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Objective: Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of common data models, need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself., Materials and Methods: We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall real-world evidence generation and dissemination process to promote transparency and build confidence in the resulting output., Results: The Data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3300 configurable data quality checks., Discussion: Transparently communicating how well common data model-standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research., Conclusion: Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Aim: To assess label compliance in prescription of medications approved for treatment of attention-deficit/hyperactivity disorder (ADHD) in Japan at the time of this study: methylphenidate (MPH), atomoxetine, and guanfacine., Methods: Retrospective descriptive study was conducted in prevalent-user cohorts from the Japan Medical Data Center database. Patients who were prescribed a study drug between January 1, 2013 and September 30, 2018 and were in the database for ≥30 days were included. A prescription was considered compliant if all 4 criteria were satisfied: appropriate age, daily dose not exceeding the approved maximum, no contraindicated concurrent medications, and no contraindicated conditions., Results: Among 17 418 patients who were prescribed a study drug during 2013-2018, 73% were male and 53% were children (aged <18 years). Fewer than 2% of prescriptions were for patients outside the approved age, 10%-13% of patients in the atomoxetine and MPH cohorts received ≥1 prescription exceeding maximum approved dose, no patients were co-prescribed a contraindicated medication, and 16%-18% of patients in the MPH cohorts had ≥1 contraindicated condition. During their first 500 days of use, for approximately 73%-86% of patients, all prescriptions were compliant with all label requirements., Conclusions: Among patients exposed to ADHD medications in Japan during 2013-2018, nearly all prescriptions for these medications were label-compliant for age. For >85% of patients, all prescriptions were label-compliant for dose, and for approximately 80%, all prescriptions were label-compliant for contraindicated conditions. We did not find evidence of widespread abuse or noncompliant use of prescribed ADHD medications., (© 2021 Janssen Research & Development, LLC. Neuropsychopharmacology Reports published by John Wiley & Sons Australia, Ltd on behalf of The Japanese Society of Neuropsychopharmacology.)

Evidence-Based Medicine (EBM) encourages clinicians to seek the most reputable evidence. The quality of evidence is organized in a hierarchy in which randomized controlled trials (RCTs) are regarded as least biased. However, RCTs are plagued by poor generalizability, impeding the translation of clinical research to practice. Though the presence of poor external validity is known, the factors that contribute to poor generalizability have not been summarized and placed in a framework. We propose a new population-oriented conceptual framework to facilitate consistent and comprehensive evaluation of generalizability, replicability, and assessment of RCT study quality., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Upholstered furniture is often manufactured with polyurethane foam (PUF) containing flame retardants (FRs) to prevent the risk of a fire and/or to meet flammability regulations, however, exposure to certain FRs and other chemicals have been linked to adverse health effects. This study developed a new methodology for evaluating volatile organic compound (VOC) and FR exposures to users of upholstered furniture by simulating use of a chair in a controlled exposure chamber and assessing the health significance of measured chemical exposure. Chairs with different fire-resistant technologies were evaluated for VOC and FR exposures via inhalation, ingestion, and dermal contact exposure routes. Data show that VOC exposure levels are lower than threshold levels defined by the US and global indoor air criteria. Brominated FRs were not detected from the studied chairs. The organophosphate FRs added to PUF were released into the surrounding air (0.4 ng/m 3 ) and as dust (16 ng/m 2 ). Exposure modeling showed that adults are exposed to FRs released from upholstered furniture mostly by dermal contact and children are exposed via dermal and ingestion exposure. Children are most susceptible to FR exposure/dose (2 times higher average daily dose than adults) due to their frequent hand to mouth contact., (© 2021 The Authors. Indoor Air published by John Wiley & Sons Ltd.)

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Mycotoxin contamination in animal milk is an emerging concern around the globe. Here we developed and validated an ultrahigh-performance liquid chromatography and mass spectrometry-selected reaction monitoring (UHPLC/MS-SRM) method to quantify low concentrations of aflatoxins (AFs) and ochratoxins (OTs) in routinely consumed animal milk samples collected from southern India. Stable isotope dilution methodology was applied to quantify AFB 1 , AFB 2 , AFG 1 , AFG 2 , AFM 1 , AFM 2 and OTA, OTB in n  = 38 different milk samples, using 1 mL of milk. Bioanalytical parameters including method accuracy, precision, recovery, regression analysis and stability were assessed. Dynamic ranges for quantification were between 15.6-1000 pg/mL for AFB 1 , AFB 2 , AFG 1 , and OTA; 7.8-500 pg/mL for AFM 1 , AFM 2 and OTB; 78.6-5000 pg/mL for AFG 2 . Method accuracy ranged between 80-120%, with ± 15% precision. Recoveries for spiked standards were > 88% in water and 75% in milk, with limits of quantification (LOQ) ranging between 31.3 pg/mL for AFB 1 , AFB 2 , AFG 1 and OTA, 15.6 pg/mL for AFM 1 , AFM 2 and OTB and 156 pg/mL for AFG 2 . R 2 values for regression analyses ranged between 0.9991-0.9999. AFB 2 [mean: 38 pg/mL (0.038 µg/kg)] was quantified in goat milk, AFM 1 was quantified in cow, goat, pasteurized milk [mean: 331 pg/mL (0.331 µg/kg), 406 pg/mL (0.406 µg/kg), 164 pg/mL (0.164 µg/kg)]. Additionally, 90% of cow, goat and pasteurized milk samples were above European Union (EU) limits of 50 pg/mL (0.05 µg/kg) and 40% of cow and goat milk samples were above the Food Safety Standards Authority of India (FSSAI) limit of 500 pg/mL (0.5 µg/kg). AFM 2 was also quantified in cow, goat, and pasteurized milk samples [mean: 249 pg/mL (0.249 µg/kg), 375 pg/mL (0.375 µg/kg), 81 pg/mL (0.081 µg/kg)]. Our dynamic ranges for quantification are lower than other published methods, with need for a smaller volume of milk. This validated method can be applied for routine quantification of mycotoxins in milk., Supplementary Information: The online version contains supplementary material available at (10.1007/s13197-021-04986-w)., Competing Interests: Conflicts of interestThe authors declare no conflicts of interest., (© Association of Food Scientists & Technologists (India) 2021.)

