Relative burdens of the COVID-19, malaria, tuberculosis and HIV/AIDS epidemics in sub-Saharan Africa COVID-19 has had considerable global impact, but in sub-Saharan Africa is one of several infectious disease priorities. Prioritization is normally guided by disease burden, but the highly age-dependent nature of COVID-19 and other infectious diseases makes comparisons challenging unless considered through metrics that incorporate life years lost and time lived in adverse health. We therefore compared 2020 mortality and Disability-Adjusted Life Years lost (DALYs lost) estimates for malaria, tuberculosis, and HIV/AIDS in sub-Saharan African populations with 12+ months of COVID-19 burden (up to end March 2021), applying known age-related mortality to United Nations estimates of age structure. We further compared exacerbations of disease burden predicted from the COVID-19 public health response. Data was derived from public sources, predicted exacerbations derived from those published by international agencies. For sub-Saharan African populations north of South Africa, recorded COVID-19 DALYs lost in 2020 was 3.7%, 2.3%, and 2.4% of those estimated for tuberculosis, HIV/AIDS and malaria respectively. Predicted exacerbations of these comparator diseases were greater than the estimated COVID-19 burden. Including South Africa and Lesotho, COVID-19 DALYs lost were <12% of those for comparator diseases and dominated by them in all age groups below 65 years. The analysis suggests a relatively low impact from COVID-19. While all four epidemics continue, tuberculosis, HIV/AIDS, and malaria remain far greater health priorities based on disease burden. Resource diversion to COVID-19 therefore runs a high risk of increasing the overall disease burden and causing net harm, further increasing global inequities in health and life expectancy.
MAGNITUDE AND DURABILITY OF THE EFFECT OF VACCINATION ON THE CASE FATALITY RATE FOR COVID-19 ObjectiveTo evaluate the benefits of vaccination on the case fatality rate (CFR) for COVID-19 infections.

DesignMultivariate modeling of data from electronic medical records

Setting130 medical centers of the United States Department of Veterans Affairs

Participants339,772 patients with COVID-19 confirmed by nucleic acid amplification testing as of September 30, 2021

MethodsThe primary outcome was death within 60 days of the diagnosis. Patients were considered vaccinated if they had completed a full series >= 14 days prior to diagnosis. Cases presenting in July - September of 2021 were considered to have the delta variant. Logistic regression was used to derive adjusted odds ratios (OR) for vaccination and infection with delta versus earlier variants. Models were adjusted for demographic traits, standard comorbidity indices, selected clinical terms, and 3 novel parameters representing all prior diagnoses, all prior vital signs/ baseline laboratory tests, and current outpatient treatment. Patients with a delta infection were divided into 8 cohorts based upon the time from vaccination to diagnosis (in 4-week blocks). A common model was used to estimate the odds of death associated with vaccination for each cohort relative that of all unvaccinated patients.

Results9.1% of subjects had been fully vaccinated, and 21.5% were presumed to have the delta variant. 18,120 patients (5.33%) died within 60 days of their diagnoses. The adjusted OR for delta infection was 1.87 +/- 0.05 which corresponds to a relative risk of 1.78. The overall adjusted OR for prior vaccination was 0.280 +/- 0.011 corresponding to a relative risk of 0.291. The study of vaccine cohorts with a delta infection showed that the raw CFR rose steadily after 10-14 weeks. However, the OR for vaccination remained stable for 10-34 weeks.

ConclusionsOur study confirms that delta is substantially more lethal than earlier variants and that vaccination is an effective means of preventing COVID death. After adjusting for major selection biases, we found no evidence that the benefits of vaccination on CFR declined over 34 weeks.
Molecular analysis suggests oligoclonality and metastasis of endometriosis lesions across anatomically defined subtypes. Endometriosis symptoms are heterogeneous with controversy on whether it constitutes a single disease or multiple distinct types. Our previous work found recurrent somatic cancer-driver alterations in endometriosis; however, these have not been found ubiquitously. A handful of cases spread across studies also suggest mutations might be shared (clonal) between lesions of the same type. As current classification systems correlate poorly with symptoms or outcomes, somatic genomics may improve the current system. Here, we investigate heterogeneity of somatic cancer-driver mutations within patients and across endometriosis types. We examined anatomically distinct types of endometriosis (ovarian, deep infiltrating, and superficial endometriosis) in 27 individual patients all of whom had at least two types of endometriosis. Specimens were analyzed using high-sensitivity targeted sequencing with orthogonal validation from droplet digital PCR and mutation-surrogate immunohistochemistry. Results found 13/27 patients had informative somatic driver mutation in endometriosis, 9/13 had identical mutations across distinct lesions. Endometriomas tended to have a higher mutational complexity, with functionally redundant driver mutations in same gene and within the same lesions.

