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Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

Rachel B Weinstein, Martijn J Schuemie, Patrick B Ryan, Paul E Stang Epidemiology, Janssen Research and Development, LLC, Titusville, NJ, USA Background: Presumed seasonal use of acetaminophen-containing products for relief of cold/influenza (“flu”) symptoms suggests that there might also be a corresponding seasonal pattern for acute liver injury (ALI), a known clinical consequence of acetaminophen overdose. Objective: The objective of this study was to determine whether there were any temporal patterns in hospitalizations for ALI that would correspond to assumed acetaminophen use in cold/flu season. Methods: In the period 2002–2010, monthly hospitalization rates for ALI using a variety of case definitions were calculated. Data sources included Truven MarketScan® Commercial Claims and Encounters (CCAE) and Medicare Supplemental and Coordination of Benefits (MDCR) databases. We performed a statistical test for seasonality of diagnoses using the periodic generalized linear model. To validate that the test can distinguish seasonal from nonseasonal patterns, we included two positive controls (ie, diagnoses of the common cold [acute nasopharyngitis] and influenza), believed to change with seasons, and two negative controls (female breast cancer and diabetes), believed to be insensitive to season. Results: A seasonal pattern was observed in monthly rates for common cold and influenza diagnoses, but this pattern was not observed for monthly rates of ALI, with or without comorbidities (cirrhosis or hepatitis), breast cancer, or diabetes. The statistical test for seasonality was significant for positive controls (P

Objective: Systematic analysis of observational medical databases for active safety surveillance is hindered by the variation in data models and coding systems. Data analysts often find robust clinical data models difficult to understand and ill suited to support their analytic approaches. Further, some models do not facilitate the computations required for systematic analysis across many interventions and outcomes for large datasets. Translating the data from these idiosyncratic data models to a common data model (CDM) could facilitate both the analysts' understanding and the suitability for large-scale systematic analysis. In addition to facilitating analysis, a suitable CDM has to faithfully represent the source observational database. Before beginning to use the Observational Medical Outcomes Partnership (OMOP) CDM and a related dictionary of standardized terminologies for a study of large-scale systematic active safety surveillance, the authors validated the model's suitability for this use by example.Validation By Example: To validate the OMOP CDM, the model was instantiated into a relational database, data from 10 different observational healthcare databases were loaded into separate instances, a comprehensive array of analytic methods that operate on the data model was created, and these methods were executed against the databases to measure performance.Conclusion: There was acceptable representation of the data from 10 observational databases in the OMOP CDM using the standardized terminologies selected, and a range of analytic methods was developed and executed with sufficient performance to be useful for active safety surveillance. [ABSTRACT FROM AUTHOR]

SUMMARYAcute respiratory infections (ARI) are the leading cause of death worldwide in children aged <5 years, and understanding contributing factors to their seasonality is important for targeting and implementing prevention strategies. In tropical climates, ARI typically peak during the pre-rainy and rainy seasons. One hypothesis is that rainfall leads to more time spent indoors, thus increasing exposure to other people and in turn increasing the risk of ARI. A case-crossover study design in 718 Bangladeshi children aged <5 years was used to evaluate this hypothesis. During a 3-month period with variable rainfall, rainfall was associated with ARI [odds ratio (OR) 2·97, 95% confidence interval (CI) 1·87-4·70]; some evidence of an increased strength of association as household crowding increased was found (3 people/room, OR 3·31, 95% CI 2·03-5·38), but there was a lack of association in some of the most crowded households (5 to <6 people/room, OR 1·55, 95% CI 0·54-4·47). These findings suggest that rainfall may be increasing exposure to crowded conditions, thus leading to an increased risk of ARI, but that additional factors not captured by this analysis may also play a role. [ABSTRACT FROM AUTHOR]

