks. None developed TB or serious drug toxicity. 11 patients withdrew (mostly inadequate response). Absence of control was a limitation.

ConclusionA ten-week administration of biosimilar adalimumab in difficult to treat AS showed early substantial improvement which often persisted for 24 weeks. This unconventional strategy was socioeconomically appealing. It merits further validation and acceptance especially in resource strained settings. The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients The factors involved in the persistence of antibodies to SARS-CoV-2 are unknown. We evaluated the antibody response to SARS-CoV-2 in personnel from 10 healthcare facilities and its association with individuals characteristics and COVID-19 symptoms in an observational study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. In individuals positive for IgG ([&ge;] 12 AU/mL) at the beginning of the study, we found an increase [p= 0.0002] in antibody response in symptomatic subjects, particularly with anosmia/dysgeusia (OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb. The effects of modifiable maternal pregnancy exposures on offspring molar-incisor hypomineralisation: A negative control study ObjectivesO_LITo explore the associations between modifiable maternal pregnancy exposures: pre-pregnancy body mass index (BMI), pregnancy smoking and alcohol consumption with offspring molar-incisor hypomineralisation (MIH).
C_LIO_LITo explore for the presence of residual confounding using negative control analyses.
C_LI

MethodsThis study used data from Avon Longitudinal Study of Parents and Children (ALSPAC), a UK prospective birth cohort. We defined offspring MIH using prospectively collected questionnaire data. We used logistic regression to explore confounder adjusted associations between maternal pre-pregnancy BMI and smoking and alcohol consumption during pregnancy with MIH. We included negative control exposure (paternal BMI, smoking and alcohol around the time of pregnancy) and outcome (offspring dental trauma) analyses to explore for the presence of residual confounding.

Results5536 mother/offspring pairs were included (297 MIH cases [5.4%]). We found a positive association between maternal mean BMI and offspring MIH (OR per 1-kg/m2 difference in maternal BMI: 1.04, 95% CI: 1.00, 1.08). In subsequent analyses, we found evidence that this effect was non-linear and being driven by women in the highest BMI quintiles. In negative control analyses, we found no evidence of association between paternal BMI and offspring MIH (OR: 0.94, 95% CI: 0.89, 1.00) and maternal BMI and offspring dental trauma (OR: 0.99, 95% CI: 0.96, 1.02). There was no clear evidence of an association for maternal pregnancy smoking (OR: 0.76, 95%CI: 0.46, 1.22) and alcohol consumption (OR: 0.79, 95% CI: 0.56, 1.21) with offspring MIH with results imprecisely estimated.

ConclusionIn summary we found evidence of a possible intrauterine effect of high maternal pre-pregnancy BMI on offspring MIH. We did not find robust evidence for an intrauterine effect of maternal pregnancy smoking or alcohol consumption on offspring MIH. Our findings provide further support for women of reproductive age to maintain a healthy weight. Future studies are warranted to explore possible mechanisms on how the pregnancy environment may relate to offspring MIH. Development of machine learning models for the prediction of complications after colorectal and small intestine surgery in psychiatric and non-psychiatric patient collectives (P-Study) Psychiatric and psychosomatic diseases are an increasingly cumbersome burden for the medical system. Indeed, hospital costs associated with mental health conditions have been constantly on the rise in recent years. Moreover, psychiatric conditions are likely to have a negative effect on the treatment of other medical conditions and surgical outcomes, in addition to their direct effects on the overall quality of life. Our study aims to investigate the impact of preoperative Risk factor, psychiatric and psychosomatic diseases on the outcomes of colorectal surgery and length of hospital stay via predictive modeling techniques.

MethodPatient data will be collected from several participating national and international surgical centers The machine learning models will be calculated and coded, but also published according to the TRIPOD guidelines (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis).

ResultIt is conceivable to arrive at generalizable models predicting the abovementioned endpoints through large amounts of data from several centers. The models will be subsequently deployed as a free-to-use web-based prediction tool using the Shiny environment [47]. The cost for the hosting server and digital infrastructure will be covered by Dr. Anas Taha. The sponsor will store the data for ten years. The ANTsX ecosystem for quantitative biological and medical imaging The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis. Emergence and Evolution of Big Data Science in HIV Research: Bibliometric Analysis of Federally Sponsored Studies 2000-2019 BackgroundThe rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data Science. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data Science in HIV/AIDS-related publications that were funded by the US federal agencies.