Background: Countries continue to debate the need for decontamination of cold-chain food packaging to reduce possible SARS-CoV-2 fomite transmission among workers. While laboratory-based studies demonstrate persistence of SARS-CoV-2 on surfaces, the likelihood of fomite-mediated transmission under real-life conditions is uncertain., Methods: Using a quantitative risk assessment model, we simulated in a frozen food packaging facility 1) SARS-CoV-2 fomite-mediated infection risks following worker exposure to contaminated plastic packaging; and 2) reductions in these risks attributed to masking, handwashing, and vaccination., Findings: In a representative facility with no specific interventions, SARS-CoV-2 infection risk to a susceptible worker from contact with contaminated packaging was 2·8 × 10 -3 per 1h-period (95%CI: 6·9 × 10 -6 , 2·4 × 10 -2 ). Implementation of standard infection control measures, handwashing and masks (9·4 × 10 -6 risk per 1h-period, 95%CI: 2·3 × 10 -8 , 8·1 × 10 -5 ), substantially reduced risk (99·7%). Vaccination of the susceptible worker (two doses Pfizer/Moderna, vaccine effectiveness: 86-99%) combined with handwashing and masking reduced risk to less than 1·0 × 10 -6 . Simulating increased infectiousness/transmissibility of new variants (2-, 10-fold viral shedding) among a fully vaccinated workforce, handwashing and masks continued to mitigate risk (2·0 × 10 -6 -1·1 × 10 -5 risk per 1h-period). Decontamination of packaging in addition to these interventions reduced infection risks to below the 1·0 × 10 -6 risk threshold., Interpretation: Fomite-mediated SARS-CoV-2 infection risks were very low under cold-chain conditions. Handwashing and masking provide significant protection to workers, especially when paired with vaccination., Funding: U.S. Department of Agriculture.

Objective: The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science., Methods: OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world's population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI's research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed., Results: OHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications., Conclusions: OHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Exposure to per- and polyfluoroalkyl substances (PFAS) has been associated with adverse health outcomes, especially when exposure occurs within sensitive time windows such as the pre- and post-natal periods and early childhood. However, few studies have focused on PFAS exposure distribution and predictors in pregnant women, especially among African American women. We quantified serum concentrations of the four most common PFAS collected in all 453 participants and an additional 10 PFAS in 356 participants who were pregnant African American women enrolled from 2014 to 2018 in Atlanta, Georgia, and investigated the sociodemographic predictors of exposure. Additional home environment and behavior predictors were also examined in 130 participants. Perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) were detected in >95% of the samples with PFOS having the highest concentrations (geometric mean (GM) 2.03 ng/mL). N-Methyl perfluorooctane sulfonamido acetic acid (NMeFOSAA), perfluoropentanoic acid (PFPeA), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUnDA) were found in 40-50% of the samples, whereas the detection frequencies for the other six PFAS were below 15%. When compared to National Health and Nutrition Examination Survey (NHANES) participants matching sex, race, and age with this study, our results showed similar concentrations of most PFAS, but higher concentrations of PFHxS (GM 0.99 ng/mL in this study; 0.63 and 0.4 ng/mL in NHANES, 2014-2015 and 2016-2017 cycles). A decline in concentrations over the study period was found for most PFAS but not PFPeA. In adjusted models, education, sampling year, parity, BMI, tobacco and marijuana use, age of house, drinking water source, and cosmetic use were significantly associated with serum PFAS concentrations. Our study reports the first PFAS exposure data among pregnant African American women in the Atlanta area, Georgia. The identified predictors will facilitate the setting of research priorities and enable development of exposure mitigation strategies., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: To quantify the background incidence rates of 15 prespecified adverse events of special interest (AESIs) associated with covid-19 vaccines., Design: Multinational network cohort study., Setting: Electronic health records and health claims data from eight countries: Australia, France, Germany, Japan, the Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model., Participants: 126 661 070 people observed for at least 365 days before 1 January 2017, 2018, or 2019 from 13 databases., Main Outcome Measures: Events of interests were 15 prespecified AESIs (non-haemorrhagic and haemorrhagic stroke, acute myocardial infarction, deep vein thrombosis, pulmonary embolism, anaphylaxis, Bell's palsy, myocarditis or pericarditis, narcolepsy, appendicitis, immune thrombocytopenia, disseminated intravascular coagulation, encephalomyelitis (including acute disseminated encephalomyelitis), Guillain-Barré syndrome, and transverse myelitis). Incidence rates of AESIs were stratified by age, sex, and database. Rates were pooled across databases using random effects meta-analyses and classified according to the frequency categories of the Council for International Organizations of Medical Sciences., Results: Background rates varied greatly between databases. Deep vein thrombosis ranged from 387 (95% confidence interval 370 to 404) per 100 000 person years in UK CPRD GOLD data to 1443 (1416 to 1470) per 100 000 person years in US IBM MarketScan Multi-State Medicaid data among women aged 65 to 74 years. Some AESIs increased with age. For example, myocardial infarction rates in men increased from 28 (27 to 29) per 100 000 person years among those aged 18-34 years to 1400 (1374 to 1427) per 100 000 person years in those older than 85 years in US Optum electronic health record data. Other AESIs were more common in young people. For example, rates of anaphylaxis among boys and men were 78 (75 to 80) per 100 000 person years in those aged 6-17 years and 8 (6 to 10) per 100 000 person years in those older than 85 years in Optum electronic health record data. Meta-analytic estimates of AESI rates were classified according to age and sex., Conclusion: This study found large variations in the observed rates of AESIs by age group and sex, showing the need for stratification or standardisation before using background rates for safety surveillance. Considerable population level heterogeneity in AESI rates was found between databases., Competing Interests: Competing interests: All authors have completed the ICMJE disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare the following interests: DPA receives funding from the UK National Institute for Health Research (NIHR) in the form of a senior research fellowship and the Oxford NIHR Biomedical Research Centre. XL receives the Clarendon Fund and Brasenose College Scholarship (University of Oxford) to support her DPhil study. DPA’s research group has received research grants from the European Medicines Agency; the Innovative Medicines Initiative; Amgen, Chiesi, and UCB Biopharma; and consultancy or speaker fees from Astellas, Amgen, and UCB Biopharma. GH and AO receive funding from the US National Institutes of Health (NIH) and the US Food and Drug Administration. KV and PR work for a research group that receives/received unconditional research grants from Yamanouchi, Pfizer-Boehringer Ingelheim, Novartis, GSK, Amgen, and Chiesi, none of which relates to the content of this paper. PBR, RM, AS, GR, and AGS are employees of Janssen Research and Development and shareholders in Johnson and Johnson. MAS receives grants and contracts from the FDA and the US Department of Veterans Affairs within the scope of this research, and grants and contracts from NIH, IQVIA, and Private Health Management outside the scope of this research., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.)