BACKGROUND: 3-Phenoxybenzoic acid (3PBA), a pyrethroid metabolite, was detected in 75% of urine samples analyzed for pesticides in the U.S. National Health and Nutrition Examination Survey (NHANES) 1999-2002. NHANES also includes 24-hr diet data and information on household pesticide use, activities, occupation, demographics, and other exposure factors. OBJECTIVES: The objective of our study was to explore the relative importance of diet versus nondiet predictors in explaining variability in urinary 3PBA. A secondary objective was to explore whether the NHANES data could be used to identify particular foods driving 3PBA levels. METHODS: We divided subjects into child (6-10 years of age), teen (11-18 years), and adult (> or = 19 years) age groups and restricted our analyses to subjects in the morning sampling session who fasted for > or = 8 hr beforehand. Regression modeling consisted of several model-building steps and a final Tobit regression on the left-censored log 3PBA measurements. We also conducted bootstrap analyses to evaluate the stability of the regression parameters. RESULTS: Reported household pesticide use was not significantly associated with urinary 3PBA in any age group. Diet was significant for all three groups, and certain foods appeared to contribute more than others. Among adults, tobacco use was positively associated with 3PBA (p = 0.0326), and positive associations were suggested with the number of cytochrome p450-inhibiting medications taken (p = 0.0652) and minutes spent gardening (p = 0.0613) in the past month. CONCLUSIONS: Although exploratory, our findings underline the importance of collecting accurate data on household pesticide use and dietary intake when evaluating pyrethroid exposure-biomarker relationships. [ABSTRACT FROM AUTHOR]

To address the lack of research on the pulmonary health effects of ozone and fine particulate matter (

A prospective longitudinal study of a home health agency-based drug regimen review program was completed. A consultant pharmacist working collaboratively with home health nurses and physicians reduced medication utilization and costs to homebound patients. Within a 12 month period, medication utilization in 840 homebound patients was reduced by 27%. Projected total net medication cost saving per annum was $183,379.20 (e.g., $218.31 per patient reviewed). Implementation of similar drug regimen review programs in home-health agencies could improve patient care, prevent adverse drug reactions, and achieve cost-effective medication utilization. [ABSTRACT FROM AUTHOR]

Street-level and rooftop (three-story building) concentrations of particulate matter < or = 2.5 µm in diameter (PM2.5) were measured in downtown Trujillo, Peru, in July and August 2003 to determine the PM2.5 concentration reduction on days with normal traffic conditions (32 days) versus a day when motor vehicles were temporarily banned from the downtown district (8:00 a.m. to 6:00 p.m., July 15) known as El Dia Sin Autos ('The Day Without Cars'). The mean 8:00 a.m. to 6:00 p.m. street-level PM2.5 concentration during the motor vehicle ban (21.4 µg/m3; one day) was 49% lower than when vehicles were not impeded (42.2 ± 7.8 µg/m3 -- mean ± 1 standard deviation; 20 days). The rooftop monitoring station indicated a 20% decrease in PM2.5 concentrations (24.8 ± 2.6 µg/m3 vs. 19.9 ± 6.0 µg/m3) when motor vehicles were not present within historic downtown Trujillo. Temperature, relative humidity, and wind speed during the motor vehicle ban and during normal traffic were not significantly different (p > .05). [ABSTRACT FROM AUTHOR]

Background: Longitudinal trends in per- and polyfluoroalkyl substances (PFAS) serum concentrations across pregnancy have not been thoroughly examined, despite evidence linking prenatal PFAS exposures with adverse birth outcomes., Objectives: We sought to characterize longitudinal PFAS concentrations across pregnancy and to examine the maternal-fetal transfer ratio among participants in a study of risk and protective factors for adverse birth outcomes among African Americans., Methods: In the Atlanta African American Maternal-Child cohort (2014-2020), we quantified serum concentrations of four PFAS in 376 participants and an additional eight PFAS in a subset of 301 participants during early (8-14 weeks gestation) and late pregnancy (24-30 weeks gestation). Among these, PFAS concentrations were also measured among 199 newborns with available dried blood spot (DBS) samples. We characterized the patterns, variability, and associations in PFAS concentrations at different time points across pregnancy using intraclass correlation coefficients (ICCs), maternal-newborn pairs transfer ratios, linear mixed effect models, and multivariable linear regression, adjusting for socioeconomic and prenatal predictors., Results: Perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) were detected in > 95 % of maternal samples, with PFHxS and PFOS having the highest median concentrations. We observed high variability in PFAS concentrations across pregnancy time points ( ICC = 0.03 - 0.59 ). All median PFAS concentrations increased from early to late pregnancy, except for PFOA and N-methyl perfluorooctane sulfonamido acetic acid (NMFOSAA), which decreased [paired t -test for all PFAS p < 0.05 except for PFOA and perfluorobutane sulfonic acid (PFBS)]. Prenatal serum PFAS were weakly to moderately correlated with newborn DBS PFAS ( - 0.05 < rho < 0.49 ). The median maternal-fetal PFAS transfer ratio was lower for PFAS with longer carbon chains. After adjusting for socioeconomic and prenatal predictors, in linear mixed effect models, the adjusted mean PFAS concentrations significantly increased during pregnancy, except for PFOA. In multivariable linear regression, PFAS concentrations in early pregnancy significantly predicted the PFAS concentrations in late pregnancy and in newborns., Discussion: We found that the concentrations of most PFAS increased during pregnancy, and the magnitude of variability differed by individual PFAS. Future studies are needed to understand the influence of within-person PFAS variability during and after pregnancy on birth outcomes. https://doi.org/10.1289/EHP14334.

