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Background: A third of people living with HIV (PLHIV) in Western Africa had an undiagnosed infection in 2020. In 2019-2021, the ATLAS programme has distributed a total of 380 000 HIV self-testing (HIVST) kits to key populations (KP) including female sex workers (FSW) and men who have sex with men (MSM), and their partners in Cote d'Ivoire, Mali and Senegal. We predicted the potential impact of ATLAS and of national HIVST scale-up strategies among KP. Methods: A deterministic model of HIV transmission was calibrated to country-specific empirical HIV and intervention data over time. We simulated scenarios reflecting 1) the actual ATLAS HIVST distribution only over 2019-2021 (~2% of all tests done in countries), and 2) ATLAS followed by a scale-up of HIVST distribution to KP (total of ~570 000 kits distributed each year). Impacts on HIV diagnosis, new HIV infections and deaths were derived using counterfactual scenarios without HIVST. Findings: ATLAS was predicted to substantially increase HIV diagnosis among KP by the end of 2021, especially among MSM in Mali (94.3 percentage point [pp] increase), and a 1.0pp increase overall. ATLAS might have averted a median of 706 new HIV infections among KP over 2019-2028 in the 3 countries combined, especially among MSM, and 1794 new HIV infections (0.4-3.3% of all new HIV infections across countries) and 591 HIV-related deaths overall. HIVST scale-up increased HIV diagnosis at the end of 2028 by around 8pp among FSW and 33pp among MSM in every country. Overall increases ranged from 1.0pp (Cote d'Ivoire) to 11.0pp (Senegal). HIVST scale-up may avert 3-5% of new HIV infections among FSW, 3-10% among FSW clients, and 20-28% among MSM across countries (and 2-16% overall), and avert 13-18% of HIV-related deaths among MSM over 2019-2028. Interpretation: Scaling-up HIVST distribution among KP in Western Africa may substantially attenuate disparities in access to HIV testing and help reduce HIV infections and deaths among KP and their partners.

Background: In biomedical research, it is often desirable to seek consensus among individuals who have differing perspectives and experience. This is important when evidence is emerging, inconsistent, limited or absent. Even when research evidence is abundant, clinical recommendations, policy decisions and priority-setting may still require agreement from multiple, sometimes ideologically opposed parties. Despite their prominence and influence on key decisions, consensus methods are often poorly reported. We aimed to develop the first reporting guideline applicable to all consensus methods used in biomedical research, called ACCORD (ACcurate COnsensus Reporting Document). Methods: We followed methodology recommended by the EQUATOR Network for the development of reporting guidelines: a systematic review was followed by a Delphi process and meetings to finalise the ACCORD checklist. The preliminary checklist was drawn from the systematic review of existing literature on the quality of reporting of consensus methods and suggestions from the Steering Committee. Results: A Delphi panel (n=72) was recruited with representation from six continents and a broad range of experience, including clinical, research, policy and patient perspectives. The three rounds of the Delphi process were completed by 58, 54 and 51 panellists. The preliminary checklist of 56 items was refined to a final checklist of 35 items relating to the article title (n=1), introduction (n=3), methods (n=21), results (n=5), discussion (n=2) and other information (n=3). Conclusions: The ACCORD checklist is the first reporting guideline applicable to all consensus-based studies. It will support authors in writing accurate, detailed manuscripts, thereby improving the completeness and transparency of reporting and providing readers with clarity regarding the methods used to reach agreement. Furthermore, the checklist will make the rigour of the consensus methods used to guide the recommendations clear for readers. Reporting consensus studies with greater clarity and transparency may enhance trust in the recommendations made by consensus panels.