The SARS-CoV-2 global pandemic poses significant health risks to workers who are essential to maintaining the food supply chain. Using a quantitative risk assessment model, this study characterized the impact of risk reduction strategies for controlling SARS-CoV-2 transmission (droplet, aerosol, fomite-mediated) among front-line workers in a representative enclosed food manufacturing facility. We simulated: 1) individual and cumulative SARS-CoV-2 infection risks from close contact (droplet and aerosols at 1-3m), aerosol, and fomite-mediated exposures to a susceptible worker following exposure to an infected worker during an 8h-shift; and 2) the relative reduction in SARS-CoV-2 infection risk attributed to infection control interventions (physical distancing, mask use, ventilation, surface disinfection, hand hygiene). Without mitigation measures, the SARS-CoV-2 infection risk was largest for close contact (droplet and aerosol) at 1m (0.96, 95%CI: 0.67-1.0). In comparison, risk associated with fomite (0.26, 95%CI: 0.10-0.56) or aerosol exposure alone (0.05, 95%CI: 0.01-0.13) at 1m distance was substantially lower (73-95%). At 1m, droplet transmission predominated over aerosol and fomite-mediated transmission, however, this changed by 3m, with aerosols comprising the majority of the exposure dose. Increasing physical distancing reduced risk by 84% (1 to 2m) and 91% (1 to 3m). Universal mask use reduced infection risk by 52-88%, depending on mask type. Increasing ventilation (from 0.1 to 2-8 air changes/hour) resulted in risk reductions of 14-54% (1m) and 55-85% (2m). Combining these strategies, together with handwashing and surface disinfection, resulted in <1% infection risk. Current industry SARS-CoV-2 risk reduction strategies, particularly when bundled, provide significant protection to essential food workers., Significance Statement: Using mathematical modeling, we find that workers in enclosed food manufacturing facilities are at higher risk of SARS-CoV-2 infection from close contact transmission (exposure to large droplets and small aerosol particles) than fomite transmission. Thus, strategies protecting workers should prioritize close contact transmission pathways, such as physical distancing, universal mask use, and room air changes, with surface disinfection (reducing fomite transmission) and handwashing of secondary importance. Our work supports current international (EU-OSHA), domestic (FDA, OSHA), and food industry-standard guidance for managing COVID-19 transmission in essential workers in the food manufacturing sector. Although our model was designed for an indoor food manufacturing setting, it can be readily adapted to other indoor environments and infectious respiratory pathogens.

Background: Personalized risk assessment provides opportunities for tailoring treatment, optimizing healthcare resources and improving outcome. The aim of this study was to develop a 90-day mortality-risk prediction model for identification of high- and low-risk patients undergoing surgery for colorectal cancer., Methods: This was a nationwide cohort study using records from the Danish Colorectal Cancer Group database that included all patients undergoing surgery for colorectal cancer between 1 January 2004 and 31 December 2015. A least absolute shrinkage and selection operator logistic regression prediction model was developed using 121 pre- and intraoperative variables and internally validated in a hold-out test data set. The accuracy of the model was assessed in terms of discrimination and calibration., Results: In total, 49 607 patients were registered in the database. After exclusion of 16 680 individuals, 32 927 patients were included in the analysis. Overall, 1754 (5.3 per cent) deaths were recorded. Targeting high-risk individuals, the model identified 5.5 per cent of all patients facing a risk of 90-day mortality exceeding 35 per cent, corresponding to a 6.7 times greater risk than the average population. Targeting low-risk individuals, the model identified 20.9 per cent of patients facing a risk less than 0.3 per cent, corresponding to a 17.7 times lower risk compared with the average population. The model exhibited discriminatory power with an area under the receiver operating characteristics curve of 85.3 per cent (95 per cent c.i. 83.6 to 87.0) and excellent calibration with a Brier score of 0.04 and 32 per cent average precision., Conclusion: Pre- and intraoperative data, as captured in national health registries, can be used to predict 90-day mortality accurately after colorectal cancer surgery., (© The Author(s) 2021. Published by Oxford University Press on behalf of BJS Society Ltd.)

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Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated., Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases., Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia., Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68., Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model., (©Jenna M Reps, Chungsoo Kim, Ross D Williams, Aniek F Markus, Cynthia Yang, Talita Duarte-Salles, Thomas Falconer, Jitendra Jonnagaddala, Andrew Williams, Sergio Fernández-Bertolín, Scott L DuVall, Kristin Kostka, Gowtham Rao, Azza Shoaibi, Anna Ostropolets, Matthew E Spotnitz, Lin Zhang, Paula Casajust, Ewout W Steyerberg, Fredrik Nyberg, Benjamin Skov Kaas-Hansen, Young Hwa Choi, Daniel Morales, Siaw-Teng Liaw, Maria Tereza Fernandes Abrahão, Carlos Areia, Michael E Matheny, Kristine E Lynch, María Aragón, Rae Woong Park, George Hripcsak, Christian G Reich, Marc A Suchard, Seng Chan You, Patrick B Ryan, Daniel Prieto-Alhambra, Peter R Rijnbeek. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.04.2021.)

Mycotoxins are carcinogenic secondary metabolites of fungi that have been linked to infant growth faltering. In this study, we quantified co-occurring mycotoxins in breast milk and food samples from Haryana, India, and characterized determinants of exposure. Deterministic risk assessment was conducted for mothers and infants. We examined levels of eight mycotoxins (Aflatoxin B 1 , B 2 , G 1 , G 2 , M 1 , M 2 ; Ochratoxin A, B) in 100 breast milk samples (infants 2-4 months) using ultra-high-performance liquid chromatography tandem mass spectrometry. Aflatoxin B 1 (AFB 1 ), fumonisin B 1 (FB 1 ) and deoxynivalenol (DON) were detected in several food items (n = 298) using enzyme-linked immunosorbent assays. We report novel data on the presence of mycotoxins in breast milk samples from India. Whereas breast milk concentrations (AFM 1 median: 13.7; range: 3.9-1200 ng/L) remain low, AFM 1 was detected above regulatory limits in 27% of animal milk samples. Additionally, 41% of infants were above provisional maximum tolerable daily intake (PMTDI) limits for AFM 1 due to consumption of breast milk (mean: 3.04, range: 0.26-80.7 ng kg -1 bw day -1 ). Maternal consumption of breads (p < 0.05) was associated with breast milk AFM 1 exposure. AFB 1 (μg/kg) was detected in dried red chilies (15.7; 0-302.3), flour (3.13; 0-214.9), groundnuts (0; 0-249.1), maize (56.0; 0-836.7), pearl millet (1.85; 0-160.2), rice (0; 0-195.6), wheat (1.9; 0-196.0) and sorghum (0; 0-63.5). FB 1 (mg/kg) was detected in maize (0; 0-61.4), pearl millet (0; 0-35.4) and sorghum (0.95; 0-33.2). DON was not detected in food samples. Mothers in our study exceeded PMTDI recommendations for AFB 1 due to consumption of rice and flour (mean: 75.81; range: 35.2-318.2 ng kg -1 bw day -1 ). Our findings show the presence of Aflatoxin B 1 and M 1 at various levels of the food chain and in breast milk, with estimated intakes exceeding PMTDI recommendations. Aflatoxins are known carcinogens and have also been linked to stunting in children. Their presence across the food system and in breast milk is concerning, thus warranting further research to replicate and expand on our findings and to understand implications for maternal and child health., (© 2020 The Authors. Maternal & Child Nutrition published by John Wiley & Sons Ltd.)

Alpha-1 blockers, often used to treat benign prostate hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storms release. We conducted a prevalent-user active-comparator cohort study to assess association between alpha-1 blocker use and risks of three COVID-19 outcomes: diagnosis, hospitalization, and hospitalization requiring intensive services. Our study included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH therapy during the period between November 2019 and January 2020, found in electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We found no differential risk for any of COVID-19 outcome, pointing to the need for further research on potential COVID-19 therapies.