MethodsWe identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies from 2000 to 2019 by integrating data from National Institutes of Health (NIH) ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing (NLP) methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications.

ResultsWe harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) some institutes (e.g., University of California system, MD Anderson Cancer Center, and Harvard Medical School) are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years.

ConclusionsWe identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and suggested a number of fast-evolving areas of Big Data Science in HIV/AIDS research including secondary analysis of EHR, machine learning, Deep Learning, predictive analysis, and NLP. Overview of The Current Progress of Facultative Anaerobic Bacteria in Cancer Biotherapy with TNF-alpha as Main Mechanism of Action This review article focuses on the use of infectious bacteria as delivery tool for tumour necrosis factor  (TNF-), a well-studied cytokine, in the context of immunotherapy for cancer treatment. The tumour targeting properties of certain bacteria strains has been known for decades as well as the tumour catabolizing effect of TNF-. The combination of these two have been studied in murine models for various types of cancer with promising results. Research in this fascinating field is unfortunately uncommon, thus the number of high-quality articles is limited. Search was done via Google Scholar in combination with PubMed, to increase the coverage and find peer-reviewed, original, and primary research articles.

Key findings show that attenuated or genetically modified species of bacteria have fewer side effects and can be effective in delivering cytokines to tumour sites. TNF- is produced by macrophages/monocytes during acute inflammation or infection, thus can be triggered by infectious bacteria which in turn induce apoptosis. The cytotoxic effect of the bacteria can be enhanced with localized irradiation. Promising results have been shown in bladder, breast, colon, glial, lung, ovarian, pancreatic, prostate, and renal cancer cells.

The need for better, safer, and more effective cancer treatment is apparent as traditional chemotherapy and radiation can cause a lot of harm for the patient, and not necessarily prolong the lifespan. The success-rate for these treatments vary greatly depending on the type of cancer, but for tumours that cannot be surgically removed the outcome is generally quite poor. A drawback of chemotherapy is that tumours can grow resistant to the treatment while healthy cells continue to be exposed, increasing the risk of severe side effects. Different types of biological therapies are a modern and possibly safer approach, even though immunotherapy comes with substantial risks. Using the innate immune system to fight tumour cells is not always safe, because uncontrolled and excessive release of pro-inflammatory signalling molecules can result in multisystem organ failure and death. This phenomenon is called cytokine release syndrome (cytokine storm) and is one of the major risks of immunotherapy. However, tailored biological therapies have proven their effectiveness for a wide range of cancer types, and the next step in this evolution is to genetically engineer both delivery systems and mechanisms of action. This approach can be combined with the traditional radiation and chemotherapy for increased effectiveness, even if biological therapies as a stand-alone treatment, can be a goal for the future. Using controlled interrupted time series to estimate the population-level effects of Ontario's recently implemented overdose prevention sites and consumption and treatment services BackgroundOntario recently implemented overdose prevention sites and consumption and treatment services (OPS/CTS) to stem the harms of the opioid epidemic. We tested whether operating any site improved local opioid-related health service use and mortality rates.

MethodsWe used monthly counts of all opioid-related emergency department (ED) visits, hospitalizations, and deaths between January 2014 and December 2020 for our outcomes. For each public health unit (PHU) that implemented any OPS/CTS, we created a synthetic control as a weighted combination of unexposed PHU. We estimated the population-level effects of operating any site using controlled interrupted time series with segmented regression and adjusted for time varying confounders (i.e. OPS/CTS capacity, naloxone kits distributed, and persons receiving opioid agonist treatment per 100,000 population). We repeated the analysis using a multiple baseline approach to estimate province-wide effects.

ResultsBetween 2017 and 2020, nine out of 34 PHU implemented at least one OPS/CTS. ED visit (RR=0.96, 95%CI: 0.92 - 0.99) and hospitalization (RR=0.95, 95%CI: 0.92 - 0.98) trends declined faster among treated units. Improvements in local ED visit rate trends were observed for the majority of treated units. Hospitalization rates declined faster for London (RR=0.97, 95%CI: 0.95 - 0.99) and Niagara (RR=0.95, 95%CI: 0.92 - 0.98); while mortality rates declined faster for Hamilton (RR=0.93, 95%CI: 0.90 - 0.96), Niagara (RR=0.97, 95%CI: 0.94 - 0.99) and Guelph (RR=0.94, 95%CI: 0.88 - 1.00).