Background: In mechanistic and preliminary human studies, prenatal exposure to per- and polyfluoroalkyl substances (PFAS) is associated with oxidative stress, a potential contributor to maternal liver disease. Bilirubin is an endogenous antioxidant abundant in the liver that may serve as a physiological modulator of oxidative stress in pregnant people. Hence, our objective was to estimate the association between repeated measures of PFAS and bilirubin during pregnancy., Methods: The study population included 332 participants in the Atlanta African American Maternal-Child Cohort between 2014 and 2020. Serum samples were collected up to two times (early pregnancy: 6-18 gestational weeks; late pregnancy: 21-36 gestational weeks) for the measurement of perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and total bilirubin. We analyzed single PFAS with linear mixed effect regression and a mixture of the four PFAS with quantile g-computation. Models were repeated with a multiplicative interaction term to explore effect modification by study visit., Results: Overall, PFHxS was positively associated with bilirubin (β = 0.08, 95 % CI = 0.01, 0.15). We also found during late pregnancy, there was a positive association of PFHxS and the PFAS mixture with bilirubin (β = 0.12, 95 % CI = 0.02, 0.22; ψ = 0.19, 95 % CI = 0.03, 0.34, respectively). Finally, study visit modified the PFOA-bilirubin association (interaction p-value = 0.09), which was greater during early pregnancy (β = 0.08, 95 % CI = 0.01, 0.15)., Conclusion: In a prospective cohort of pregnant African Americans, an increase in PFOA, PFHxS, and the PFAS mixture was associated with an increase in bilirubin. Our results suggest that, depending on pregnancy stage, prenatal PFAS exposure disrupts the maternal liver antioxidant capacity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce the risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head clinical trials., Objectives: The aim of this study was to compare the cardiovascular effectiveness of SGLT2is, GLP-1 RAs, dipeptidyl peptidase-4 inhibitors (DPP4is), and clinical sulfonylureas (SUs) as second-line antihyperglycemic agents in T2DM., Methods: Across the LEGEND-T2DM (Large-Scale Evidence Generation and Evaluation Across a Network of Databases for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, spanning 1992 to 2021. In total, 1,492,855 patients with T2DM and cardiovascular disease (CVD) on metformin monotherapy were identified who initiated 1 of 4 second-line agents (SGLT2is, GLP-1 RAs, DPP4is, or SUs). Large-scale propensity score models were used to conduct an active-comparator target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, on-treatment Cox proportional hazards models were fit for 3-point MACE (myocardial infarction, stroke, and death) and 4-point MACE (3-point MACE plus heart failure hospitalization) risk and HR estimates were combined using random-effects meta-analysis., Results: Over 5.2 million patient-years of follow-up and 489 million patient-days of time at risk, patients experienced 25,982 3-point MACE and 41,447 4-point MACE. SGLT2is and GLP-1 RAs were associated with lower 3-point MACE risk than DPP4is (HR: 0.89 [95% CI: 0.79-1.00] and 0.83 [95% CI: 0.70-0.98]) and SUs (HR: 0.76 [95% CI: 0.65-0.89] and 0.72 [95% CI: 0.58-0.88]). DPP4is were associated with lower 3-point MACE risk than SUs (HR: 0.87; 95% CI: 0.79-0.95). The pattern for 3-point MACE was also observed for the 4-point MACE outcome. There were no significant differences between SGLT2is and GLP-1 RAs for 3-point or 4-point MACE (HR: 1.06 [95% CI: 0.96-1.17] and 1.05 [95% CI: 0.97-1.13])., Conclusions: In patients with T2DM and CVD, comparable cardiovascular risk reduction was found with SGLT2is and GLP-1 RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of SGLT2is and GLP-1 RAs should be prioritized as second-line agents in those with established CVD., Competing Interests: Funding Support and Author Disclosures This study was partially funded through National Institutes of Health grants R01 HL167858, K23 HL153775, R01 LM006910, R01 HG006139, and R01 HL169954 and the U.S. Department of Veterans Affairs under the research priority to “Put VA Data to Work for Veterans” (VA ORD 22-D4V). The funders had no role in the design and conduct of the protocol; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Dr Khera is an associate editor at JAMA; and has received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (awards R01 HL167858 and K23 HL153775) and the Doris Duke Charitable Foundation (award 2022060); has received research support, through Yale University, from Bristol Myers Squibb, Novo Nordisk, and BridgeBio; is a coinventor of U.S. provisional patent applications WO2023230345A1, US20220336048A1, 63/346,610, 63/428,569, 63/484,426, 63/508,315, 63/580,137, 63/606,203, 63/619,241, and 63/562,335, unrelated to the present work; and is a cofounder of Evidence2Health and Ensight-AI, precision health platforms to improve evidence-based cardiovascular care and cardiovascular diagnostics. Dr DuVall has received grants from Alnylam Pharmaceuticals, AstraZeneca Pharmaceuticals, Biodesix, Celgene, Cerner Enviza, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis International, and Parexel International through the University of Utah or the Western Institute for Veteran Research (outside the submitted work). Dr Man has received support from the C.W. Maplethorpe Fellowship, the National Institute of Health Research, European Commission Framework Horizon 2020, the Hong Kong Research Grant Council, and the Innovation and Technology Commission of the Hong Kong Special Administration Region Government outside the submitted work). Dr Morales is supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). In the past 3 years, Dr Krumholz has received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, the Arnold and Porter Law Firm, and the Martin/Baughman Law Firm; is a cofounder of Refactor Health, HugoHealth, and Ensight-AI; and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and, through Yale University, from Johnson & Johnson. Dr You is chief technology officer of PHI Digital Healthcare. Dr Ross has received research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation program (grant U01 FD005938), from the Agency for Healthcare Research and Quality (grant R01 HS022882), and from Arnold Ventures; has received research support from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology and from the National Heart, Lung, and Blood Institute of the National Institutes of Health (grants R01 HS025164 and R01 HL144644); and was an expert witness at the request of the relator’s attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen that was settled September 2022. Dr Thangaraj is a coinventor on provisional patent 63/606,203, unrelated to the present work, and is funded by grant 5T32 HL155000-03. Ms Blacketer, Dr Ostropolets, and Dr Ryan are employees of Johnson & Johnson. Dr Schuemie is an employee and shareholder of Johnson & Johnson. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. All rights reserved.)