Many observational studies explore the association between acetaminophen and cancer, but known limitations such as vulnerability to channeling, protopathic bias, and uncontrolled confounding hamper the interpretability of results. To help understand the potential magnitude of bias, we identify key design choices in these observational studies and specify 10 study design variants that represent different combinations of these design choices. We evaluate these variants by applying them to 37 negative controls - outcome presumed not to be caused by acetaminophen - as well as 4 cancer outcomes in the Clinical Practice Research Datalink (CPRD) database. The estimated odds and hazards ratios for the negative controls show substantial bias in the evaluated design variants, with far fewer of the 95% confidence intervals containing 1 than the nominal 95% expected for negative controls. The effect-size estimates for the cancer outcomes are comparable to those observed for the negative controls. A comparison of exposed and unexposed reveals many differences at baseline for which most studies do not correct. We observe that the design choices made in many of the published observational studies can lead to substantial bias. Thus, caution in the interpretation of published studies of acetaminophen and cancer is recommended., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Researchers developing prediction models are faced with numerous design choices that may impact model performance. One key decision is how to include patients who are lost to follow-up. In this paper we perform a large-scale empirical evaluation investigating the impact of this decision. In addition, we aim to provide guidelines for how to deal with loss to follow-up., Methods: We generate a partially synthetic dataset with complete follow-up and simulate loss to follow-up based either on random selection or on selection based on comorbidity. In addition to our synthetic data study we investigate 21 real-world data prediction problems. We compare four simple strategies for developing models when using a cohort design that encounters loss to follow-up. Three strategies employ a binary classifier with data that: (1) include all patients (including those lost to follow-up), (2) exclude all patients lost to follow-up or (3) only exclude patients lost to follow-up who do not have the outcome before being lost to follow-up. The fourth strategy uses a survival model with data that include all patients. We empirically evaluate the discrimination and calibration performance., Results: The partially synthetic data study results show that excluding patients who are lost to follow-up can introduce bias when loss to follow-up is common and does not occur at random. However, when loss to follow-up was completely at random, the choice of addressing it had negligible impact on model discrimination performance. Our empirical real-world data results showed that the four design choices investigated to deal with loss to follow-up resulted in comparable performance when the time-at-risk was 1-year but demonstrated differential bias when we looked into 3-year time-at-risk. Removing patients who are lost to follow-up before experiencing the outcome but keeping patients who are lost to follow-up after the outcome can bias a model and should be avoided., Conclusion: Based on this study we therefore recommend (1) developing models using data that includes patients that are lost to follow-up and (2) evaluate the discrimination and calibration of models twice: on a test set including patients lost to follow-up and a test set excluding patients lost to follow-up.

Background: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension., Methods: In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296., Findings: Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons., Interpretation: No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19., Funding: Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data., Materials and Methods: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020-June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death., Results: There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4-28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event., Discussion: By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients., Conclusions: This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Population growth which has resulted in a need for increased crop yields to sustain food security, in addition to the effects of climate change, have led to the widespread use of chemical pesticides. The indiscriminate use of pesticides has in turn led to contamination of the environment, food commodities and bioaccumulation in human tissues, particularly in agrarian regions of India including the northern state of Haryana., Methods: We conducted a pilot screening study to investigate the presence of organochlorine, organophosphate, and pyrethroid pesticides in breastmilk samples (n = 75) from Haryana, India. Pesticide analyses were conducted using gas chromatography mass spectrometry (GC-MS) for OC and OP pesticides and GC-electron capture detector for pyrethroids. The study was complemented by a qualitative evaluation of maternal and community perceptions, knowledge, attitudes and practices associated with pesticide use and risk of exposure (n = 30 in-depth interviews; n = 9 focus group discussions)., Results: Analysis of breastmilk showed the presence of p,p'-dichlorodiphenyltrichloroethane (DDT) and p,p'-dichlorodiphenyldichloroethylene (DDE) in 4% (range:

Objective: Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza., Design: Multinational network cohort study., Setting: Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea)., Participants: All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included., Main Outcome Measures: 30-day complications during hospitalisation and death., Results: We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%)., Conclusions: Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases., What Is Already Known About This Topic: Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions., What This Study Adds: Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave., Competing Interests: COMPETING INTEREST STATEMENT All authors have completed the ICMJE uniform disclosure form, with the following declarations made: DPA reports grants from Amgen, grants and other from UCB Biopharma, grants from Johnson and Johnson, outside the submitted work. DM is supported by a Wellcome Trust Clinical Research Development Fellowship (Grant 214588/Z/18/Z) and reports grants from Chief Scientist Office (CSO), grants from Health Data Research UK (HDR-UK), grants from National Institute of Health Research (NIHR), and Tenovus outside the submitted work. SCY reports grants from Korean Ministry of Health & Welfare, grants from Korean Ministry of Trade, Industry & Energy, during the conduct of the study. AG reports personal fees from Regeneron Pharmaceuticals, outside the submitted work; and she is a full-time employee at Regeneron Pharmaceuticals. This work was not conducted at Regeneron Pharmaceuticals. Dr. Lane reports grants from Versus Arthritis, grants from Medical Research Council, outside the submitted work. MS reports grants from US National Science Foundation, grants from US National Institutes of Health, grants from IQVIA, personal fees from Janssen Research and Development, during the conduct of the study. HA reports personal fees from Eli Lilly and Company, outside the submitted work. AS reports personal fees from Janssen Research & Development, during the conduct of the study; personal fees from Janssen Research & Development, outside the submitted work. AS is a full-time employee of Janssen and shareholder of Johnson & Johnson. FD reports personal fees from Janssen Research & Development, during the conduct of the study; personal fees from Janssen Research & Development, outside the submitted work. KK reports she is an employee of IQVIA. CR reports he is an employee of IQVIA. FN was an employee of AstraZeneca until 2019 and holds some AstraZeneca shares. SF is an employee of Janssen Research and Development, a subsidiary of Johnson and Johnson. VS reports grant funding from the National Science Foundation, Agency for Healthcare Research and Quality, and the Arizona Board of Regents outside of the submitted work. The views expressed are those of the authors and do not necessarily represent the views or policy of the Department of Veterans Affairs or the United States Government. PR reports and is employee of Janssen Research and Development and shareholder of Johnson & Johnson. No other relationships or activities that could appear to have influenced the submitted work.