Our data are consistent with clonality across endometriosis lesions regardless of subtype. Further the finding of redundancy in mutations with the same gene and lesions is also consistent with endometriosis representing an oligoclonal disease with dissemination likely to consist of multiple epithelial clones travelling together. This suggests the current anatomically defined classification of endometriosis does not fully recognize the etiology of the disease. A novel classification should take into account genomic and other molecular features. These findings could further contribute to development of a more personalized endometriosis diagnosis and care. 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. Using Mendelian randomisation to explore the gateway hypothesis: Possible causal effects of smoking initiation and alcohol consumption on substance use outcomes Background and AimsInitial use of drugs such as tobacco and alcohol may lead to subsequent more problematic drug use - the  gateway hypothesis. However, observed associations may be due to a shared underlying risk factor, such as trait impulsivity. We used bidirectional Mendelian Randomisation (MR) to test the gateway hypothesis.

DesignOur main method was inverse-variance weighted (IVW) MR, with other methods included as sensitivity analyses (where consistent results across methods would raise confidence in our primary results). MR is a genetic instrumental variable approach used to support stronger causal inference in observational studies.

SettingEuropean ancestry individuals.

ParticipantsGenome-wide association summary data for smoking initiation, alcoholic drinks per week, cannabis use and dependence, cocaine and opioid dependence (N=1,749 to 1,232,091).

MeasurementsGenetic variants for exposure.

FindingsWe found evidence of causal effects from smoking initiation to increased drinks per week (IVW: {beta}=0.06; 95% CI=0.03 to 0.09; p=9.44x10-06), cannabis use (IVW: OR=1.34; 95% CI=1.24 to 1.44; p=1.95x10-14), and cannabis dependence (IVW: OR=1.68; 95% CI=1.12 to 2.51; p=0.01). We also found evidence of an effect of cannabis use on increased likelihood of smoking initiation (IVW: OR=1.39; 95% CI=1.08 to 1.80; p=0.01). We did not find evidence of an effect of drinks per week on other substance use outcomes, except weak evidence of an effect on cannabis use. We found weak evidence of an effect of opioid dependence on increased drinks per week (IVW: {beta}=0.002; 95% CI=0.0005 to 0.003; p=8.61x10-03).

ConclusionsSmoking initiation may lead to increased alcohol consumption, cannabis use and dependence. Cannabis use may also lead to smoking initiation, and opioid dependence to alcohol consumption. However, given tobacco and alcohol use typically begin before other drug use, these results may reflect a shared risk factor, or a bidirectional effect for cannabis use. Further research should explore potentially shared risk factors. Relationship Between Patient Sex and Serum Tumor Necrosis Factor Antagonist Drug and Anti-Drug Antibody Concentrations in Inflammatory Bowel Disease; A Nationwide Cohort Study BackgroundAnti-drug antibodies to infliximab (ATI) and adalimumab (ATA) are associated with loss of response to tumor necrosis factor antagonist (anti-TNF) therapy in inflammatory bowel disease (IBD). We evaluated the relationship between patient sex and serum TNF antagonist drug and antibody concentrations in inflammatory bowel disease

MethodsA nationwide multicenter retrospective cohort study was conducted by evaluating patients charts from July 2018 until September 2021. The effect of patient sex on anti-drug antibodies and serum drug concentration in patients with IBD across 7 hospitals was investigated. A subgroup analysis also investigated the effect of anti-TNF combination therapy. Geometric means were calculated, and multiple linear regression was used to estimate the adjusted ratio of geometric means (RoGM) and their 95% confidence intervals (CI).

ResultsIn the total study sample (n = 1093), males receiving infliximab had higher anti-drug antibody concentrations (38.3 vs. 22.3 AU/ml; aRoGM = 1.72, 95% CI: 1.30-2.27, p-value <0.001) compared to females. Additionally, infliximab serum drug concentrations among males were lower compared to females (2.6 vs. 4.1 ug/ml; aRoGM = 0.62, 95% CI: 0.44-0.88, p-value = 0.007). In the subgroup analysis (n = 359), male compared to female patients on combination therapy with infliximab and immunomodulators had similar anti-drug antibody concentrations (30.2 vs. 21.9 AU/ml; aRoGM = 1.38, 95% CI: 0.79-2.40, p-value = 0.254). There was no difference in the anti-drug antibody and serum drug concentrations among males and females on adalimumab.

ConclusionIn patients receiving infliximab, anti-drug antibodies were higher in males than females. Consistent with this, serum drug concentrations were lower in males than females on infliximab. There was no difference in anti-drug antibody and serum drug concentrations among males and females on adalimumab. In addition, no difference in anti-drug antibodies between males and females receiving anti-TNF combination therapy was observed. SEPRES: Sepsis prediction via a clinical data integration system and real-world studies in the intensive care unit BackgroundSepsis is vital in critical care medicine, and early detection and intervention are key to survival. We aimed to establish an early warning system for sepsis based on a data integration system that can be implemented in the intensive care unit (ICU).