Background: Prenatal exposure to phthalates, a group of synthetic chemicals widely used in consumer products, has previously been associated with adverse infant and child development. Studies also suggest that maternal depression and anxiety, may amplify the harmful effects of phthalates on infant and child neurodevelopment., Study Design: Our analysis included a subset of dyads enrolled in the Atlanta African American Maternal-Child Cohort (N = 81). We measured eight phthalate metabolites in first and second trimester (8-14 weeks and 24-32 weeks gestation) maternal urine samples to estimate prenatal exposures. Phthalate metabolite concentrations were averaged across visits and natural log-transformed for analysis. Maternal symptoms of depression and anxiety were assessed using validated questionnaires (Edinberg Postnatal Depression Scale and State Trait Anxiety Inventory, respectively) and the total score on each scale was averaged across study visits. The NICU Network Neurobehavioral Scale (NNNS) was administered at two weeks of age. Our primary outcomes included two composite NNNS scores reflecting newborn attention and arousal. Linear regression was used to estimate associations between individual phthalate exposures and newborn attention and arousal. We assessed effect modification by maternal depression and anxiety., Results: Higher levels of urinary phthalate metabolites were not associated with higher levels of infant attention and arousal, but true associations may still exist given the limited power of this analysis. In models examining effect modification by maternal depression, we observed that an interquartile range increase in mono (2-ethlyhexyl) phthalate (MEHP), mono (2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) was associated with a significant increase in newborn arousal only among those with high depressive symptoms (MEHP: β = 0.71, 95% confidence interval [CI] = 0.10, 1.32 for high, β = -0.30, 95% CI = -0.73, 0.12 for low; MEOHP: β = 0.60, 95% CI = -0.03, 1.23 for high, β = -0.12, 95% CI = -0.58, 0.33 for low; MEHHP: β = 0.54, 95% CI = -0.04, 1.11 for high, β = -0.11, 95% CI = -0.54, 0.32 for low). Similar patterns were observed in models stratified by maternal anxiety, although CIs were wide., Conclusion: Our results suggest maternal anxiety and depression symptoms may exacerbate the effect of phthalates on infant neurodevelopment. Future studies are needed to determine the optimal levels of attention and arousal in early infancy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Purpose: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab., Design: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network., Subjects: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion)., Methods: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate., Main Outcome Measures: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure., Results: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60)., Conclusions: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. All rights reserved.)