Purpose The majority of missense variants in clinical genetic tests are classified as variants of uncertain significance. Broadening the evidence of the PS1 and PM5 criteria has the potential to increase conclusive variant interpretation. Methods We hypothesized that incorporation of pathogenic missense variants in conserved residues across paralogous genes can increase the number of variants where ACMG PS1/PM5 criteria can be applied. We mapped over 2.5 million pathogenic and general population variants from ClinVar, HGMD, and gnomAD databases onto 9,990 genes and aligned these by gene families. Subsequently, we developed a novel framework to extend PS1/PM5 by incorporating pathogenic paralogous variants annotations (para-PS1/PM5). Results We demonstrate that para-PS1/PM5 criteria increase the number of classifiable amino acids 3.6-fold compared to PS1 and PM5. Across all gene families with at least two disease-associated genes, the calculated likelihood ratios suggest moderate evidence for pathogenicity. Moreover, for 36 genes, the extended para-PS1/PM5 criteria reach strong evidence level. Conclusion We show that single pathogenic paralogous variants incorporation at paralogous protein positions increases the applicability of the PS1 and PM5 criteria, likely leading to a reduction of variants of uncertain significance across many monogenic disorders. Future iterations of the ACMG guidelines may consider para-PS1 and para-PM5.

Objective Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). Materials and Methods A comprehensive search was conducted in MEDLINE and Embase, encompassing Jan 1st, 2011 to March 14th, 2023. The review included primary English peer-reviewed research articles studying humans, focused on use of computers to guide clinical decision making and delivering genetically guided, patient-specific assessments and/or recommendations to healthcare providers and/or patients. Results The search yielded 3,832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy. Discussion Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes. Conclusion We recommend that future research on CDS system implementation for genetically guided precision medicine should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

Abstract Background: Small and nutritionally at-risk infants aged <6 months are at high risk of death, but important evidence gaps exist on how to best identify them. We aimed to determine associations between anthropometric deficits and mortality among infants <6m admitted to inpatient therapeutic care. Methods: A secondary analysis of 2002-2008 data included 5,034 infants aged <6m from 12 countries. The prevalence, concurrence, and severity of wasted, stunted, underweight, and the Composite Index of Anthropometric Failure (CIAF) were analysed. We used logistic regression to examine the association of different anthropometric deficits with in-programme mortality. Results: Among 3,692 infants aged <6m with complete data, 3,539 (95.8%) were underweight, 3,058 (82.8%) were wasted, 2,875 (77.8%) were stunted, and 3,575 (96.8%) had CIAF. Infants with multiple anthropometric deficits were more severely wasted, stunted, and underweight. A total of 141 infants died during inpatient therapeutic care. Among these, severely wasted (116) and severely underweight (138) infants had higher odds of mortality than normal infants (OR=2.1, 95% CI: 1.2-2.7, p=0.009, and OR=3.3, 95% CI: 0.8-13.6, p=0.09, respectively). Boys had higher odds of inpatient mortality than girls (OR=1.40, 95% CI: 1.02-1.92, p=0.03). Conclusion: Multiple anthropometric deficits (CIAF) is common among infants <6m. Future work needs to explore which are the most useful indicators for programme admission and in-programme prognosis: our data supports both WLZ and WAZ, but future work which better accounts for admission bias is urgently needed. Boys appear to be most at-risk. Programmes should ensure that all infants receive timely, evidence-based, effective care.

