bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing surveyed adults with long COVID and those with just a positive test helps put COVID into perspective
Authors: Adams Mary L. , Grandpre Joseph 
Published: February 2024

AbstractIntroductionAmong adults who test positive for COVID-19, some develop long COVID (symptoms lasting ≥3 months), and some do not. We compared 3 groups on selected measures to help determine strategies to reduce COVID impact.MethodsUsing Stata and data for 385,617 adults from the 2022 Behavioral Risk Factor Surveillance System, we compared adults reporting long COVID, those with just a positive test, and those who never tested positive, on several health status and risk factor measures plus vaccination rates (data for 178,949 adults in 29 states).ResultsPrevalence of just COVID was 26.5% (95% CI 26.2-26.8) and long COVID was 7.4% (7.3-7.6). Compared with adults with just COVID those with long COVID had worse rates for 13 of 17 measures of chronic disease, disability, and poor health status, while those with just COVID had the best results for 15 of the 17 measures among all 3 groups. The 5 risk factors (obesity, diabetes, asthma, cardiovascular disease, and COPD) previously associated with COVID deaths, increased long COVID but not just COVID rates, which were highest among younger and higher income adults. Adults with long COVID had the highest rate among the 3 groups for any COVID risk factors and data from 29 states showed they had the lowest rates for ≥3 vaccine doses of 35.6%, vs. 42.7% and 50.3% for those with just a positive test, and neither, respectively. Vaccination with ≥3 vaccines vs. <3 reduced long COVID rates by 38%, and just COVID rates by 16%.ConclusionsResults show the seriousness of long COVID vs. just a positive test and that increasing vaccine coverage by targeting adults with risk factors shows promise for reducing COVID impact.
Clinical and epidemiological characteristics of Madariaga and Venezuelan equine encephalitis virus infections
Authors: Rivera Luis Felipe , Lezcano-Coba Carlos , Galué Josefrancisco , Rodriguez Xacdiel , Juarez Yelissa , de Souza William M. , Capitan-Barrios Zeuz , Valderrama Anayansi , Abrego Leyda , Cedeño Hector , Jackman Carmela , Waggoner Jesse J. , Aguilar Patricia V. , Guzman Hilda , Weaver Scott C. , Tesh Robert B. , López-Vèrges Sandra , Donnelly Christl A. , Estofolete Cassia F. , Nogueira Mauricio L. , Faria Nuno R. , Vasilakis Nikos , Vittor Amy Y. , Smith Darci R. , Carrera Jean-Paul 
Published: February 2024

AbstractMadariaga virus (MADV) and Venezuelan equine encephalitis virus (VEEV) are emerging arboviruses affecting rural and remote areas of Latin America. However, there are limited clinical and epidemiological reports available, and outbreaks are occurring at an increasing frequency. We addressed this gap by analyzing all the available clinical and epidemiological data of MADV and VEEV infections recorded since 1961 in Panama. A total of 168 of human alphavirus encephalitis cases were detected in Panama from 1961 to 2023. Here we describe the clinical signs and symptoms and epidemiological characteristics of these cases, and also explored signs and symptoms as potential predictors of encephalitic alphavirus infection when compared to those of other arbovirus infections occurring in the region. Our results highlight the challenges clinical diagnosis of alphavirus disease in endemic regions with overlapping circulation of multiple arboviruses.
Distinguishing different psychiatric disorders using DDx-PRS
Authors: Peyrot Wouter J. , Panagiotaropoulou Georgia , Olde Loohuis Loes M. , Adams Mark J. , Awasthi Swapnil , Ge Tian , McIntosh Andrew M. , Mitchell Brittany L. , Mullins Niamh , O’Connell Kevin S , Penninx Brenda W.J.H. , Posthuma Danielle , Ripke Stephan , Ruderfer Douglas M. , Uffelmann Emil , Vilhjalmsson Bjarni J. , Zhu Zhihong ,   Schizophrenia Working Group of the Psychiatric Genomics Consortium,   Bipolar Disorder Working Group of the Psychiatric Genomics Consortium,   Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Smoller Jordan W. , Price Alkes L. 
Published: February 2024

AbstractDespite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS (N=41,917-173,140 cases; totalN=1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (totalN=11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
The effect of health insurance coverage on antenatal care utilization in Cambodia: A secondary analysis of Cambodia Demographic and Health Survey 2021-2022
Authors: Um Samnang , Phan Channnarong , Dany Leng , Veha Khun , Pay Soklim , Chau Darapheak 
Published: February 2024