MethodsWe trained the LightGBM and multilayer perceptron on the open-source database Medical Information Mart for Intensive Care for sepsis prediction. An ensemble sepsis prediction model was established based on the transfer learning and ensemble learning technique on the private dataset of Ruijin Hospital. The Shapley Additive Explanations analysis was applied to present feature importance on the prediction inference. With the development of data-integrating hub to collect and transmit data from different brands of ICU medical devices, the data integration system was established to receive, integrate, standardize, and store the real-time clinical data. In this way, the sepsis prediction model developed in the ICU of the Ruijin Hospital for the real-world study of sepsis early warning on ICU management. The trial was registered with ClinicalTrials.gov (NCT05088850).

FindingsOur best early warning model achieved an area under the receiver operating characteristic curve (AUC) of 0{middle dot}9833 in the task of detecting sepsis in 4-h preceding on the open-source database, while our ensemble model achieved an AUC of 0{middle dot}9065-0{middle dot}9436 in the retrospective research from 1-5-h preceding on the private database, and 0{middle dot}8636-0{middle dot}8992 in real-time real-world studies using the data integration system in the ICU of the Ruijin Hospital. In the continuous early warning process of patients admitted to the ICU, 22 patients who met the diagnostic criteria for sepsis during hospitalization were predicted as positive cases; 29 patients without sepsis were predicted as negative cases. Additionally, 17 patients were predicted as false-positive cases; in six patients with sepsis during ICU stay, the predicted probabilities at different time nodes were all less than the warning threshold 0{middle dot}7 and predicted as false-negative cases.

InterpretationMachine learning models could allow accurate and real-time inference to detect sepsis onset within 5-h preceding at most with the help of the data integration system. We identified the features such as age, antibiotics, ventilation, and net balance to be important for the sepsis prediction inference. We argue that this system has promising potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.

FundingShanghai Municipal Science and Technology Major Project, the ZHANGJIANG LAB, and the Science and Technology Commission of Shanghai Municipality. In Vivo Cortical Microstructure: A Proxy for Tauopathy and Cognitive impairment in the Elderly with and without MCI/Dementia Aggregation of hyperphosphorylated tau protein is currently one of the most reliable indicators of Alzheimers pathology and cognitive impairment in older adults. However, it would be useful to have a non-invasive, accessible proxy measure that does not rely on Positron Emission Tomography (PET). We used data from multi-shell diffusion-weighted imaging (DWI) to assess indices from the Neurite Orientation Dispersion and Density Imaging (NODDI) model to determine possible proxies for tau and relationship with cognitive impairment. After controlling for age, sex, and the time difference between the scan acquisitions (DWI vs. PET), we used multiple factor analysis (MFA) to assess the fit between NODDI indices (orientation dispersion [ODI], neurite density [NDI], and free-water [fISO]), cortical thickness, and tau binding (via PET). We used data from 80 participants from the ADNI-3 sample who had a multi-shell DWI and an [18F]AV-1451 (tau) PET scan. Of these 80, 49 individuals were considered cognitively normal older adults (age ~74 years), 26 individuals had a diagnosis of mild cognitive impairment (age ~75 years), and five individuals had Alzheimers dementia (age ~78 years). fISO and tau shared a large amount of spatial overlap, and both strongly correlated with the first MFA dimension. Macrostructural features (such as cortical thickness and subcortical volume) introduced in a follow-up analysis were less related to this first MFA dimension than fISO and eight percent less than tau. Subsequent mediation analyses demonstrated that fISO mediated the relationship between cortical thickness and tau, explaining all of the variance. Microstructural features derived from advanced DWI acquisitions such as fISO may be useful proxies for tau. Cortical fISO, rather than cortical thickness, may represent the impact of tau on the brain (and, by extension, cognition). Hyperactive immature state and differential CXCR2 expression of neutrophils in severe COVID-19 Neutrophils are vital in defence against pathogens but excessive neutrophil activity can lead to tissue damage and promote acute respiratory distress syndrome (ARDS). COVID-19 is associated with systemic expansion of immature neutrophils but the functional consequences of this shift to immaturity are not understood. We used flow cytometry to investigate activity and phenotypic diversity of circulating neutrophils in COVID-19. First, we demonstrate hyperactivation of immature CD10- subpopulations in severe disease, with elevated degranulation of secondary granule markers. Partially activated immature neutrophils are detectable three months post symptom onset, indication long term myeloid dysregulation in convalescent COVID-19 patients. Second, we demonstrate that neutrophils from moderately ill patients downregulate the chemokine receptor CXCR2, while neutrophils from severely ill individuals failed to do so, suggesting altered ability for organ trafficking. CD10- and CXCR2hi neutrophil subpopulations were enriched in severe disease and may represent biomarkers for early identification of individuals at high risk of progressing to severe COVID-19. Assessing the Utility of Low Resolution Brain Imaging: Treatment of Infant Hydrocephalus As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post-hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use. Elevated Levels of IL-6 in IgA Nephropathy patients are induced by an Epigenetically Driven Mechanism Triggered by Viral and Bacterial RNA Recently, a several models has been proposed to describe the pathogenesis of Immunoglobulin A nephropathy (IgAN), and among them the multihit and the gut-microbiota. These models explain the pathogenesis of IgAN caused by the production of aberrant IgA, but it is believed further predisposing factors are present, including immunological, genetic, environmental, or nutritional factors that can influence the pathogenesis and that could be useful for development of precision nephrology and personalized therapy.