Importance: Current guidelines recommend ticagrelor as the preferred P2Y12 platelet inhibitor for patients with acute coronary syndrome (ACS), primarily based on a single large randomized clinical trial. The benefits and risks associated with ticagrelor vs clopidogrel in routine practice merits attention., Objective: To determine the association of ticagrelor vs clopidogrel with ischemic and hemorrhagic events in patients undergoing percutaneous coronary intervention (PCI) for ACS in clinical practice., Design, Setting, and Participants: A retrospective cohort study of patients with ACS who underwent PCI and received ticagrelor or clopidogrel was conducted using 2 United States electronic health record-based databases and 1 nationwide South Korean database from November 2011 to March 2019. Patients were matched using a large-scale propensity score algorithm, and the date of final follow-up was March 2019., Exposures: Ticagrelor vs clopidogrel., Main Outcomes and Measures: The primary end point was net adverse clinical events (NACE) at 12 months, composed of ischemic events (recurrent myocardial infarction, revascularization, or ischemic stroke) and hemorrhagic events (hemorrhagic stroke or gastrointestinal bleeding). Secondary outcomes included NACE or mortality, all-cause mortality, ischemic events, hemorrhagic events, individual components of the primary outcome, and dyspnea at 12 months. The database-level hazard ratios (HRs) were pooled to calculate summary HRs by random-effects meta-analysis., Results: After propensity score matching among 31 290 propensity-matched pairs (median age group, 60-64 years; 29.3% women), 95.5% of patients took aspirin together with ticagrelor or clopidogrel. The 1-year risk of NACE was not significantly different between ticagrelor and clopidogrel (15.1% [3484/23 116 person-years] vs 14.6% [3290/22 587 person-years]; summary HR, 1.05 [95% CI, 1.00-1.10]; P = .06). There was also no significant difference in the risk of all-cause mortality (2.0% for ticagrelor vs 2.1% for clopidogrel; summary HR, 0.97 [95% CI, 0.81-1.16]; P = .74) or ischemic events (13.5% for ticagrelor vs 13.4% for clopidogrel; summary HR, 1.03 [95% CI, 0.98-1.08]; P = .32). The risks of hemorrhagic events (2.1% for ticagrelor vs 1.6% for clopidogrel; summary HR, 1.35 [95% CI, 1.13-1.61]; P = .001) and dyspnea (27.3% for ticagrelor vs 22.6% for clopidogrel; summary HR, 1.21 [95% CI, 1.17-1.26]; P < .001) were significantly higher in the ticagrelor group., Conclusions and Relevance: Among patients with ACS who underwent PCI in routine clinical practice, ticagrelor, compared with clopidogrel, was not associated with significant difference in the risk of NACE at 12 months. Because the possibility of unmeasured confounders cannot be excluded, further research is needed to determine whether ticagrelor is more effective than clopidogrel in this setting.

Introduction: Observational studies estimating severe outcomes for paracetamol versus ibuprofen use have acknowledged the specific challenge of channeling bias. A previous study relying on negative controls suggested that using large-scale propensity score (LSPS) matching may mitigate bias better than models using limited lists of covariates., Objective: The aim was to assess whether using LSPS matching would enable the evaluation of paracetamol, compared to ibuprofen, and increased risk of myocardial infarction, stroke, gastrointestinal (GI) bleeding, or acute renal failure., Study Design and Setting: In a new-user cohort study, we used two propensity score model strategies for confounder controls. One replicated the approach of controlling for a hand-picked list. The second used LSPSs based on all available covariates for matching. Positive and negative controls assessed residual confounding and calibrated confidence intervals. The data source was the Clinical Practices Research Datalink (CPRD)., Results: A substantial proportion of negative controls were statistically significant after propensity score matching on the publication covariates, indicating considerable systematic error. LSPS adjustment was less biased, but residual error remained. The calibrated estimates resulted in very wide confidence intervals, indicating large uncertainty in effect estimates once residual error was incorporated., Conclusions: For paracetamol versus ibuprofen, when using LSPS methods in the CPRD, it is only possible to distinguish true effects if those effects are large (hazard ratio > 2). Due to their smaller hazard ratios, the outcomes under study cannot be differentiated from null effects (represented by negative controls) even if there were a true effect. Based on these data, we conclude that we are unable to determine whether paracetamol is associated with an increased risk of myocardial infarction, stroke, GI bleeding, and acute renal failure compared to ibuprofen, due to residual confounding.

Objectives: To demonstrate the application of the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) principles described in our companion article to hypertension treatments and assess internal and external validity of the generated evidence., Materials and Methods: LEGEND defines a process for high-quality observational research based on 10 guiding principles. We demonstrate how this process, here implemented through large-scale propensity score modeling, negative and positive control questions, empirical calibration, and full transparency, can be applied to compare antihypertensive drug therapies. We assess internal validity through covariate balance, confidence-interval coverage, between-database heterogeneity, and transitivity of results. We assess external validity through comparison to direct meta-analyses of randomized controlled trials (RCTs)., Results: From 21.6 million unique antihypertensive new users, we generate 6 076 775 effect size estimates for 699 872 research questions on 12 946 treatment comparisons. Through propensity score matching, we achieve balance on all baseline patient characteristics for 75% of estimates, observe 95.7% coverage in our effect-estimate 95% confidence intervals, find high between-database consistency, and achieve transitivity in 84.8% of triplet hypotheses. Compared with meta-analyses of RCTs, our results are consistent with 28 of 30 comparisons while providing narrower confidence intervals., Conclusion: We find that these LEGEND results show high internal validity and are congruent with meta-analyses of RCTs. For these reasons we believe that evidence generated by LEGEND is of high quality and can inform medical decision-making where evidence is currently lacking. Subsequent publications will explore the clinical interpretations of this evidence., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Alendronate and raloxifene are among the most popular anti-osteoporosis medications. However, there is a lack of head-to-head comparative effectiveness studies comparing the two treatments. We conducted a retrospective large-scale multicenter study encompassing over 300 million patients across nine databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporotic hip fracture, while secondary outcomes were vertebral fracture, atypical femoral fracture (AFF), osteonecrosis of the jaw (ONJ), and esophageal cancer. We used propensity score trimming and stratification based on an expansive propensity score model with all pre-treatment patient characteritistcs. We accounted for unmeasured confounding using negative control outcomes to estimate and adjust for residual systematic bias in each data source. We identified 283,586 alendronate patients and 40,463 raloxifene patients. There were 7.48 hip fracture, 8.18 vertebral fracture, 1.14 AFF, 0.21 esophageal cancer and 0.09 ONJ events per 1,000 person-years in the alendronate cohort and 6.62, 7.36, 0.69, 0.22 and 0.06 events per 1,000 person-years, respectively, in the raloxifene cohort. Alendronate and raloxifene have a similar hip fracture risk (hazard ratio [HR] 1.03, 95% confidence interval [CI] 0.94-1.13), but alendronate users are more likely to have vertebral fractures (HR 1.07, 95% CI 1.01-1.14). Alendronate has higher risk for AFF (HR 1.51, 95% CI 1.23-1.84) but similar risk for esophageal cancer (HR 0.95, 95% CI 0.53-1.70), and ONJ (HR 1.62, 95% CI 0.78-3.34). We demonstrated substantial control of measured confounding by propensity score adjustment, and minimal residual systematic bias through negative control experiments, lending credibility to our effect estimates. Raloxifene is as effective as alendronate and may remain an option in the prevention of osteoporotic fracture.

Introduction: Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results., Methods: Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments., Results: Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons., Conclusion: There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.