ConclusionAlthough OPS/CTS are not sufficient to stem the harms of the opioid overdose epidemic on their own, they play a critical role in local harm reduction strategies. COVID-19 Acceleration and Vaccine Status in France - Summer 2021 ObjectivesThis note provides an assessment of COVID-19 acceleration among groups with different vaccine status in France.

MethodsWe assess viral acceleration using a novel indicator introduced in Baunez et al. (2021). The acceleration index relates the percentage change of tests that have been performed on a given day to the percentage change in the associated positive cases that same day. We compare viral acceleration among vaccinated and unvaccinated individuals in France over the period May 31st - August 29, 2021.

ResultsOnce the state of the epidemic within each groups is accounted for, it turns out that viral acceleration has since mid-July converged to similar levels among vaccinated and unvaccinated individuals in France, even though viral speed is larger for the latter group compared to the former.

ConclusionOur results call for an increasing testing effort for both vaccinated and unvaccinated individuals, in view of the fact that viral circulation is currently accelerating at similar levels for both groups in France. Treatment interruption is a risk factor for sickness presenteeism: a large-scale cross-sectional study during the COVID-19 pandemic ObjectivesThis study examined the relationship between interruption to routine medical care during the coronavirus disease 2019 pandemic and sickness presenteeism among workers in Japan.

MethodsA cross-sectional study using data obtained from an internet monitor questionnaire was conducted. Interruption to medical care was defined based on the response "I have not been able to go to the hospital or receive treatment as scheduled." The fraction of sickness presenteeism days in the past 30 days was employed as the primary outcome. A fractional logit model was used for analysis to treat bounded data.

ResultsOf the 27,036 participants, 17,526 (65%) were workers who did not require routine medical care, 8,451 (31%) were using medical care as scheduled, and 1,059 (4%) experienced interrupted medical care. The adjusted odds ratio (aOR) of sickness presenteeism was significantly higher among workers who experienced interrupted medical care (3.44; 95% confidence interval [CI]: 3.04-3.89) than those who did not require routine medical care. In terms of symptoms, the highest aOR was observed among workers with mental health symptoms (aOR: 5.59, 95%CI: 5.04-6.20).

ConclusionsThis study suggests the importance of continuing necessary treatment during a pandemic to prevent presenteeism. Simple risk scores to predict hospitalization or death in outpatients with COVID-19 including the Omicron variant ImportanceOutpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, in particular whether to hospitalize a patient and if managed as an outpatient, how closely to follow them.

ObjectiveTo develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging.

DesignDerivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules.

SettingPrimary and Express care clinics in a Pennsylvania health system.

ParticipantsPatients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19.

Main outcomes and measuresClassification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC).

ResultsOverall, 7.4% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21) and 5.5% in the late validation cohort (3806 patients presenting 3/1/21 or later) were ultimately hospitalized. We developed and temporally validated three risk scores that all included age, dyspnea, and the presence of comorbidities, adding respiratory rate for the second score and oxygen saturation for the third. All had very good overall accuracy (AUC 0.77 to 0.78) and classified over half of patients in the validation cohort as very low risk with a 1.7% or lower likelihood of hospitalization. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization (2.8%).

Conclusions and relevanceSimple risk scores applicable to outpatient and telehealth settings can identify patients with very low (1.6% to 1.7%), low (5.2% to 5.9%), moderate (14.7% to 15.6%), and high risk (32.0% to 34.2%) of hospitalization. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/. Decreased Diastolic Hydraulic Forces Incrementally Associate With Survival Beyond Conventional Measures of Diastolic Dysfunction AIMSDecreased hydraulic forces during diastole contribute to reduced left ventricular (LV) filling and heart failure with preserved ejection fraction. However, their association with diastolic function and patient outcomes are unknown. The aim of this study was to determine the association between diastolic hydraulic forces, estimated by echocardiography as the atrioventricular area difference (AVAD), and both diastolic function and survival.