Newly, the role of IL-6 in pathogenesis is becoming increasingly important. It is essential for glomerular immunoglobulin A deposition and the development of renal pathology in Cd37-deficient mice, even if the reason why levels of IL-6 are elevated in IgAN patients is not well understood. One attainable hypothesis on high levels of IL-6 in IgAN comes out from our recent whole genome DNA methylation screening in IgAN patients, that identified, among others, a hypermethylated region comprising Vault RNA 2-1 (VTRNA2-1), a non-coding RNA also known as precursor of miR-886 (pre-mi-RNA). Consistently, the VTRNA2-1 expression was found down-regulated in IgAN patients.

Here we confirm that VTRNA2-1 is low expressed in IgAN subjects compared to HS and we found that also in transplanted IgAN patients (TP-IgAN), compared to non IgAN transplanted patients (TP), the VTRNA2-1 transcript was expressed at level very low. We found that in IgAN patients with downregulated VTRNA2-1, PKR is overactivated, coherently with the role of the VTRNA2-1 that binds to PKR and inhibits its phosphorylation. The loss of the VTRNA2-1 natural restrain causes, in turn, the activation of CREB by PKR. We found CREB, a classical cAMP-inducible CRE-binding factor interacting with a region of the IL-6 promoter and leading to IL-6 production, overactivated both in IgAN and in TP-IgAN patients. Effectively, in the same patients, we found elevated levels of IL-6 correlating with CREB and PKR phosphorylation.

Since PKR is normally activated by bacterial and viral RNA we hypothesized that these microrganisms can further activate the PKR/CREB/IL-6 pathway leading to an excess of IL-6 production, explaining both the high levels of IL-6, both infection involvement in the disease, both cases of IgAN associated with COVID-19 infection and with COVID-19 RNA-vaccination, and recent data showing microbiota involvment in IgAN.

Effectively, we found that Effectively, we sfound that both the RNA poly(I:C) and the COVID-19 RNA-vaccine stimulation significantly increase the IL-6 levels in IgAN patient PBMCs.

The PKR/CREB/IL-6 pathway may be very important also in the setting of renal transplantation. We found that this pathway is upregulated also in IgAN transplanted patients. Recent studies showed that the cumulative risk of IgA nephropathy recurrence increases after transplant and is associated with a 3.7-fold greater risk of graft loss. Finally, we showed that the IL-6 secretion can be reduced by the PKR inhibitor imoxin.

In conclusion, the discovery of the upregulated VTRNA2-1/PKR/CREB/IL-6 pathway in IgAN patients may provide novel approach to treat the disease and may be useful for development of precision nephrology and personalized therapy, possibly by checking the VTRNA2-1 methylation level in IgAN patients. Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs BackgroundPalliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patients frailty are usual dimensions to decide PC inclusion.

ObjectivesThe main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting PC decision making: one-year mortality, survival regression and one-year frailty classification.

MethodsThe dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forests GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve and mean absolute error were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods.

ResultsThe one-year mortality model achieved an AUC ROC of 0.87 {+/-} 0.01; the mortality regression model achieved an MAE of 329.97 {+/-} 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 {+/-} 0.01. The Spearmans correlation between the admission frailty index and the survival time was -0.1, while the point-biserial correlation between one-year frailty index and survival time was -0.16.

ConclusionsOne-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting one-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://demoiapc.upv.es. Machine Learning Approaches for Binary Classification to Discover Liver Diseases using Clinical Data For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, Multiple Imputation by Chained Equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR, all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed a better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification. The incidence of delirium in an acute geriatric community hospital: an observational cohort feasibility study. ObjectivesDelirium in hospitalised older adults is associated with negative health outcomes. Admission to an alternative care setting may lower the incidence of delirium. The Acute Geriatric Community Hospital (AGCH) was recently opened in the Netherlands and uses a multi-component non-pharmacological intervention strategy to prevent delirium. The objective of this study was to describe the incidence of delirium at the AGCH and compare this incidence to existing rates from literature. If a possible effect on delirium is seen in this comparison this would support conducting a larger prospectively controlled study on delirium in this new care setting.

DesignProspective cohort feasibility study; exploratory meta-analysis of proportions.

Setting and ParticipantsThe AGCH is an acute geriatric unit in a skilled nursing facility for patients aged >65 years with acute medical conditions.