Introduction. Stroke is a major cause of death and disability worldwide, frequently resulting in persistent cognitive deficits among survivors. These deficits negatively impact recovery and therapy engagement, and their treatment is consistently rated as high priority by stakeholders and clinicians. Although clinical guidelines endorse cognitive screening for post-stroke management, there is currently no gold standard approach for identifying cognitive deficits after stroke, and clinical stroke services lack the capacity for long-term cognitive monitoring and care. Currently available assessment tools are either not stroke-specific, not in-depth or lack scalability, leading to heterogeneity in patient assessments. Methods and Analysis. To address these challenges, a cost-effective, scalable, and comprehensive screening tool is needed to provide a stroke-specific assessment of cognition. The current study presents such a novel digital tool, the Imperial Comprehensive Cognitive Assessment in Cerebrovascular Disease (IC3), designed to detect both domain-general and domain-specific cognitive deficits in patients after stroke with minimal input from a health professional. To ensure its reliability, we will utilise multiple validation approaches, and aim to recruit a large normative sample of age-, gender-, and education-matched UK-based controls. Moreover, the IC3 assessment will be integrated within a larger prospective observational longitudinal clinical trial, where post-stroke cognition will be examined in tandem with brain imaging and blood biomarkers to identify novel multimodal biomarkers of recovery after stroke. This study will enable deeper cognitive phenotyping of patients at a large scale, whilst identifying those with highest risk of progressive cognitive decline, as well as those with greatest potential for recovery. Ethics and dissemination. This study has been approval by South West - Frenchay Research Ethics Committee (IRAS 299333), and authorized by the UK's Health Research Authority. Study registration. The study is registered as an observational trial under NCT05885295.

Importance: Low total kidney volume (TKV) is a risk factor for chronic kidney disease (CKD). However, evaluations of causal inference and prognostic utility beyond traditional biomarkers are lacking. Objective: To investigate the observational and Mendelian randomization (MR) association of TKV with kidney and cardiovascular traits and assess improvement in CKD risk prediction when TKV is added to estimated glomerular filtration rate (eGFR) and urine albumin creatinine ratio (uACR). Design: UK Biobank (2006 to 2023) cohort and two-sample MR analysis using genome-wide association study (GWAS) summary statistics. Setting: 22 assessment centers, United Kingdom. Participants: Individuals of European ancestry with kidney volume assessment derived from magnetic resonance imaging. Exclusion criteria include records of kidney transplantation, excision, congenital malformation, and cystic kidney disease. Exposures: TKV, height adjusted (htTKV), and body surface area adjusted TKV (BSA-TKV). Main Outcomes: Observational and bidirectional MR association estimates of TKV with CKD risk. Incident CKD prediction using likelihood ratio, C-statistic, calibration, and category free net absolute reclassification index (NARI). Results: Observational analysis included 34,595 individuals [median (IQR) age 64 (12) years, 17,835 (51.6%) females]. Adjusted for confounders and risk factors including eGFR and uACR, a 10 mL decrease in TKV was associated with 7% increase in the risk of incident CKD (HR 1.07, 95% CI 1.04 to 1.10, P < 0.001). Addition of prognostically significant BSA-TKV thresholds of 119 and 145 mL/m2 led to the greatest improvement in prediction performance beyond eGFR and uACR across likelihood ratio, discrimination (C-statistic 0.87, 95% CI 0.85 to 0.89, P = 0.017), calibration, and reclassification (NARI 228 per 1,000, P < 0.001). In MR, a 10 mL decrease in genetically predicted TKV was associated with 10% increase in CKD risk (OR 1.10, 95% CI 1.05 to 1.15, P < 0.001). Reciprocally, an increased risk of genetically predicted CKD by 2-fold was associated with an 8.75 mL reduction in TKV (95% CI -10.8 to -6.66, P < 0.001). There were no significant observational or MR associations of TKV with cardiovascular complications. Conclusions: A bidirectional relationship exists between TKV and CKD. Addition of TKV thresholds to eGFR and uACR can improve CKD risk stratification.