METHODS AND RESULTSPatients (n=11,734, median [interquartile range] 3.9 [2.4-5.0] years follow-up, 1,213 events) were selected from the National Echo Database Australia based on the presence of relevant transthoracic echocardiographic measures, LV ejection fraction (LVEF) [&ge;]50%, heart rate 50-100 beats/minute, the absence of moderate or severe valvular disease, and no prior cardiac surgery. AVAD was calculated as the cross-sectional area difference between the LV and left atrium. LV diastolic dysfunction was graded according to 2016 guidelines. AVAD was weakly associated with E/e, left atrial volume index, and LVEF (multivariable global R2=0.15, p<0.001), and not associated with e and peak tricuspid regurgitation velocity. Decreased AVAD was independently associated with poorer survival, and demonstrated improved model discrimination after adjustment for diastolic function grading (C-statistic [95% confidence interval] 0.645 [0.629-0.662] vs 0.607 [0.592-0.623], p<0.001) and E/e (0.639 [0.623-0.655] vs 0.621 [0.604-0.637], p<0.001), respectively.

CONCLUSIONDecreased hydraulic forces, estimated by AVAD, are weakly associated with diastolic dysfunction and demonstrate an incremental prognostic association with survival beyond conventional measures used to grade diastolic dysfunction. Transdiagnostic phenotyping of psychopathology in a help-seeking population (PhenoNetz): a study protocol for an experience sampling study Prevention in psychiatry provides a promising way to address the burden by mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Conceptualizing psychopathology manifested in help-seeking populations from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of Experience Sampling Methods (ESM). A combination of ESM with network analyses provides valid insights beyond established assessment instruments. We will examine n = 75 individuals (age 18-40 years) of the help-seeking population of the Cologne early recognition centre (FETZ). For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days utilizing a mobile application to assess ten transdiagnostic symptoms, i.e., depressive, anxious and psychotic symptoms as well as distress level. With these data, we will generate average group-level symptom networks and personalized symptom networks using a two-step multilevel vector autoregressive model. Additionally, we will explore associations between symptom networks and sociodemographic, risk and resilience factors, as well as psychosocial functioning. Our study will provide insights about feasibility and utility of ESM in a help-seeking population. Providing a first explorative phenotyping of the transdiagnostic help-seeking population, this study will contribute to innovation of early recognition in psychiatry. Results will help to pave the way for prevention and targeted early intervention in a broader patient group and thus, enable greater intended effects in alleviating the burden of psychiatric disorders. Short Practical Regimen of Acupuncture for Melasma: A Prospective Cohort Study in a Tertiary Hospital in Thailand BackgroundMelasma is a common skin pigmentation disorder. Acupuncture shows benefits for patients with melasma, although no standardized method or optimal number of sessions have been determined.

ObjectiveTo comparatively assess clinical outcomes of melasma between five vs ten standard acupuncture sessions.

MethodsWe performed a prospective observational cohort study in all melasma patients at the Acupuncture Clinic, King Chulalongkorn Memorial Hospital, Bangkok, Thailand. All patients were treated with standard acupuncture procedure twice a week and were evaluated after the fifth and the tenth sessions of acupuncture, with a follow-up one week after the last session. Participants were divided into two treatment groups: Groups A and B received five and ten acupuncture sessions respectively. Melasma outcomes were assessed by using the Melanin Index (MI), Melasma Area and Severity Index (MASI), patient-reported improvement scores and acupuncture-related adverse events.

ResultsOf 113 participants, 67 received five sessions of acupuncture treatment while 39 received ten sessions. 7 participants did not finish treatment. After five sessions of acupuncture in Group A, the mean MI decreased by 28.7 (95%CI -38.5 to -18.8), whereas the median MASI decreased by 3.4 (-6.9, -1.2) points. After ten sessions of acupuncture in Group B, the mean MI decreased by 31.3 (95%CI -45.0 to -17.6), whereas the median MASI decreased by 5.4 (-9.9, - 3.0) points. The first five sessions of acupuncture had a higher effect on the change in magnitude than the last five sessions, although there was no statistically significant difference. However, ten-session of acupuncture, had not reached the peak benefit on melasma yet. Further studies should be done with a higher number of sessions to determine the optimal rounds of treatment. Twenty-nine participants reported minor side effects, mostly consisting of persistent pain and small hematoma. Group B had RR of having adverse events 1.8 times (95% CI 1.0-3.4) compared to group A.

ConclusionAcupuncture has a potential dose-response effect on clinical and patient-reported melasma outcomes with minor side effects. Post-viral parenchymal lung disease following COVID-19 and viral pneumonitis hospitalisation: A systematic review and meta-analysis BackgroundApproximately half of COVID-19 survivors present persisting breathlessness, which may include development of pulmonary fibrosis.