MethodsDelirium assessment using the Confusion Assessment Method (CAM) upon admission and on day one, two and three or until delirium had resolved. Patients charts were reviewed if CAM was missing. In an linear mixed-effects model, the delirium incidence rate in AGCH was compared to pooled delirium incidence rates from six studies found in a high-quality review.

Results214 patients from the AGCH (mean age 81.9 years, 47% male, 12% with a history of dementia) were included in the analysis. Delirium developed in 8% (18/214) (95% confidence interval [CI] 5-13%) of patients during AGCH admission compared to 16% (95% CI 12-21%) in hospitals. Admission to the AGCH was associated with a decreased delirium incidence rate compared to the hospital control group (OR[odds ratio]= 0.49, 95% CI 0.24-0.98, p-value=0.044).

Conclusions and implicationsThe delirium incidence in the AGCH was low compared to those incidences found in general hospitals in literature. Based on these findings a controlled observational or randomized study measuring delirium in this care setting is recommended. Human lipoproteins comprise at least 12 different classes that are lognormally distributed. Lipoproteins in medical samples have been measured by enzymatic methods that coincide with conventional ultracentrifugation. However, the high gravity and time required for ultracentrifugation can cause sample degradation. This study presents the results of HPLC, a gentler and rapid separation method, for 55 human serum samples. The elution patterns were analysed parametrically, and the attribute of each class was confirmed biochemically. Human samples contained 12 classes of lipoproteins, each of which may consist primarily of proteins. There are three classes of VLDLs. The level of each class was distributed lognormally, and the standard amount and the 95% range were estimated. Enzymatic methods measure the levels of several mixed classes. This lognormal character suggests that the levels are controlled by the synergy of multiple factors. The CT Scan Lung Severity Score and Vaccination Status in COVID-19 patients in India: Perspective of an Independent Radiology Practice BackgroundThe COVID-19 patients often undergo a high-resolution CT scan to determine extent of their lung involvement. The aim of this study was to determine lung involvement in confirmed/suspected COVID-19 patients (encountered at an independent radiology practice) and its correlation to vaccination status amidst the second COVID-19 wave in India.

MethodsWe retrospectively queried our data since April 2021 to identify adult patients (>17 years) who had confirmed (positive RT-PCR or antigen test) or suspected COVID-19 (classic symptoms but negative RT-PCR) and received a high-resolution CT scan to determine the extent of their lung involvement using the CT severity (CT-SS) score. Patients were classified in 3 groups based on their vaccination status to determine its correlation with the CT-SS score: fully vaccinated, partially vaccinated, and unvaccinated. Basic descriptive statistics, univariate tests and multivariate linear regression analysis were used.

ResultsWe identified 229 patients (median age 45 years, 60% male) of which 205 (89%) had confirmed COVID-19 (positive RT-PCR) and 24 had suspected disease (negative RT-PCT but classic symptoms). Of the 229 patients 29 (13%) had complete vaccination, 38 (17%) had partial vaccination and 162 (70%) had no vaccination. The CT score of the completely vaccinated patients was significantly lower compared to partially or unvaccinated patients (median 0 v. 3.5 v. 10, respectively p<.01). A multivariate linear regression model showed that partial or fully vaccinated patients had lower CT severity score compared to unvaccinated patients (adjusted R squared = 0.47).

ConclusionWe present here the real-world findings from an independent radiology practice (a unique and common practice model), in India amid the second COVID-19 wave showing significantly lower CT severity score in fully or partially vaccinated patients compared to unvaccinated patients. Complete vaccination in patients could be critical in preventing severe lung disease. Artificial intelligence analytics applied to body mass index global burden of disease worldwide cohort data derives a multiple regression formula with population attributable fraction risk factor coefficients testable by all nine Bradford Hill causality criteria BackgroundArtificial intelligence (AI) analytics have not been applied to global burden of disease (GBD) risk factor data to study population health. The comparative risk assessment (CRA) systematic literature review-based methodology for population attributable fractions (PAFs in percents) calculations has not been utilised for quantifying dietary and other risk factors for body mass index kg/M2 (BMI).

MethodsInstitute of Health Metrics and Evaluation (IHME) staff and volunteer collaborators analysed over 12,000 GBD risk factor surveys of people from 195 countries and synthesized the data into representative mean cohort BMI and risk factor values. We formatted IHME GBD data relevant to BMI and associated risk factors. We empirically explored the univariate and multiple regression correlations of BMI risk factors with worldwide BMI to derive a BMI multiple regression formula (BMI formula). Main outcome measures included the performances of the BMI formula when tested with all nine Bradford Hill causality criteria each scored on a 0-5 scale: 0=negative to 5=very strong support.