Background: Perfusion assessment in cerebrovascular disease is essential for evaluating cerebral hemodynamics and guides many current treatment decisions. Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is of great utility to generate perfusion parameter maps, but its reliance on a contrast agent with associated health risks and technical challenges limit its usability. We hypothesized that native Time-of-flight magnetic resonance angiography (TOF-MRA) can be used to generate perfusion parameter maps with an artificial intelligence (AI) method, called generative adversarial network (GAN), offering a contrast-free alternative to DSC-MRI. Methods: We propose an adapted 3D-pix2pix GAN that generates common perfusion maps from TOF-MRA images (CBF, CBV, MTT, Tmax). The models are trained on two datasets consisting of 272 patients with acute stroke and steno-occlusive disease. The performance was evaluated by the structural similarity index measure (SSIM), for the acute dataset we calculated the Dice coefficient for lesions with a time-to-maximum (Tmax) >6s. Findings: Our GAN model showed high visual overlap and high performance for all perfusion maps on both the acute stroke dataset (mean SSIM 0.88-092) and data including steno-occlusive disease patients (mean SSIM 0.83-0.98). For lesions of Tmax>6, the median Dice coefficient was 0.49. Interpretation: Our study shows that our AI model can accurately generate perfusion parameter maps from TOF-MRA images, paving the way for clinical utility. We present a noninvasive alternative to contrast agent-based imaging for the assessment of cerebral hemodynamics in patients with cerebrovascular disease. Leveraging TOF MRA data for the generation of perfusion maps represents a groundbreaking approach in cerebrovascular disease imaging. This method could greatly impact the stratification of patients with cerebrovascular diseases by providing an alternative to contrast agent-based perfusion assessment. Funding: This work has received funding from the European Commission (Horizon2020 grant: PRECISE4Q No. 777107, coordinator: DF) and the German Federal Ministry of Education and Research (Go Bio grant: PREDICTioN2020 No. 031B0154 lead: DF).

Background. Loneliness is a serious public health problem and became even more visible during the COVID-19 pandemic. Yet it is unknown which aspects of social networks are most important. Here, we evaluated social network structure and function and associations with moderate and severe social and emotional loneliness in older adults. Methods. This cross-sectional study includes online questionnaire data (SaNAE cohort, August-November 2020), in independently living Dutch adults aged 40 years and older. For the separate outcomes social and emotional loneliness, associations with structural network aspects (e.g., network diversity - having various types of relationships, and density - having network members who know each other), and functional network aspects (informational, emotional, and practical social support) were assessed and risk estimates were adjusted for the number of contacts, age, educational level, level of urbanization and chronic conditions. Multivariable logistic regression analyses were stratified by sex. Results. Of 3,396 participants (55% men; mean age 65 years), 18% were socially lonely which was associated with a less diverse and less dense network, living alone, feeling less connected to friends, not having a club membership, and fewer emotional supporters (men only) or informational supporters (women only). 28% were emotionally lonely, which was associated with being socially lonely, and more exclusively online (versus in-person) contacts (men only), and fewer emotional supporters (women only). Conclusion. Network structure and function beyond the mere number of contacts is key in loneliness, and in particular, the types of relationships are important. Public health strategies should be sex-tailored and promote network diversity and density, club membership, informational and emotional support, and in-person contact.

Depression is a widely prevalent psychiatric illness with variable levels of severity that necessitate different approaches to treatment. To enhance the management of this condition, there is a growing interest in utilizing mobile devices, especially smartphones, for remote monitoring of patients. This study aims to build prediction models for depression severity based on active and passive features collected from patients with major depressive disorder (MDD) and healthy controls to assess the feasibility of remote monitoring of depression severity. Using data from 142 participants (85 healthy controls, 67 MDD) we extracted features such as GPS-derived mobility markers, ecological momentary assessments (EMA), age, and sex to develop machine learning models of depression severity on the different diagnostic subgroups in this cohort. Our results indicate that the employed models outperformed baseline estimators in random split scenarios. However, the improvement was marginal in user-split scenarios, highlighting the need for larger and more diverse samples for clinical utility. Among the features, mood EMA emerged as the most influential predictor, followed by GPS-derived mobility features. Models also showed a significant association between depression severity and average reported mood, as well as GPS-derived mobility markers such as number of places visited and percent home. While predicting composite depression scores is important, future studies could explore predicting individual symptom items or symptom groups for a more comprehensive assessment of depression severity. Challenges for clinical utility include participant dropout, which could be addressed through more engaging app design to promote user adherence. Harmonization of phone-derived measures is also crucial to facilitate model transfer across studies. In conclusion, this study contributes valuable evidence supporting the potential utility of smartphone data for mood state monitoring and predicting depression severity. Future research should focus on predicting depression further ahead in time and addressing the challenges identified to create more robust and effective depression monitoring solutions using smartphone-based data.