Background: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement., Methods: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors., Results: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration., Conclusions: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia., (© 2024. The Author(s).)

Per- and polyfluoroalkyl substances (PFAS) are fluorinated organic compounds used in a variety of consumer products and industrial applications that persist in the environment, bioaccumulate in biological tissues, and can have adverse effects on human health, especially in vulnerable populations. In this study, we focused on PFAS exposures in residents of senior care facilities. To investigate relationships between indoor, personal, and internal PFAS exposures, we analyzed 19 PFAS in matched samples of dust collected from the residents' bedrooms, and wristbands and serum collected from the residents. The median ∑PFAS concentrations (the sum of all PFAS detected in the samples) measured in dust, wristbands, and serum were 120 ng/g, 0.05 ng/g, and 4.0 ng/mL, respectively. The most abundant compounds in serum were linear- and branched-perfluorooctane sulfonic acid (L-PFOS and B-PFOS, respectively) at medians of 1.7 ng/mL and 0.83 ng/mL, respectively, followed by the linear perfluorooctanoic acid (L-PFOA) found at a median concentration of 0.59 ng/mL. Overall, these three PFAS comprised 80 % of the serum ∑PFAS concentrations. A similar pattern was observed in dust with L-PFOS and L-PFOA found as the most abundant PFAS (median concentrations of 13 and 7.8 ng/g, respectively), with the overall contribution of 50 % to the ∑PFAS concentration. Only L-PFOA was found in wristbands at a median concentration of 0.02 ng/g. Significant correlations were found between the concentrations of several PFAS in dust and serum, and in dust and wristbands, suggesting that the indoor environment could be a significant contributor to the personal and internal PFAS exposures in seniors. Our findings demonstrate that residents of assisted living facilities are widely exposed to PFAS, with several PFAS found in blood of each study participant and in the assisted living environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Per- and polyfluoroalkyl substances (PFAS) are man-made chemicals that are slow to break down in the environment and widely detected in humans. Epidemiological evidence suggests that prenatal exposure to perfluorooctanoic acid (PFOA), a legacy PFAS, is linked to gestational hypertension and preeclampsia. However, the relationship between other PFAS, which are structurally similar, and these outcomes remains largely understudied, despite biologic plausibility. Here, we examined associations between serum PFAS mixtures in relation to hypertensive disorders of pregnancy within a birth cohort of African Americans., Methods: Participants in the present study were enrolled in the Atlanta African American Maternal-Child cohort between 2014 and 2020 (n = 513). Serum samples collected between 8 and 14 weeks gestation were analyzed for four PFAS. Logistic regression was used to assess associations between individual natural log transformed PFAS and specific hypertensive disorders of pregnancy (preeclampsia, gestational hypertension), while quantile g-computation was used to estimate mixture effects. Preeclampsia and gestational hypertension were treated as separate outcomes in individual models. All models were adjusted for maternal education, maternal age, early pregnancy body mass index, parity, and any alcohol, tobacco, or marijuana use., Results: The geometric mean of PFOS and PFHxS was slightly lower among those with preeclampsia relative to those without a hypertensive disorder (e.g., geometric mean for PFOS was 1.89 and 1.94, respectively). Serum concentrations of PFAS were not strongly associated with gestational hypertension or preeclampsia in single pollutant or mixture models. For example, using quantile g-computation, a simultaneous one quartile increase in all PFAS was not associated with odds of gestational hypertension (odds ratio = 0.86, 95% CI = 0.60, 1.23), relative to those without a hypertensive disorder of pregnancy., Conclusions: In this birth cohort of African Americans, there was no association between serum PFAS measured in early pregnancy and hypertensive disorders of pregnancy, which may be reflective of the fairly low PFAS levels in our study population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability., (© 2024. The Author(s).)