FindingsThe BMI formula derived, with all foods in kilocalories/day (kcal/day), BMI formula risk factor coefficients were adjusted to equate with their PAFs. BMI increasing foods had "+" signs and BMI decreasing foods "-" signs. Total BMI formula PAF=80.96%. BMI formula=(0.37%*processed meat + 4.23%*red meat + 0.02%*fish + 2.24%*milk + 5.67%*poultry + 1.77%*eggs + 0.34%*alcohol + 0.99%*sugary beverages + 0.04%*corn + 0.72%*potatoes + 8.48%*saturated fatty acids + 3.89%*polyunsaturated fatty acids + 0.27%*trans fatty acids - 2.99%*fruit - 4.07%*vegetables - 0.37%*nuts and seeds - 0.45%*whole grains - 1.49%*legumes - 8.62%*rice - 0.10%*sweet potatoes - 7.45% physical activity (METs/week) - 20.38%*child underweight + 6.02%*sex (male=1, female=2))*0.05012 + 21.77. BMI formula versus BMI: r=0.907, 95% CI: 0.903 to 0.911, p<0.0001. Bradford Hill causality criteria test scores (0-5): (1) strength=5, (2) experimentation=5, (3) consistency=5, (4) dose-response=5, (5) temporality=5, (6) analogy=4, (7), plausibility=5, (8) specificity=5, and (9) coherence=5. Total score=44/45.

InterpretationNine Bradford Hill causality criteria strongly supported a causal relationship between the BMI formula derived and mean BMIs of worldwide cohorts. The artificial intelligence methodology introduced could inform individual, clinical, and public health strategies regarding overweight/obesity prevention/treatment and other health outcomes.

FundingNone

Research in contextO_ST_ABSEvidence before this studyC_ST_ABSComparative risk assessment (CRA) systematic literature review-based methodology has been used in worldwide global burden of disease (GBD) analysis to determine population attributable fraction(s) (PAF(s)) for one or more risk factors for various health outcomes. So far, CRA has not been applied to derive PAFs for dietary and other risk factors for worldwide BMI. Artificial intelligence (AI) analytics has not yet been applied to worldwide GBD data as an alternative to the CRA methodology for determining risk factor PAFs for health outcomes.

Added value of this study{square}A multiple regression derived BMI formula (BMI formula) including PAFs of 20 dietary risk factors, physical activity, childhood severe underweight, and sex satisfied all nine Bradford Hill causality criteria. The BMI formula also plausibly predicted the long-term BMI outcomes related to various dietary and physical activity scenarios. All the BMI formulas 24 risk factor PAFs were consistent in sign (+ or -) with the preponderance of previously published studies on those risk factors related to BMI.

Implications of all the available evidenceThe AI analytics methodology of GBD data modeling of BMI and associated risk factors infers causality of the BMI formula estimates with BMI worldwide and BMIs of subsets. This methodology may enable multiple regression formulas for risk factors of health outcomes for a range of non-communicable diseases--testable by Bradford Hill causality criteria. Correcting the Reproduction Number for Time-Varying Tests: a Proposal and an Application to COVID-19 in France We provide a novel way to correct the effective reproduction number for the time-varying amount of tests, using the acceleration index as a simple measure of viral spread dynamics (Baunez et al., 2021). Not doing so results in the reproduction number being a biased estimate of viral acceleration and we provide a formal decomposition of the resulting bias, involving the useful notions of test and infectivity intensities. When applied to French data for the COVID-19 pandemic (May 13 - November 19, 2020), our decomposition shows that the reproduction number, when considered alone, consistently underestimates the resurgence of the pandemic since the summer of 2020, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all relevant information and captures in real time the sizable time variation featured by viral circulation, it is a more parsimonious indicator to track the dynamics of an infectious disease outbreak in real time, compared to the equivalent alternative which would be to complement the reproduction number with the test and infectivity intensities. Humoral immune responses to COVID-19 vaccination in people living with HIV receiving suppressive antiretroviral therapy Humoral responses to COVID-19 vaccines in people living with HIV (PLWH) remain incompletely understood. We measured circulating antibodies against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, ACE2 displacement and live viral neutralization activities one month following the first and second COVID-19 vaccine doses in 100 adult PLWH and 152 controls. All PLWH were receiving suppressive antiretroviral therapy, with median CD4+ T-cell counts of 710 (IQR 525-935) cells/mm3. Nadir CD4+ T-cell counts ranged as low as <10 (median 280; IQR 120-490) cells/mm3. After adjustment for sociodemographic, health and vaccine-related variables, HIV infection was significantly associated with 0.2 log10 lower anti-RBD antibody concentrations (p=0.03) and [~]11% lower ACE2 displacement activity (p=0.02), but not lower viral neutralization (p=0.1) after one vaccine dose. Following two doses however, HIV was no longer significantly associated with the magnitude of any response measured. Rather, older age, a higher burden of chronic health conditions, and having received two ChAdOx1 doses (versus a heterologous or dual mRNA vaccine regimen) were independently associated with lower responses. After two vaccine doses, no significant correlation was observed between the most recent or nadir CD4+ T-cell counts and vaccine responses in PLWH. These results suggest that PLWH with well-controlled viral loads on antiretroviral therapy and CD4+ T-cell counts in a healthy range will generally not require a third COVID-19 vaccine dose as part of their initial immunization series, though other factors such as older age, co-morbidities, vaccine regimen type, and durability of vaccine responses will influence when this group may benefit from additional doses. Further studies of PLWH who are not receiving antiretroviral treatment and/or who have low CD4+ T-cell counts are needed. Reflex single-gene non-invasive prenatal testing significantly increases the cost-effectiveness of carrier screening ObjectiveTo evaluate the clinical and cost savings benefits of adoption of a carrier screen with reflex single-gene non-invasive prenatal test (sgNIPT) in prenatal care.