Research QuestionWhat is the prevalence of long-term radiological and functional pulmonary sequelae of parenchymal lung disease following hospitalisation with COVID-19 and other viral pneumonia?

Study design and methodsWe performed systematic review and random effects meta-analysis of studies in adults hospitalised with SARS-CoV-2, SARS-CoV, MERS-CoV, or Influenza pneumonia and followed within 12 months from discharge. Searches were run on MEDLINE and Embase, updated 29 July 2021. Primary outcomes were proportion of 1) radiologic sequelae at CT scans; 2) restrictive impairment; 3) impaired gas transfer. Heterogeneity was explored in meta-regression.

ResultsNinety-five studies were included for qualitative synthesis, of which 70 were suitable for meta-analysis, including 60 studies of SARS-CoV-2 with a median follow up of 3 months. In SARS-CoV-2 the overall estimated proportion of inflammatory changes during follow up was 0.50 (95%CI 0.41 to 0.58, I2=94.6%), whilst fibrotic changes were estimated at 0.29 (95%CI 0.22 to 0.37, I2=94.1%). Inflammatory changes reduced compared with CTs performed during hospitalisation (-0.47; 95%CI -0.56 to -0.37), whereas no significant resolution was observed in fibrotic changes (-0.09; 95%CI -0.25 to 0.07). Impaired gas transfer was estimated at 0.38 (95%CI 0.32 to 0.44, I2=92.1%), which was greater than estimated restrictive impairment (0.17; 95%CI 0.13 to 0.23, I2=92.5%). High heterogeneity means that estimates should be interpreted with caution. Confidence in the estimates was deemed low due to the heterogeneity and because studies were largely observational without controls.

InterpretationA substantial proportion of radiological and functional sequelae consistent with parenchymal lung disease are observed following COVID-19 and other viral pneumonitis. Estimates of prevalence are limited by differences in case mix and initial severity. This highlights the importance of extended radiological and functional follow-up post hospitalisation.

PROSPERO registrationCRD42020183139 (April 2020) Genetic control of fetal placental genomics contributes to development of health and disease As the master regulator in utero, the placenta is core to the Developmental Origins of Health and Disease (DOHaD) hypothesis but is historically understudied. To identify placental gene-trait associations (GTAs) across the life course, we performed distal mediator-enriched transcriptome-wide association studies (TWAS) for 40 traits, integrating placental multi-omics from the Extremely Low Gestational Age Newborn Study. At P < 2.5 x 10-6, we detected 248 GTAs, mostly for neonatal and metabolic traits, across 176 genes, enriched for cell growth and immunological pathways. In aggregate, genetic effects mediated by placental expression significantly explained 4 early-life traits but no later-in-life traits. 89 GTAs showed significant mediation through distal genetic variants, identifying hypotheses for distal regulation of GTAs. Investigation of one hypothesis in human placenta-derived choriocarcinoma cells showed that knockdown of mediator gene EPS15 upregulated predicted targets SPATA13 and FAM214A, both associated with waist-hip ratio in TWAS, and multiple genes involved in metabolic pathways. These results suggest profound health impacts of placental genomic regulation in developmental programming across the life course. Post-COVID-19 Memory Complaints: Prevalence and Associated Factors IntroductionMemory complaints resulting from COVID-19 may have a significant impact on the survivors quality of life. Unfortunately, there is insufficient information available on memory loss and its relationship to COVID-19. Therefore, the purpose of this research was to determine the prevalence of memory complaints in post-COVID-19 patients and to find potential contributing factors.

MethodA cross-sectional survey was conducted on 401 individuals who had previously been diagnosed with COVID-19 at four COVID testing centers situated across Bangladesh. The MAC-Q questionnaire was used to evaluate memory. A binary logistic regression model was fit to study the variables related to memory complaints, with a p-value of <0.05 deemed statistically significant.

ResultMemory complaints was prevalent in 19.2% of the post-COVID patients. Individual predictor analysis revealed that among the treatment modalities, steroids and antibiotics were associated with impaired memory. Multiple logistic regression showed that individuals who recovered from COVID-19 within six to twelve months were more likely to have memory deficits. Even though age, sex, oxygen demand, and hospitalization were not linked with memory complaints, rural residents exhibited more significant memory complaints than urban residents.

ConclusionNearly one-fifth of the COVID-19 patients suffer from various degrees of memory complaints within one year. However, no association was found between COVID-19 severity to memory complaints.