Background: The immune system undergoes unique adaptations during pregnancy and is particularly sensitive to environmental chemicals, such as phthalates, which are associated with acute and chronic inflammatory medical conditions. However, current knowledge of how phthalate exposures are associated with systemic inflammation in pregnant people is limited by cross-sectional study designs and single chemical models. Our objective was to estimate the association between repeated measures of prenatal phthalate exposures, examined individually and collectively, and a panel of clinical inflammatory biomarkers., Methods: In the Atlanta African American Maternal-Child Cohort, biospecimens were collected at mean 11 and 26 weeks gestation (N = 126). Concentrations of eight urinary phthalate metabolites and five serum inflammatory biomarkers, including CRP, IFN-γ, IL-6, IL-10, and TNF-α, were measured. Linear mixed effect regression and quantile g-computation models were used to estimate the associations for single phthalates and their exposure mixture, respectively., Results: Participants who self-reported any use of alcohol, tobacco, or marijuana in the month prior to pregnancy had increased MEP, MBP, MiBP, and CRP, relative to those with no substance use. IFN-γ was elevated in response to MECPP (% change = 17.35, 95 % confidence interval [CI] = 0.32, 32.27), MEHHP (% change = 12.75, 95 % CI = 2.22, 24.36), MEOHP (% change = 11.63, 95 % CI = 1.21, 23.12), and their parent phthalate, ΣDEHP (% change = 15.03, 95 % CI = 0.28, 31.94). The phthalate mixture was also associated with an increase in IFN-γ (% change = 15.03, 95 % CI = 6.18, 24.61)., Conclusions: Our findings suggest DEHP metabolites induce systemic inflammation during pregnancy. The pro-inflammatory cytokine IFN-γ may play an important role in the relationship between prenatal phthalate exposures and adverse pregnancy outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Objectives: Chart review as the current gold standard for phenotype evaluation cannot support observational research on electronic health records and claims data sources at scale. We aimed to evaluate the ability of structured data to support efficient and interpretable phenotype evaluation as an alternative to chart review., Materials and Methods: We developed Knowledge-Enhanced Electronic Profile Review (KEEPER) as a phenotype evaluation tool that extracts patient's structured data elements relevant to a phenotype and presents them in a standardized fashion following clinical reasoning principles. We evaluated its performance (interrater agreement, intermethod agreement, accuracy, and review time) compared to manual chart review for 4 conditions using randomized 2-period, 2-sequence crossover design., Results: Case ascertainment with KEEPER was twice as fast compared to manual chart review. 88.1% of the patients were classified concordantly using charts and KEEPER, but agreement varied depending on the condition. Missing data and differences in interpretation accounted for most of the discrepancies. Pairs of clinicians agreed in case ascertainment in 91.2% of the cases when using KEEPER compared to 76.3% when using charts. Patient classification aligned with the gold standard in 88.1% and 86.9% of the cases respectively., Conclusion: Structured data can be used for efficient and interpretable phenotype evaluation if they are limited to relevant subset and organized according to the clinical reasoning principles. A system that implements these principles can achieve noninferior performance compared to chart review at a fraction of time., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome., Materials and Methods: We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates., Results: Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52., Discussion: The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition., Conclusion: Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network., Competing Interests: R.M., A.S., G.R., E.A.V., A.G.S., K.H., and P.B.R. are employees of Janssen Pharmaceutical Company of Johnson & Johnson and shareholder of Johnson & Johnson. A.O. has received funding from the US National Institutes of Health and the US Food and Drug Administration. G.H. received support through a grant to Columbia University (NIH R01 LM006910). M.A.S. receives grants from the US Department of Veterans Affairs within the scope of this work, and grants and contracts from the US National Institutes of Health, US Food & Drug Administration, and Janssen Research & Development outside the scope of this work. P.R.R. has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 806968. M.A.M., F.N., J.M.R.-A., F.M., T.D.-S., S.D., V.P., L.Y.H.L., and T.M.A. have no conflicts of interest to declare that are directly relevant to the contents of this study., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Prenatal exposure to single chemicals belonging to the per- and polyfluoroalkyl substances (PFAS) family is associated with biological perturbations in the mother, fetus, and placenta, plus adverse health outcomes. Despite our knowledge that humans are exposed to multiple PFAS, the potential joint effects of PFAS on the metabolome remain largely unknown. Here, we leveraged high-resolution metabolomics to identify metabolites and metabolic pathways perturbed by exposure to a PFAS mixture during pregnancy. Targeted assessment of perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorooctanesulfonic acid (PFOS), and perfluorohexanesulfonic acid (PFHxS), along with untargeted metabolomics profiling, were conducted on nonfasting serum samples collected from pregnant African Americans at 6-17 weeks gestation. We estimated the overall mixture effect and partial effects using quantile g-computation and single-chemical effects using linear regression. All models were adjusted for maternal age, education, parity, early pregnancy body mass index, substance use, and gestational weeks at sample collection. Our analytic sample included 268 participants and was socioeconomically diverse, with the majority receiving public health insurance (78%). We observed 13.3% of the detected metabolic features were associated with the PFAS mixture ( n = 1705, p < 0.05), which was more than any of the single PFAS chemicals. There was a consistent association with metabolic pathways indicative of systemic inflammation and oxidative stress (e.g., glutathione, histidine, leukotriene, linoleic acid, prostaglandins, and vitamins A, C, D, and E metabolism) across all metabolome-wide association studies. Twenty-six metabolites were validated against authenticated compounds and associated with the PFAS mixture ( p < 0.05). Based on quantile g-computation weights, PFNA contributed the most to the overall mixture effect for γ-aminobutyric acid (GABA), tyrosine, and uracil. In one of the first studies of its kind, we demonstrate the feasibility and utility of using methods designed for exposure mixtures in conjunction with metabolomics to assess the potential joint effects of multiple PFAS chemicals on the human metabolome. We identified more pronounced metabolic perturbations associated with the PFAS mixture than for single PFAS chemicals. Taken together, our findings illustrate the potential for integrating environmental mixture analyses and high-throughput metabolomics to elucidate the molecular mechanisms underlying human health.