MethodA decision-analytic model was developed to compare carrier screen with reflex sgNIPT (maternal carrier status and fetal risk reported together) as first-line carrier screening to the traditional carrier screening workflow (positive maternal carrier screen followed by paternal screening to evaluate fetal risk). The model compared the clinical outcomes and cost effectiveness between the two screening methods. These results were used to simulate appropriate pricing for reflex sgNIPT.

ResultsReflex sgNIPT carrier screening detected 108 of 110 affected pregnancies per 100,000 births (98.5% sensitivity), whereas traditional carrier screening detected 46 of 110 affected pregnancies (41.5% sensitivity). The cost to identify one affected pregnancy was reduced by 62% in the reflex sgNIPT scenario compared to the traditional scenario. Adding together the testing cost savings and the savings from earlier clinical intervention made possible by reflex sgNIPT, the total cost savings was $37.6 million per 100,000 pregnancies. Based on these cost savings, we simulated appropriate reflex sgNIPT pricing range: if the cost to identify one affected pregnancy is the unit cost, carrier screening with reflex sgNIPT can be priced up to $1,859 per test (or $7,233 if sgNIPT is billed separately); if the cost per 100,000 pregnancies is the unit cost, carrier screening with sgNIPT can be priced up to $1,070 per test (or $2,336 if sgNIPT is billed separately).

ConclusionUsing the carrier screen with reflex sgNIPT as first-line screening improves the detection of affected fetuses by 2.4-fold and can save costs for the healthcare system. A real-life experience will be needed to assess the clinical utility and exact cost savings of carrier screen with reflex sgNIPT. Signals of significantly increased vaccine breakthrough, decreased hospitalization rates, and less severe disease in patients with COVID-19 caused by the Omicron variant of SARS-CoV-2 in Houston, Texas Genetic variants of SARS-CoV-2 continue to dramatically alter the landscape of the COVID-19 pandemic. The recently described variant of concern designated Omicron (B.1.1.529) has rapidly spread worldwide and is now responsible for the majority of COVID-19 cases in many countries. Because Omicron was recognized very recently, many knowledge gaps exist about its epidemiology and clinical severity and disease course. A comprehensive genome sequencing study of SARS-CoV-2 in the Houston Methodist healthcare system identified 862 symptomatic patients with infections caused by Omicron from late November 2021 through December 18, 2021. Omicron very rapidly increased in only three weeks to cause 90% of all new COVID-19 cases. Compared to patients infected with either Alpha or Delta variants in our healthcare system, Omicron patients were significantly younger, had significantly increased vaccine breakthrough rates, and were significantly less likely to be hospitalized. Omicron patients required less intense respiratory support and had a shorter length of hospital stay, consistent with decreased disease severity. Although the number of Omicron patients we studied is relatively small, in the aggregate the data document the unusually rapid spread and increased occurrence of COVID-19 caused by the Omicron variant in metropolitan Houston, and provide information about disease character. SARS-CoV-2 Acquisition and Immune Pathogenesis Among School-Aged Learners in Four Diverse Schools BackgroundUnderstanding SARS-CoV-2 infection in children is necessary to reopen schools safely.

MethodsWe measured SARS-CoV-2 infection in 320 learners [10.5 {+/-} 2.1(sd); 7-17 y.o.] at four diverse schools with either remote or on-site learning. Schools A and B served low-income Hispanic learners; school C served many special-needs learners; and all provided predominantly remote instruction. School D served middle- and upper-income learners, with predominantly on-site instruction. Testing occurred in the fall (2020), and 6-8 weeks later during the fall-winter surge (notable for a tenfold increase in COVID-19 cases). Immune responses and mitigation fidelity were also measured.

ResultsWe found SARS-CoV-2 infections in 17 learners only during the surge. School A (97% remote learners) had the highest infection (10/70, 14.3%, p<0.01) and IgG positivity rates (13/66, 19.7%). School D (93% on-site learners) had the lowest infection and IgG positivity rates (1/63, 1.6%). Mitigation compliance [physical distancing (mean 87.4%) and face covering (91.3%)] was remarkably high at all schools. Documented SARS-CoV-2-infected learners had neutralizing antibodies (94.7%), robust IFN-{gamma}+ T cell responses, and reduced monocytes.