Background: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets., Methods: Five previously published prognostic models (ATRIA, CHADS 2 , CHADS 2 VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites., Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation., Conclusion: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

Introduction: In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of 'end-of-life' care., Objective: The objective of this study was to develop a diagnostic predictive model to identify those in end-of-life care at the time of a drug exposure., Methods: We used data from four administrative claims datasets from 2000 to 2017. The index date was the date of the first prescription for the last new drug subjects received during their observation period. The outcome of end-of-life care was determined by the presence of one or more codes indicating terminal or hospice care. Models were developed using regularized logistic regression. Internal validation was through examination of the area under the receiver operating characteristic curve (AUC) and through model calibration in a 25% subset of the data held back from model training. External validation was through examination of the AUC after applying the model learned on one dataset to the three other datasets., Results: The models showed excellent performance characteristics. Internal validation resulted in AUCs ranging from 0.918 (95% confidence interval [CI] 0.905-0.930) to 0.983 (95% CI 0.978-0.987) for the four different datasets. Calibration results were also very good, with slopes near unity. External validation also produced very good to excellent performance metrics, with AUCs ranging from 0.840 (95% CI 0.834-0.846) to 0.956 (95% CI 0.952-0.960)., Conclusion: These results show that developing diagnostic predictive models for determining subjects in end-of-life care at the time of a drug treatment is possible and may improve the validity of the risk profile for those treatments.

We developed a robust analytical method for quantification of malondialdehyde (MDA) in urine and serum samples using dansylhydrazine (DH) as a derivatizing reagent. The derivatization procedure was partially carried out using an autosampler injection program to minimize errors associated with the low-volume addition of reagents and was optimized to yield a stable hydrazone derivative of MDA and its labeled d2-MDA analogue. The target MDA-DH derivatives were separated on an Agilent Zorbax Eclipse Plus Phenyl-Hexyl (3.0 × 100 mm, 3.5 μm) column. The mass-to-charge ratios of the target derivatives [(M+H)+ of 302 and 304 for MDA-DH and d2-MDA-DH, respectively] were analyzed in single ion monitoring mode using a single quadrupole mass spectrometer operated under positive electrospray ionization. The method limits of quantification were 5.63 nM (or 0.405 ng/mL) for urine analysis and 5.68 nM (or 0.409 ng/mL) for serum analysis. The quantification range for urine analysis was 5.63-500 nM (0.405-36.0 ng/mL) while the quantification range for serum analysis was 5.68-341 nM (0.409-24.6 ng/mL). The method showed good relative recoveries (98-103%), good accuracies (92-98%), and acceptable precisions (relative standard deviations 1.8-7.3% for inter-day precision; 1.8-6.1% for intra-day precision) as observed from the repeat analysis of quality control samples prepared at different concentrations. The method was used to measure MDA in individual urine samples (n = 287) and de-identified archived serum samples (n = 22) to assess the overall performance of the method. The results demonstrated that our method is capable of measuring urinary and serum levels of MDA, allowing its future application in epidemiologic investigations., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Biomarkers of exposure, susceptibility, and effect are fundamental for understanding environmental exposures, mechanistic pathways of effect, and monitoring early adverse outcomes. To date, no study has comprehensively evaluated a large suite and variety of biomarkers in household air pollution (HAP) studies in concert with exposure and outcome data. The Household Air Pollution Intervention Network (HAPIN) trial is a liquified petroleum gas (LPG) fuel/stove randomized intervention trial enrolling 800 pregnant women in each of four countries (i.e., Peru, Guatemala, Rwanda, and India). Their offspring will be followed from birth through 12 months of age to evaluate the role of pre- and postnatal exposure to HAP from biomass burning cookstoves in the control arm and LPG stoves in the intervention arm on growth and respiratory outcomes. In addition, up to 200 older adult women per site are being recruited in the same households to evaluate indicators of cardiopulmonary, metabolic, and cancer outcomes., Objectives: Here we describe the rationale and ultimate design of a comprehensive biomarker plan to enable us to explore more fully how exposure is related to disease outcome., Methods: HAPIN enrollment and data collection began in May 2018 and will continue through August 2021. As a part of data collection, dried blood spot (DBS) and urine samples are being collected three times during pregnancy in pregnant women and older adult women. DBS are collected at birth for the child. DBS and urine samples are being collected from the older adult women and children three times throughout the child's first year of life. Exposure biomarkers that will be longitudinally measured in all participants include urinary hydroxy-polycyclic aromatic hydrocarbons, volatile organic chemical metabolites, metals/metalloids, levoglucosan, and cotinine. Biomarkers of effect, including inflammation, endothelial and oxidative stress biomarkers, lung cancer markers, and other clinically relevant measures will be analyzed in urine, DBS, or blood products from the older adult women. Similarly, genomic/epigenetic markers, microbiome, and metabolomics will be measured in older adult women samples., Discussion: Our study design will yield a wealth of biomarker data to evaluate, in great detail, the link between exposures and health outcomes. In addition, our design is comprehensive and innovative by including cutting-edge measures such as metabolomics and epigenetics. https://doi.org/10.1289/EHP5751.

Importance: Chlorthalidone is currently recommended as the preferred thiazide diuretic to treat hypertension, but no trials have directly compared risks and benefits., Objective: To compare the effectiveness and safety of chlorthalidone and hydrochlorothiazide as first-line therapies for hypertension in real-world practice., Design, Setting, and Participants: This is a Large-Scale Evidence Generation and Evaluation in a Network of Databases (LEGEND) observational comparative cohort study with large-scale propensity score stratification and negative-control and synthetic positive-control calibration on databases spanning January 2001 through December 2018. Outpatient and inpatient care episodes of first-time users of antihypertensive monotherapy in the United States based on 2 administrative claims databases and 1 collection of electronic health records were analyzed. Analysis began June 2018., Exposures: Chlorthalidone and hydrochlorothiazide., Main Outcomes and Measures: The primary outcomes were acute myocardial infarction, hospitalization for heart failure, ischemic or hemorrhagic stroke, and a composite cardiovascular disease outcome including the first 3 outcomes and sudden cardiac death. Fifty-one safety outcomes were measured., Results: Of 730 225 individuals (mean [SD] age, 51.5 [13.3] years; 450 100 women [61.6%]), 36 918 were dispensed or prescribed chlorthalidone and had 149 composite outcome events, and 693 337 were dispensed or prescribed hydrochlorothiazide and had 3089 composite outcome events. No significant difference was found in the associated risk of myocardial infarction, hospitalized heart failure, or stroke, with a calibrated hazard ratio for the composite cardiovascular outcome of 1.00 for chlorthalidone compared with hydrochlorothiazide (95% CI, 0.85-1.17). Chlorthalidone was associated with a significantly higher risk of hypokalemia (hazard ratio [HR], 2.72; 95% CI, 2.38-3.12), hyponatremia (HR, 1.31; 95% CI, 1.16-1.47), acute renal failure (HR, 1.37; 95% CI, 1.15-1.63), chronic kidney disease (HR, 1.24; 95% CI, 1.09-1.42), and type 2 diabetes mellitus (HR, 1.21; 95% CI, 1.12-1.30). Chlorthalidone was associated with a significantly lower risk of diagnosed abnormal weight gain (HR, 0.73; 95% CI, 0.61-0.86)., Conclusions and Relevance: This study found that chlorthalidone use was not associated with significant cardiovascular benefits when compared with hydrochlorothiazide, while its use was associated with greater risk of renal and electrolyte abnormalities. These findings do not support current recommendations to prefer chlorthalidone vs hydrochlorothiazide for hypertension treatment in first-time users was found. We used advanced methods, sensitivity analyses, and diagnostics, but given the possibility of residual confounding and the limited length of observation periods, further study is warranted.