AbstractHealth insurance is essential in reducing or eliminating the financial constraint to accessing maternal health services caused by out-of-pocket payments. Also, it has a beneficial effect in minimizing the number of maternal and child mortality. However, limited studies in Cambodia examined the association between health insurance coverage on antenatal care (ANC) utilization. Therefore, this study have examined the effect of health insurance coverage on ANC utilization in Cambodia. The study utilized data from the 2021-2022 Cambodia Demographic and Health Surveys (CDHS). A total of 3,162 weighted women who gave birth within two years were included in the study. Multiple logistic regression analysis was explored to assess the association between health insurance coverage with women who attended four or more ANC visits. About 24.9% of the women had health insurance coverage during 2021-2022. Most (86.1%) of women attended four or more ANC visits. Women with health insurance coverage were statistically significantly associated with attending four or more ANC visits with an adjusted odds ratio (AOR = 1.6; 95% CI: 1.1–2.4). Other factors significantly associated with attending four or more ANC visits include women with higher education (AOR = 3.1; 95% CI: 1.2–7.7), secondary education (AOR = 2.3; 95% CI: 1.5–3.5), richest households (AOR = 3.2; 95% CI: 1.5-6.8), and richer households (AOR = 1.9;95% CI: 1.2-2.8). Pregnant women with health insurance coverage, who had completed at least secondary education and had a better wealth index were more likely to attend at least four ANC visits. Thus, providing health insurance coverage and improving women’s economic and educational may be essential to improving women’s access to maternal health services in Cambodia.
Proteome-Wide Assessment of Clustering of Missense Variants in Neurodevelopmental Disorders Versus Cancer
Authors: Ng Jeffrey K. , Chen Yilin , Akinwe Titilope M. , Heins Hillary B. , Mehinovic Elvisa , Chang Yoonhoo , Payne Zachary L. , Manuel Juana G. , Karchin Rachel , Turner Tychele N. 
Published: February 2024

ABSTRACTMissense de novo variants (DNVs) and missense somatic variants contribute to neurodevelopmental disorders (NDDs) and cancer, respectively. Proteins with statistical enrichment based on analyses of these variants exhibit convergence in the differing NDD and cancer phenotypes. Herein, the question of why some of the same proteins are identified in both phenotypes is examined through investigation of clustering of missense variation at the protein level. Our hypothesis is that missense variation is present in different protein locations in the two phenotypes leading to the distinct phenotypic outcomes. We tested this hypothesis in 1D protein space using our software CLUMP. Furthermore, we newly developed 3D-CLUMP that uses 3D protein structures to spatially test clustering of missense variation for proteome-wide significance. We examined missense DNVs in 39,883 parent-child sequenced trios with NDDs and missense somatic variants from 10,543 sequenced tumors covering five TCGA cancer types and two COSMIC pan-cancer aggregates of tissue types. There were 57 proteins with proteome-wide significant missense variation clustering in NDDs when compared to cancers and 79 proteins with proteome-wide significant missense clustering in cancers compared to NDDs. While our main objective was to identify differences in patterns of missense variation, we also identified a novel NDD protein BLTP2. Overall, our study is innovative, provides new insights into differential missense variation in NDDs and cancer at the protein-level, and contributes necessary information toward building a framework for thinking about prognostic and therapeutic aspects of these proteins.
Predicting post-stroke cognitive impairment using electronic health record data
Authors: Ashburner Jeffrey M. , Chang Yuchiao , Porneala Bianca , Singh Sanjula D. , Yechoor Nirupama , Rosand Jonathan M. , Singer Daniel E. , Anderson Christopher D. , Atlas Steven J. 
Published: February 2024

ABSTRACTImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main OutcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and RelevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
Spatial and Temporal Drug Usage Patterns in Wastewater Correlate with Socioeconomic and Demographic Indicators in Southern Nevada
Authors: Zhuang Xiaowei , Moshi Michael A. , Quinones Oscar , Trenholm Rebecca A. , Chang Ching-Lan , Cordes Dietmar , Vanderford Brett J. , Vo Van , Gerrity Daniel , Oh Edwin C. 
Published: February 2024