Background Continuum of care for maternal health services is essential in minimizing preventable fatalities linked to pregnancy and childbirth. The study focuses on assessing determinants of maternal health service utilization i.e., four or more antenatal care (ANC) visits, institutional delivery, and postnatal care (PNC) visit within the first 2 days of delivery and the continuum of care. Methods We performed weighted analysis of Nepal Demographic and Health Survey 2022 accounting for complex survey design. Categorical variables are presented using frequency, percentage, and 95% confidence intervals (CI), while numerical variables were represented as mean and a 95% CI. We performed bivariable and multivariable binary logistic regression and the results are odds ratios presented with 95%. Results Among total participants, 80.5% (95% CI: 77.9, 82.8) had four or more antenatal care (ANC) visits, 79.4% (95% CI: 76.8, 81.9) had institutional delivery and 70.2% (95% CI:67.5, 72.9 postnatal care (PNC) visit within 2 days of delivery. The proportion of participants having both four or more ANC visits and institutional delivery was 67.6% (95% CI: 64.7, 70.4) those completing all three components of care (4 or more ANC visits, delivering in health facility and having PNC visit for mother within 2 days of delivery) was 51.2% (95% CI: 48.3, 54.0). Compared to participants in poorest wealth quintile, participants in wealthiest quintile had 12 folds higher odds (AOR: 11.96, 95% CI: 14.36, 32.79) of having both four or more ANC visits and institutional delivery. Residents of the Madhesh had lower odds (AOR: 0.47, 95% CI: 0.23, 0.99), Sudurpaschim had higher odds (AOR: 2.37, 95% CI: 1.17, 4.82) of having 4 or more ANC visits and institutional delivery compared to Koshi Province. Residents of Bagmati Province had lower odds (AOR:0.49, 95% CI: 0.28, 0.87) of having all three components of care: 4 or more ANC visits, institutional delivery and PNC visit within 2 days of delivery for mother. Conclusion There are notable differences in coverage of maternal health services based on education, wealth quintile, province and place of residence. Addressing economic inequalities and provincial differences and harnessing technology to provide and equitable access to vital maternal and newborn health initiatives.

There is a growing desire to use social data to support local evidence-based health planning and decision-making. However, the geographic boundaries which social data are disseminated for do not usually align exactly with boundaries used by local health organizations. In this paper, we propose a method we call "pseudo-geography" to estimate counts for locally-defined geographic boundaries using data on smaller spatial units. We compared six different pseudo-geography methods, using data in Saskatoon, and identified the most accurate one, which incorporates the area-weighted spatial join technique. We further found that the pseudo-geography method can be refined by eliminating the areas with few or no residents before carrying out any spatial joins. We expect this method to be more accurate in larger cities and when the ratio of the locally-defined area to the smaller spatial units gets larger.