Widespread polybrominated biphenyls (PBBs) contamination occurred in Michigan from 1973 to 1974, when PBBs were accidentally substituted for a nutritional supplement in livestock feed. People who lived in the state were exposed to PBBs via several routes including ingestion, inhalation and skin absorption. PBBs sequestered in lipid-rich matrices such as adipose tissue, are slowly eliminated after entering the human body, and can also be transferred from a mother to her offspring through the placenta and breastfeeding. Due to the long biological half-lives of PBBs, as well as concerns from the exposed community, biomonitoring measurements were conducted from 2012 to 2015. Because of their similar structures, serum PBBs, polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) were all measured 40 years after the PBB contamination incident (N = 862). The serum PBB-153 levels among the original highly-exposed groups (i.e., chemical workers, the family of chemical workers, and individuals who lived on or received food from the contaminated farms) remains significantly higher than other Michigan residents. Several predictors such as sampling age, sex, and smoking status were significantly associated with the serum levels of some persistent organic pollutants (POPs). Higher average values and also wider ranges of serum POP levels were found in this study compared to the National Health and Nutrition Examination Survey (NHANES), with the most substantial difference in serum PBB-153. This was true for all groups of Michigan residents including those who were not part of the above-described highly-exposed groups. Moreover, the people born after the contamination incident began also have higher serum PBB-153 levels when compared with more recent NHANES data (2010-2014), which suggests potential intergenerational exposure and/or continued environmental exposure following the contamination period., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Some patients who are given opioids for pain could develop opioid use disorder. If it was possible to identify patients who are at a higher risk of opioid use disorder, then clinicians could spend more time educating these patients about the risks. We develop and validate a model to predict a person's future risk of opioid use disorder at the point before being dispensed their first opioid., Methods: A cohort study patient-level prediction using four US claims databases with target populations ranging between 343,552 and 384,424 patients. The outcome was recorded diagnosis of opioid abuse, dependency or unspecified drug abuse as a proxy for opioid use disorder from 1 day until 365 days after the first opioid is dispensed. We trained a regularized logistic regression using candidate predictors consisting of demographics and any conditions, drugs, procedures or visits prior to the first opioid. We then selected the top predictors and created a simple 8 variable score model., Results: We estimated the percentage of new users of opioids with reported opioid use disorder within a year to range between 0.04%-0.26% across US claims data. We developed an 8 variable Calculator of Risk for Opioid Use Disorder (CROUD) score, derived from the prediction models to stratify patients into higher and lower risk groups. The 8 baseline variables were age 15-29, medical history of substance abuse, mood disorder, anxiety disorder, low back pain, renal impairment, painful neuropathy and recent ER visit. 1.8% of people were in the high risk group for opioid use disorder and had a score > = 23 with the model obtaining a sensitivity of 13%, specificity of 98% and PPV of 1.14% for predicting opioid use disorder., Conclusions: CROUD could be used by clinicians to obtain personalized risk scores. CROUD could be used to further educate those at higher risk and to personalize new opioid dispensing guidelines such as urine testing. Due to the high false positive rate, it should not be used for contraindication or to restrict utilization., Competing Interests: All authors are employees of and receive salary from Janssen Research & Development, an affiliate of Janssen Pharmaceuticals Inc., which markets several analgesic drug products including opioids. This does not alter our adherence to PLOS ONE policies on sharing data and materials

Objective: To evaluate the relative risk of cervical neoplasms among copper intrauterine device (Cu IUD) and levonorgestrel-releasing intrauterine system (LNG-IUS) users., Methods: We performed a retrospective cohort analysis of 10,674 patients who received IUDs at Columbia University Medical Center. Our data were transformed to a common data model and are part of the Observational Health Data Sciences and Informatics network. The cohort patients and outcomes were identified by a combination of procedure codes, condition codes, and medication exposures in billing and claims data. We adjusted for confounding with propensity score stratification and propensity score 1:1 matching., Results: Before propensity score adjustment, the Cu IUD cohort included 8,274 patients and the LNG-IUS cohort included 2,400 patients. The median age for both cohorts was 29 years at IUD placement. More than 95% of the LNG-IUS cohort used a device with 52 mg LNG. Before propensity score adjustment, we identified 114 cervical neoplasm outcomes. Seventy-seven (0.9%) cervical neoplasms were in the Cu IUD cohort and 37 (1.5%) were in the LNG-IUS cohort. The propensity score matching analysis identified 7,114 Cu IUD and 2,174 LNG-IUS users, with covariate balance achieved over 16,827 covariates. The diagnosis of high-grade cervical neoplasia was 0.7% in the Cu IUD cohort and 1.8% in the LNG-IUS cohort (2.4 [95% CI 1.5-4.0] cases/1,000 person-years and 5.2 [95% CI 3.7-7.1] cases/1,000 person-years, respectively). The relative risk of high-grade cervical neoplasms among Cu IUD users was 0.38 (95% CI 0.16-0.78, P<.02) compared with LNG-IUS users. By inspection, the Kaplan-Meier curves for each cohort diverged over time., Conclusion: Copper IUD users have a lower risk of high-grade cervical neoplasms compared with LNG-IUS users. The relative risk of cervical neoplasms of LNG-IUS users compared with the general population is unknown.

Background and Purpose: Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke., Methods: We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia., Results: In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78., Conclusions: A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke., Competing Interests: Dr. Reps, Dr. Ryan, Ms. Voss and Dr. Rao are employees of Janssen Research and Development. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Healthcare professionals increasingly rely on observational healthcare data, such as administrative claims and electronic health records, to estimate the causal effects of interventions. However, limited prior studies raise concerns about the real-world performance of the statistical and epidemiological methods that are used. We present the "OHDSI Methods Benchmark" that aims to evaluate the performance of effect estimation methods on real data. The benchmark comprises a gold standard, a set of metrics, and a set of open source software tools. The gold standard is a collection of real negative controls (drug-outcome pairs where no causal effect appears to exist) and synthetic positive controls (drug-outcome pairs that augment negative controls with simulated causal effects). We apply the benchmark using four large healthcare databases to evaluate methods commonly used in practice: the new-user cohort, self-controlled cohort, case-control, case-crossover, and self-controlled case series designs. The results confirm the concerns about these methods, showing that for most methods the operating characteristics deviate considerably from nominal levels. For example, in most contexts, only half of the 95% confidence intervals we calculated contain the corresponding true effect size. We previously developed an "empirical calibration" procedure to restore these characteristics and we also evaluate this procedure. While no one method dominates, self-controlled methods such as the empirically calibrated self-controlled case series perform well across a wide range of scenarios.