Background: Administrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification. Further, existing algorithms are not transportable across data sources. We developed a transportable mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations., Methods: We used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12-55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ± 60-days of the mother's pregnancy episode live birth date. We compared the characteristics of linked vs. non-linked mothers and infants to assess similarity., Results: The algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all infants, respectively. 94.9% of linked infants' dates of birth were within ± 30-days of the associated mother's pregnancy episode end dates. Characteristics were largely similar in linked vs. non-linked mothers and infants. Differences included that linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who were not linked. Linked infants had less observation time and greater healthcare utilization than non-linked infants., Conclusions: We developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enable large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest studies using this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin., Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM., Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021., Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments., Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort., Main Outcomes Measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated., Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease., Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus., Competing Interests: Competing interests: This study is undertaken within Observational Health Data Sciences and Informatics, an open collaboration. RK received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award, 2022060). He is an Associate Editor of JAMA. He also receives research support, through Yale, from Bristol-Myers Squibb. He is a coinventor of US Provisional Patent Applications 63/177,117 and 63/346,610, unrelated to current work. He is also a founder of Evidence2Health, a precision health platform to improve evidence-based cardiovascular care. TD-S acknowledges receiving financial support from the Instituto de Salud Carlos III (ISCIII; Miguel Servet 2021: CP21/00023). KKCM reports grants from C W Maplethorpe Fellowship, grants from National Institute of Health Research, UK, grants from European Commission Framwork Horizon 2020, grants from Hong Kong Research Grant Council, grants from Innovation and Technology Commission of the Hong Kong Special Administration Region Government, and personal fees from IQVIA outside of the submitted work. DRM was supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). MJS is an employee and shareholder of Johnson & Johnson. HK received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, Arnold and Porter Law Firm, and Martin/Baughman Law Firm. He is a co-founder of Refactor Health and HugoHealth, and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and through Yale University from Johnson & Johnson. MAS receives contracts and grants from the National Institutes of Health, the US Department of Veterans Affairs, the US Food and Drug Administration and Janssen Research and Development, the latter two of which are outside the scope of this work. Other authors declare no competing interests., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Consumer products are common sources of exposure for phthalates and bisphenol A (BPA), which disrupt the endocrine system. Psychosocial stressors have been shown to amplify the toxic effects of endocrine disruptors but, information is limited among African Americans (AAs), who experience the highest rates of adverse pregnancy outcomes and are often exposed to the highest levels of chemical and non-chemical stressors. We examined the association between an exposure mixture of phthalate metabolites, BPA, and psychosocial stressors with gestational age at delivery and birthweight for gestational age z-scores in pregnant AA women., Study Design: Participants were enrolled in the Atlanta African American Maternal-Child Cohort (N = 247). Concentrations of eight phthalate metabolites and BPA were measured in urine samples collected at up to two timepoints during pregnancy (8-14 weeks gestation and 20-32 weeks gestation) and were averaged. Psychosocial stressors were measured using self-reported, validated questionnaires that assessed experiences of discrimination, gendered racial stress, depression, and anxiety. Linear regression was used to estimate individual associations between stress exposures (chemical and psychosocial) and birth outcomes. We leveraged quantile g-computation was used to examine joint effects of chemical and stress exposures on gestational age at delivery (in weeks) and birthweight for gestational age z-scores., Results: A simultaneous increase in all phthalate metabolites and BPA was associated with a moderate reduction in birthweight z-scores (mean change per quartile increase = -0.22, 95% CI = -0.45, 0.0). The association between our exposure mixture and birthweight z-scores became stronger when including psychosocial stressors as additional exposures (mean change per quantile increase = -0.35, 95% CI = -0.61, -0.08). Overall, we found null associations between exposure to chemical and non-chemical stressors with gestational age at delivery., Conclusions: In a prospective cohort of AA mother-newborn dyads, we observed that increased prenatal exposure to phthalates, BPA, and psychosocial stressors were associated with adverse pregnancy outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Per- and polyfluoroalkyl substances (PFAS) have been identified as environmental contributors to adverse birth outcomes. One potential mechanistic pathway could be through PFAS-related inflammation and cytokine production. Here, we examined associations between a PFAS mixture and inflammatory biomarkers during early and late pregnancy from participants enrolled in the Atlanta African American Maternal-Child Cohort ( N = 425). Serum concentrations of multiple PFAS were detected in >90% samples at 8-14 weeks gestation. Serum concentrations of interferon-γ (IFN-γ), interleukin 6 (IL-6), interleukin 10 (IL-10), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) were measured at up to two time points (8-14 weeks and 24-30 weeks gestation). The effect of the PFAS mixture on each inflammatory biomarker was examined using quantile g-computation, Bayesian kernel machine regression (BKMR), Bayesian Weighted Sums (BWS), and weighted quantile sum (WQS) regression. Across all models, the PFAS mixture was associated with increased IFN-γ, IL-10, and TNF-α at both time points, with the strongest effects being observed at 24-30 weeks. Using quantile g-computation, increasing concentrations of a PFAS mixture were associated with a 29% (95% confidence interval = 18.0%, 40.7%) increase in TNF-α at 24-30 weeks. Similarly, using BWS, the PFAS mixture was associated with increased TNF-α at 24-30 weeks (summed effect = 0.29, 95% highest posterior density = 0.17, 0.41). The PFAS mixture was also positively associated with TNF-α at 24-30 weeks using BKMR [75th vs 50th percentile: 17.1% (95% credible interval = 7.7%, 27.4%)]. Meanwhile, PFOS was consistently the main drivers of overall mixture effect across four methods. Our findings indicated an increase in prenatal PFAS exposure is associated with an increase in multiple pro-inflammatory cytokines, potentially contributing to adverse pregnancy outcomes.