AbstractEvaluating drug use within populations in the United States poses significant challenges due to various social, ethical, and legal constraints, often impeding the collection of accurate and timely data. Here, we aimed to overcome these barriers by conducting a comprehensive analysis of drug consumption trends and measuring their association with socioeconomic and demographic factors. From May 2022 to April 2023, we analyzed 208 wastewater samples from eight sampling locations across six wastewater treatment plants in Southern Nevada, covering a population of 2.4 million residents with 50 million annual tourists. Using bi-weekly influent wastewater samples, we employed mass spectrometry to detect 39 analytes, including pharmaceuticals and personal care products (PPCPs) and high risk substances (HRS). Our results revealed a significant increase over time in the level of stimulants such as cocaine (pFDR=1.40×10-10) and opioids, particularly norfentanyl (pFDR =1.66×10-12), while PPCPs exhibited seasonal variation such as peak usage of DEET, an active ingredient in insect repellents, during the summer (pFDR =0.05). Wastewater from socioeconomically disadvantaged or rural areas, as determined by Area Deprivation Index (ADI) and Rural-Urban Commuting Area Codes (RUCA) scores, demonstrated distinct overall usage patterns, such as higher usage/concentration of HRS, including cocaine (p=0.05) and norfentanyl (p=1.64×10-5). Our approach offers a near real-time, comprehensive tool to assess drug consumption and personal care product usage at a community level, linking wastewater patterns to socioeconomic and demographic factors. This approach has the potential to significantly enhance public health monitoring strategies in the United States.
Racial and Ethnic Differences in Obesity Treatment in the Veterans Affairs Healthcare System
Authors: Tisdale Rebecca L. , Beyene Tariku J. , Tang Wilson , Heidenreich Paul , Asch Steven , Yong Celina M. 
Published: February 2024

AbstractBackgroundThe burden of obesity in the United States and in the Veterans Affairs (VA) population disproportionately affects minoritized individuals. Major advances in the treatment of obesity have emerged in the last decade, including incretin-based injectables like semaglutide, but it is not clear that they are being distributed equitably. We examined the role that race and ethnicity play in the receipt of a lifestyle change program, medications, and surgical treatment for obesity.MethodsWe analyzed data from patients with BMI ≥27 in the national VA Healthcare System from 2018-2022. We used multivariate logistic regression to evaluate the association between race/ethnicity (American Indian/Alaska Native [AI/AN], Asian/Native Hawaiian/Pacific Islander [NHOPI], Non- Hispanic Black, Hispanic/Latino, Non-Hispanic White) and use of weight loss interventions (VA lifestyle change program, medication prescriptions, bariatric surgery), adjusting for age, gender, comorbidities, and diagnosis year.ResultsAmong Veterans with BMI ≥27 (n=2,423,070), 38.8% had Class 1 obesity (BMI 30-34.9), 18.1% had Class 2 obesity (BMI 35-39.9), and 9.8% had Class 3 obesity (BMI>40), with significant differences by race/ethnicity (p<0.01). Across the cohort, 7.7% participated in the lifestyle change program, 7.9% received weight loss medications, and 0.1% underwent bariatric surgery. Compared to Non-Hispanic White patients, Veterans belonging to many racial/ethnic minority groups were more likely to participate in the lifestyle change program (Asian/NHOPI Veterans, 1.12 [95% CI 1.06-1.19]; Non-Hispanic Black Veterans, adj OR 1.24 [95% confidence interval [CI] 1.22-1.26]; Hispanic/Latino Veterans, adj OR 1.17 [95% CI 1.14-1.20]) and less likely to receive weight loss medications (AI/AN Veterans, adj OR 0.84 [95% CI 0.77-0.92]; Asian/NHOPI Veterans: adj OR 0.94 [95% CI 0.89-0.999]; Non-Hispanic Black Veterans, adj OR 0.75 [95% CI 0.74-0.76]; Hispanic/Latino Veterans, adj OR 0.94 [95% CI 0.91-0.97]). Black Veterans were also less likely to undergo bariatric surgery (adj OR 0.79 [95% CI 0.69-0.89]).ConclusionsAmong Veterans with obesity, rates of treatment across all modalities are low. Inequities in treatment approach by race/ethnicity suggest areas for focused intervention to close gaps in care.
Impact of Intravenous Iron Therapy on Hospitalizations and Mortality in Patients with Heart Failure and Iron Deficiency: A Systematic Review and Meta-Analysis
Authors: Wang Haiming , Li Yanhua , Zhou Jingjing , Wang Jing , Shao Junjie , Yue Shuai , Li Jiayue , Guo Xinhong , Zhang Ran 
Published: February 2024