ConclusionSchools can implement successful mitigation strategies across a wide range of student diversity. Despite asymptomatic to mild SARS-CoV-2 infection, children generate robust humoral and cellular immune responses.

Key PointsO_LISuccessful COVID-19 mitigation was implemented across a diverse range of schools.
C_LIO_LISchool-associated SARS-CoV-2 infections reflect regional rates rather than remote or on-site learning.
C_LIO_LISeropositive school-aged children with asymptomatic to mild SARS-CoV-2 infections generate robust humoral and cellular immunity.
C_LI Macronutrient mass intake explains deferential weight and fat loss in isocaloric diets Differential weight and fat losses under isocaloric diets of distinct macronutrient composition are well-documented findings in obesity research.1-6 Such data are considered a result of inadequate methodology as it disagree with the energy balance theory.7 A recent mathematical analysis of this paradigm has found, however, serious analytical contradictions in its foundations.8 As an alternative, a mass balance model was proposed to explain the aforesaid body composition alterations. Here, we expand on this observation by contrasting both models. We show that mass balance explains a wide range of fending experiments including those concurring with the energy balance principle. The latter, however, is less flexible and results in poor forecasts in settings consistent with mass balance. The energy balance theory is thus an unsatisfactory model of body composition changes. Consequently, by shifting to a mass balance paradigm of obesity a much deeper understanding of this disease may follow in the near future. Colorectal Cancer Disparities Across the Continuum of Cancer Care with a Focus on Surgery and Black Patients: A Systematic Review and Meta-Analysis ImportanceDespite increases in screening among vulnerable populations, colorectal cancer disparities persist.

ObjectiveTo direct future efforts towards high impact opportunities in addressing these disparities, we performed a systematic review and meta-analysis of the colorectal cancer disparities literature with a focus on the utilization of Surgery, the primary treatment for colorectal cancer.

Data SourcesTwo electronic databases (PubMed and SCOPUS) were searched for prospective and retrospective studies reporting on colorectal cancer disparities between 2011 and March 29, 2021.

Study SelectionStudies for the systematic review were selected if: (1) reported on disparities within colorectal, colon, or rectal cancer (2) articles were published in the English language (3) articles used only United States-sourced data.

Studies for the meta-analysis were selected if: (1) reported on at least one of three utilization of surgery outcomes: receipt of colorectal, colon, or rectal surgery, refusal of surgery, or receipt of laparoscopic versus open surgery (2) Outcomes were reported as Odds ratio according to multivariate analysis.

Data Extraction and SynthesisQuality appraisal and data extraction were performed independently by 2 reviewers using the Newcastle-Ottawa Scale for quality assessment. For the meta-analysis, pooled multivariate odds ratios for the three outcomes regarding utilization of surgery were separately calculated according to fixed or random-effects models. Reporting followed the Preferred Reporting Items for Systematic Review and Meta-analyses guidelines.

Main Outcomes and MeasuresSystematic Review: The volume of publications attributed to the following sectors of the colorectal cancer care continuum: (1) Prevention, Screening, or Diagnosis (2) Treatment (3) Survivorship (4) End-of-life Care. Meta-analysis: Receipt of Colorectal, Colon, or Rectal Surgery and Black Race, Refusal of Surgery and Black Race, or Receipt of Laparoscopic versus Open Surgery and Black Race.

ResultsThe systematic review included 1,199 relevant publications. Among them, 60% were focused on Prevention, Screening, or Diagnosis, followed by Survivorship (20%), Treatment (15%), and End-of-Life Care (1%). Within the studies reporting on treatment disparities, 46% were focused on Race/Ethnicity, followed by Socioeconomic Status (28%), Age (17%), Gender (6%), Disabilities/Comorbidities (3%), and LGBTQI+ (0%). A total of 16 studies, including 1,110,674 patients, were applied to three separate meta-analyses regarding utilization of Surgery. Patients of black race were less likely to receive colorectal, or colon, or rectal surgery (OR 0.75, 95% CI 0.67-0.83), less likely to receive laparoscopic versus open colorectal cancer surgery (OR 0.91, 95% CI 0.88-0.94), and twice as likely to refuse colon surgery (OR 2.41, 95% CI 1.91-3.06).

Conclusions and RelevanceThe majority of research, to date, related to colorectal cancer disparities is focused on prevention, screening, or diagnosis and a minimal amount is dedicated to treatment. Additionally, all of the limited publications related to colorectal cancer surgery, as a treatment modality, are observational in nature. The application of this knowledge of past efforts and current continuing adverse outcomes is required in order to shift the focus of the research community towards treatment. In particular, research addressing surgical disparities is necessary, as surgery remains the primary treatment for colorectal cancer. 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.