Phytoremediation has been explored as a cost-effective method to remediate soil Pb contamination. A greenhouse study was conducted to evaluate the efficacy of Vigna unguiculata , Brassica pekinensis , Gomphrena globose , and Helianthus annuus for removing and immobilizing Pb in soil collected from the Westside Lead Superfund site in Atlanta. Plants were cultivated in sampled soil with a Pb concentration of 515 ± 10 mg/kg for 60 days. Soils growing H . annuus were additionally treated with ethylenediaminetetraacetic acid (EDTA) (0.1 g/kg) or compost (20% soil blend) to assess their capabilities for enhancing phytoremediation. Mean post-phytoremediation Pb concentrations in the four plant species were 23.5, 25.7, 50.0, and 58.1 mg/kg dry weight (DW), respectively, and were substantially higher than 1.55 mg/kg DW in respective plant species grown in control soils with no Pb contamination. The highest Pb concentration, translocation factor, and biomass were found in V . unguiculate among four species without soil amendments. H . annuus treated with EDTA and compost resulted in a significant increase in the total Pb uptake and larger biomass compared to non-treated plants, respectively. Although this study found that V . unguiculata was the best candidate for Pb accumulation and immobilization among four species, soil remediation was limited to 54 mg/kg in a growing season. We find that it is critically important to perform phytostabilization in a secure manner, since Pb bioavailability of edible plant parts implies the potential risk associated with their unintentional consumption. Efficiently and effectively remediating Pb-contaminated soils in a low-cost manner needs to be further studied., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Introduction: Vaccine safety surveillance commonly includes a serial testing approach with a sensitive method for 'signal generation' and specific method for 'signal validation.' The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown., Methods: We assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes., Results: The historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined., Conclusion: While serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals., (© 2023. The Author(s).)