Introduction. Physical activity (PA) promotion among school-aged youth is a global health priority. Recommendations for such promotion include implementing whole-of-school approaches that maximize resources across the school environment. This study examined schools participation in an annual, government-led, and emirate-wide initiative in Dubai, called the Dubai Fitness Challenge, in which the goal is to accrue 30 minutes of PA every day for 30 days (as such, the initiative is colloquially referred to as "Dubai 30x30"). Methods. A mixed-methods design was employed for this study. Three schools were recruited using convenience sampling. Participants were 18 physical education teachers, 20 classroom teachers, 2 principals and 45 students. Data sources included surveys, focus groups, and interviews. Data were analyzed using descriptive statistics, multinomial logistic regression, and open and axial coding to develop themes. Results. School staff reported that most Dubai 30x30 activities were provided in physical education, at break times during school, and before and after school. Students reported that they mainly participated in Dubai 30x30 activities during physical education and occasionally participated in activities after school and on weekends. During school, students were more likely to reach higher PA intensity levels when they were in contexts other than the regular classroom setting. Among school staff, physical education teachers were most involved and classroom teachers were least involved in promoting Dubai 30x30. Parent engagement was high. Staff perceived that Dubai 30x30 brought the community together, but physical education teachers also indicated there was a lack of implementation guidance and they felt burdened. Participants believed Dubai 30x30 increased PA participation and helped to promote their schools. Discussion: This study provides an initial glimpse into schools participation in Dubai 30x30 and suggests that a whole-of-school PA lens is useful in gleaning information that could help to increase and optimize PA opportunities for students.

Introduction This is the fourth phase of a longitudinal cohort study (2022-2023) to investigate the health and wellbeing of UK serving (Regulars and Reservists) and ex-serving personnel (veterans) who served during the era of the Iraq and Afghanistan conflicts. The cohort study was established in 2003 and has collected data over three previous phases including Phase 1 (2004-2006), Phase 2 (2007-2009) and Phase 3 (2014-2016). Methods and analysis Participants are eligible to take part if they completed the King's Centre for Military Health Research (KCMHR) Health and Wellbeing Cohort Study at Phase 3 (2014-2016) and consented to be recontacted. Participants meeting these criteria will be recruited through email, post, and text message to complete an online or paper questionnaire. The study provides a fourth phase of quantitative longitudinal data on this cohort. Data are being collected between January 2022 and September 2023. Health and wellbeing measures used in Phase 4 include measures used in previous phases that assess common mental disorders (CMD), post-traumatic stress disorder (PTSD) and alcohol misuse. Other areas of interest assess multiple symptom illness, employment, help-seeking, and family relationships. New topics include the impact of the British withdrawal from Afghanistan in 2021, Complex-PTSD (C-PTSD), illicit drug use, gambling, and loneliness. The main analyses will compare mental health status according to deployment experiences and serving status (serving or ex-service) reporting prevalences with 95% Confidence Intervals (CI), and Odds Ratios (ORs) with 95% CI. Analyses will describe the effect size between groups deployed to Iraq and/or Afghanistan or not deployed, and those who are currently in service versus ex-service personnel respectively. Multivariable logistic and multiple linear regression analyses will be conducted to assess various health and wellbeing outcomes and associations with risk and protective factors, adjusting for potential confounders. Ethics and dissemination Ethical approval has been granted by the Ministry of Defence Research Ethics Committee (Ref: 2061/MODREC/21). Participants are provided with information and agree to a series of consent statements before taking part. Data are kept on secure servers and in locked cabinets/offices, with access to personally identifiable information limited. Findings will be disseminated to UK Armed Forces stakeholders and international research institutions through stakeholder meetings, project reports and scientific publications.

Background: COVID-19 vaccines have played a critical role in controlling the COVID-19 pandemic. Although generally considered safe, COVID-19 vaccination has been associated with rare but severe thrombotic events, occurring mainly in the context of adenoviral vector vaccines. A better understanding of mechanisms underlying vaccine-induced hypercoagulability and prothrombotic state is needed to improve the vaccine safety profile. Methods: We assessed changes to biomarkers of endothelial function (endothelin, ET-1), coagulation (thrombomodulin, THBD and plasminogen activator inhibitor, PAI) and platelet activation (platelet-activating factor, PAF, and platelet factor 4 IgG antibody, PF4 IgG) within a three-week period after the first (prime) and second (boost) doses of Gam-Covid-Vac, an AdV5/AdV26-vectored COVID-19 vaccine. Blood plasma collected from vaccinees (n=58) was analyzed using ELISA assays. Participants were stratified by prior COVID-19 exposure based on their baseline SARS-CoV-2-specific serology results. Results: We observed a significant post-prime increase in circulating ET-1, with levels sustained after the boost dose compared to baseline. ET-1 elevation following dose-2 was most pronounced in vaccinees without prior COVID-19 exposure. Prior COVID-19 was also associated with a mild increase in PAI post-prime dose. Conclusions: Vaccination was associated with elevated ET-1 up to day 21 after the second vaccine dose, while no marked alterations to other biomarkers, including PF4 IgG, were seen. These findings suggest that a role of persistent endothelial activation following COVID-19 vaccination warrants further investigation.