ABSTRACTBACKGROUNDIntravenous iron therapy represents a promising potential treatment option for patients with heart failure (HF) and iron deficiency (ID), as it has been shown to improve clinical symptoms and enhance quality of life. To investigate the benefits of intravenous iron therapy on hard cardiovascular endpoints in HF and ID patients, we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs).METHODSWe implemented a systematical search of the PubMed, Embase and Cochrane Library databases for relevant RCTs of intravenous iron therapy in patients with HF and ID published from inception through January 20, 2024. Our primary endpoints of interest were HF hospitalizations, all-cause mortality, HF hospitalizations and cardiovascular death, cardiovascular hospitalizations and cardiovascular death. Sensitivity analyses and subgroup analyses were further performed to investigate additional clinical benefits in specific populations.RESULTSEleven trials encompassing a collective cohort of 6511 participants met our predefined eligibility criteria and were included in our meta-analysis. The predominant form of intravenous iron utilized in the trials included in our analysis was ferric carboxymaltose. Intravenous iron therapy yielded a 40% relative reduction in HF hospitalization (OR 0.60, 95% CI 0.51-0.70;P= 0.00001), a 46% relative reduction in HF hospitalizations and cardiovascular death (OR 0.54, 95% CI 0.46-0.63;P<0.00001) and a 53% relative reduction in cardiovascular hospitalizations and cardiovascular death (OR 0.47, 95% CI 0.37-0.59;P<0.00001). Our analysis revealed no statistically significant differences in terms of all-cause mortality (OR 0.85, 95% CI 0.72-1.01;P=0.06) while this result was fragile (reverse fragility index of 2 and reverse fragility quotient of 0.0004). Subgroup analyses revealed more favorable effects of intravenous iron therapy in trials that had a follow-up duration of ≥ 24 weeks and a sample size of over 200 cases. Intravenous iron therapy had negligible effects on infection (OR 0.86, 95% CI 0.66-1.11;P=0.25), general disorders and administration site conditions (OR 1.35, 95% CI 0.93-1.94;P=0.11), injury, poisoning and procedural complications (OR 0.96, 95% CI 0.66-1.40;P=0.85).CONCLUSIONIntravenous iron therapy in patients with HF and ID shows a significant reduction of rehospitalization for HF and cardiovascular death. The ferric carboxymaltose holds significant promise as a potential therapeutic agent for HF patients with ID.
Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images
Authors: Ryu Wi-Sun , Schellingerhout Dawid , Lee Hoyoun , Lee Keon-Joo , Kim Chi Kyung , Kim Beom Joon , Chung Jong-Won , Lim Jae-Sung , Kim Joon-Tae , Kim Dae-Hyun , Cha Jae-Kwan , Sunwoo Leonard , Kim Dongmin , Suh Sang-Il , Bang Oh Young , Bae Hee-Joon , Kim Dong-Eog 
Published: February 2024

AbstractBACKGROUNDAccurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted imaging (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype.METHODSModel training, validation, and internal testing were done in 2,988 patients with acute ischemic stroke from three stroke centers by using U-net for infarct segmentation and EfficientNetV2 for stroke subtype classification. Experienced vascular neurologists (n=5) determined stroke subtypes for external test datasets, while establishing a consensus for clinical trial datasets using the TOAST classification. Infarcts on DW images were automatically segmented using an artificial intelligence solution that we recently developed, and their masks were fed as inputs to a deep learning algorithm (DWI-only algorithm). Subsequently, another model was trained, with the presence or absence of AF included in the training as a categorical variable (DWI+AF algorithm). These models were tested: a) internally against the opinion of the labeling experts, b) against fresh external DWI data, and also c) against clinical trial DWI data acquired at a later date.RESULTSIn the training-and-validation datasets, the mean age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only algorithm and the DWI+AF algorithm respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3-60.7% and 73.7-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen’s kappa were respectively 58.1% and 0.34 for the DWI-only algorithm vs. 72.9% and 0.57 for the DWI+AF algorithm. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm.CONCLUSIONSOur deep learning algorithm trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes as accurately as a consensus of stroke experts.