Background: Sepsis is a life-threatening condition caused by a dysregulated response to infection, affecting millions of people worldwide. Early diagnosis and treatment are critical for managing sepsis and reducing morbidity and mortality rates. Materials and Methods: A systematic design approach was employed to build a model that predicts sepsis, incorporating clinical feedback to identify relevant data elements. XGBoost was utilized for prediction, and interpretability was achieved through the application of Shapely values. The model was successfully deployed within a widely used Electronic Medical Record (EMR) system. Results: The developed model demonstrated robust performance pre-operations, with a sensitivity of 92%, specificity of 93%, and a false positive rate of 7%. Following deployment, the model maintained comparable performance, with a sensitivity of 91% and specificity of 94%. Notably, the post-deployment false positive rate of 6% represents a substantial reduction compared to the currently deployed commercial model in the same health system, which exhibits a false positive rate of 30%.

Background: Consistent evidence highlights the role of stigma in impairing healthcare access in people living with HIV (PLWH), men who have sex with men (MSM), and people with both identities. We developed an incognito standardized patient (SP) approach to obtain observations of providers to inform a tailored, relevant, and culturally appropriate stigma reduction training. Our pilot cluster randomized control trial assessed the feasibility, acceptability, and preliminary effects of an intervention to reduce HIV stigma, anti-gay stigma, and intersectional stigma. Methods: Design of the intervention was informed by the results of a baseline round of incognito visits in which SPs presented standardized cases to consenting doctors. The HIV status and sexual orientation of each case was randomly varied, and stigma was quantified as differences in care across scenarios. Care quality was measured in terms of diagnostic testing, diagnostic effort, and patient-centered care. Impact of the training, which consisted of didactic, experiential, and discussion-based modules, was assessed by analyzing results of a follow-up round of SP visits using linear fixed effects regression models. Results: Feasibility and acceptability among the 55 provider participants was high. We had a 87.3% recruitment rate and 74.5% completion rate of planned visits (N=238) with no adverse events. Every participant found the training content "highly useful" or "useful." Preliminary effects suggest that, relative to the referent case (HIV negative straight man), the intervention positively impacted testing for HIV negative MSM (0.05 percentage points [PP], 95% CI,-0.24, 0.33) and diagnostic effort in HIV positive MSM (0.23 standard deviation [SD] improvement, 95% CI, -0.92, 1.37). Patient-centered care only improved for HIV positive straight cases post-training relative to the referent group (SD, 0.57; 95% CI, -0.39, 1.53). All estimates lacked statistical precision, an expected outcome of a pilot RCT. Conclusions: Our pilot RCT demonstrated high feasibility, acceptability, and several areas of impact for an intervention to reduce enacted healthcare stigma in a low-/middle-income country setting. The relatively lower impact of our intervention on care outcomes for PLWH suggests that future trainings should include more clinical content to boost provider confidence in the safe and respectful management of patients with HIV.

Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. In this study, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance (1H-NMR) spectroscopy) for incident HF. We leveraged data of 68,311 individuals and > 0.8 million person-years of follow-up from the UK Biobank (UKB) cohort to assess individual metabolite associations and to train models to predict HF risk in individuals not previously considered at risk. Specifically, we (I) fitted per-metabolite COX proportional hazards (COX-PH) models to assess individual metabolite associations and (II) trained and internally validated elastic net (EN) models to predict incident HF using the serum metabolome. We benchmarked discriminative capacities against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF, PCP-HF1). During median follow-up of {approx} 12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF; false discovery rate (FDR)-controlled P < 0.01). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters ({approx} 80 % feature reduction). The addition of metabolomics to PCP-HF improved predictive performance (C: 0.768 vs. 0.755.; continuous net reclassification improvement (NRI) = 0.287; relative integrated discrimination improvement (IDI): 17.47 %). Simplified models including age, sex and metabolomics performed almost as well as PCP-HF (C: 0.745 vs. 0.755, continuous NRI: 0.097, relative IDI: 13.445 %). Risk and survival stratification was improved by the integration of metabolomics. The assessment of serum metabolomics improves incident HF risk prediction. Scores based simply on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost- and time-effective, standardizable, and scalable single-domain alternative to more complex clinical scores.

Objective: This Phase IV prospective observational study aimed to evaluate the safety and monitor adverse events following immunization (AEFI) associated with CoronaVac, an inactivated SARS-CoV-2 vaccine, in Brazilian adult (18-59 years) and elderly ([≥]60 years) populations. Methods: Participants (n=538; 487 adults and 51 elderly) were enrolled from three public health services in Sao Paulo, Brazil. A two-dose vaccination regimen, administered 14 days apart, was used. The study assessed Adverse Reactions (AR) necessitating medical attention within seven days post-vaccination dose and monitored AEFI for a period of 42 days. Safety was monitored through a review of participant diary cards, telephone contacts, and on-site study visits. Results: Among adults, the most frequently reported local AR after the first and second dose was pain (256[52.6%] and 129 [29.5], respectively), while the most common systemic reaction was a headache (158[34.5%] and 51 [11.6%], respectively). Most local and systemic solicited ARs were of Grade 1 or 2, with these reactions being more prevalent in adults after the first dose. One serious adverse event possibly related to the vaccine was reported among adults, but there were no fatalities. Nine adult participants experienced adverse events of special interest, which included five cases of Covid-19. Conclusion: CoronaVac demonstrated safety and tolerability in the observed population. Ongoing post-marketing surveillance is crucial for the identification of rare adverse events and further affirmation of the vaccines safety profile.

Molecular epidemiologic studies of malaria parasites commonly employ amplicon deep sequencing (AmpSeq) of marker genes derived from dried blood spots (DBS) to answer public health questions related to topics such as transmission and drug resistance. As these methods are increasingly employed to inform direct public health action, it is important to rigorously evaluate the risk of false positive and false negative haplotypes derived from clinically-relevant sample types. We performed a control experiment evaluating haplotype recovery from AmpSeq of 5 marker genes (ama1, csp, msp7, sera2, and trap) from DBS containing mixtures of DNA from 1 to 10 known P. falciparum reference strains across 3 parasite densities in triplicate (n=270 samples). While false positive haplotypes were present across all parasite densities and mixtures, we optimized censoring criteria to remove 83% (148/179) of false positives while removing only 8% (67/859) of true positives. Post-censoring, the median pairwise Jaccard distance between replicates was 0.83. We failed to recover 35% (477/1365) of haplotypes expected to be present in the sample. Haplotypes were more likely to be missed in low-density samples with <1.5 genomes/{micro}L (OR: 3.88, CI: 1.82-8.27, vs. high-density samples with [≥]75 genomes/{micro}L) and in samples with lower read depth (OR per 10,000 reads: 0.61, CI: 0.54-0.69). Furthermore, minority haplotypes within a sample were more likely to be missed than dominant haplotypes (OR per 0.01 increase in proportion: 0.96, CI: 0.96-0.97). Finally, in clinical samples the percent concordance across markers for multiplicity of infection ranged from 40%-80%. Taken together, our observations indicate that, with sufficient read depth, haplotypes can be successfully recovered from DBS while limiting the false positive rate.