Background and Objectives: 2018 ESC/ESH Hypertension guideline recommends 2-drug combination as initial anti-hypertensive therapy. However, real-world evidence for effectiveness of recommended regimens remains limited. We aimed to compare the effectiveness of first-line anti-hypertensive treatment combining 2 out of the following classes: angiotensin-converting enzyme (ACE) inhibitors/angiotensin-receptor blocker (A), calcium channel blocker (C), and thiazide-type diuretics (D)., Methods: Treatment-naïve hypertensive adults without cardiovascular disease (CVD) who initiated dual anti-hypertensive medications were identified in 5 databases from US and Korea. The patients were matched for each comparison set by large-scale propensity score matching. Primary endpoint was all-cause mortality. Myocardial infarction, heart failure, stroke, and major adverse cardiac and cerebrovascular events as a composite outcome comprised the secondary measure., Results: A total of 987,983 patients met the eligibility criteria. After matching, 222,686, 32,344, and 38,513 patients were allocated to A+C vs. A+D, C+D vs. A+C, and C+D vs. A+D comparison, respectively. There was no significant difference in the mortality during total of 1,806,077 person-years: A+C vs. A+D (hazard ratio [HR], 1.08; 95% confidence interval [CI], 0.97-1.20; p=0.127), C+D vs. A+C (HR, 0.93; 95% CI, 0.87-1.01; p=0.067), and C+D vs. A+D (HR, 1.18; 95% CI, 0.95-1.47; p=0.104). A+C was associated with a slightly higher risk of heart failure (HR, 1.09; 95% CI, 1.01-1.18; p=0.040) and stroke (HR, 1.08; 95% CI, 1.01-1.17; p=0.040) than A+D., Conclusions: There was no significant difference in mortality among A+C, A+D, and C+D combination treatment in patients without previous CVD. This finding was consistent across multi-national heterogeneous cohorts in real-world practice., Competing Interests: The authors declare the following disclosures: Mr. Swerdel, Dr. Ryan, and Dr. Schuemie are employees of Janssen Research & Development. The rest of the authors declare no conflict of interest., (Copyright © 2020. The Korean Society of Cardiology.)

Background: Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database., Methods: Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts., Results: In the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79)., Conclusions: A prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers., Competing Interests: All authors are employees of Janssen Research and Development, a unit of Johnson and Johnson family of companies. The work on this study was part of their employment. All authors hold pension rights from the company and own stock options. The authors would like to declare the following patents associated with this research: Johnson & Johnson holds marketing authorizations for REMICADE™ (infliximab) and SIMPONI™ (golimumab). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Purpose: To compare the incidence of diabetic ketoacidosis (DKA) among patients with type 2 diabetes mellitus (T2DM) who were new users of sodium glucose co-transporter 2 inhibitors (SGLT2i) versus other classes of antihyperglycemic agents (AHAs)., Methods: Patients were identified from four large US claims databases using broad (all T2DM patients) and narrow (intended to exclude patients with type 1 diabetes or secondary diabetes misclassified as T2DM) definitions of T2DM. New users of SGLT2i and seven groups of comparator AHAs were matched (1:1) on exposure propensity scores to adjust for imbalances in baseline covariates. Cox proportional hazards regression models, conditioned on propensity score-matched pairs, were used to estimate hazard ratios (HRs) of DKA for new users of SGLT2i versus other AHAs. When I 2 <40%, a combined HR across the four databases was estimated., Results: Using the broad definition of T2DM, new users of SGLT2i had an increased risk of DKA versus sulfonylureas (HR [95% CI]: 1.53 [1.31-1.79]), DPP-4i (1.28 [1.11-1.47]), GLP-1 receptor agonists (1.34 [1.12-1.60]), metformin (1.31 [1.11-1.54]), and insulinotropic AHAs (1.38 [1.15-1.66]). Using the narrow definition of T2DM, new users of SGLT2i had an increased risk of DKA versus sulfonylureas (1.43 [1.01-2.01]). New users of SGLT2i had a lower risk of DKA versus insulin and a similar risk as thiazolidinediones, regardless of T2DM definition., Conclusions: Increased risk of DKA was observed for new users of SGLT2i versus several non-SGLT2i AHAs when T2DM was defined broadly. When T2DM was defined narrowly to exclude possible misclassified patients, an increased risk of DKA with SGLT2i was observed compared with sulfonylureas., (© 2019 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.)

Background: Uncertainty remains about the optimal monotherapy for hypertension, with current guidelines recommending any primary agent among the first-line drug classes thiazide or thiazide-like diuretics, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, dihydropyridine calcium channel blockers, and non-dihydropyridine calcium channel blockers, in the absence of comorbid indications. Randomised trials have not further refined this choice., Methods: We developed a comprehensive framework for real-world evidence that enables comparative effectiveness and safety evaluation across many drugs and outcomes from observational data encompassing millions of patients, while minimising inherent bias. Using this framework, we did a systematic, large-scale study under a new-user cohort design to estimate the relative risks of three primary (acute myocardial infarction, hospitalisation for heart failure, and stroke) and six secondary effectiveness and 46 safety outcomes comparing all first-line classes across a global network of six administrative claims and three electronic health record databases. The framework addressed residual confounding, publication bias, and p-hacking using large-scale propensity adjustment, a large set of control outcomes, and full disclosure of hypotheses tested., Findings: Using 4·9 million patients, we generated 22 000 calibrated, propensity-score-adjusted hazard ratios (HRs) comparing all classes and outcomes across databases. Most estimates revealed no effectiveness differences between classes; however, thiazide or thiazide-like diuretics showed better primary effectiveness than angiotensin-converting enzyme inhibitors: acute myocardial infarction (HR 0·84, 95% CI 0·75-0·95), hospitalisation for heart failure (0·83, 0·74-0·95), and stroke (0·83, 0·74-0·95) risk while on initial treatment. Safety profiles also favoured thiazide or thiazide-like diuretics over angiotensin-converting enzyme inhibitors. The non-dihydropyridine calcium channel blockers were significantly inferior to the other four classes., Interpretation: This comprehensive framework introduces a new way of doing observational health-care science at scale. The approach supports equivalence between drug classes for initiating monotherapy for hypertension-in keeping with current guidelines, with the exception of thiazide or thiazide-like diuretics superiority to angiotensin-converting enzyme inhibitors and the inferiority of non-dihydropyridine calcium channel blockers., Funding: US National Science Foundation, US National Institutes of Health, Janssen Research & Development, IQVIA, South Korean Ministry of Health & Welfare, Australian National Health and Medical Research Council., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Introduction: US claims data contain medical data on large heterogeneous populations and are excellent sources for medical research. Some claims data do not contain complete death records, limiting their use for mortality or mortality-related studies. A model to predict whether a patient died at the end of the follow-up time (referred to as the end of observation) is needed to enable mortality-related studies., Objective: The objective of this study was to develop a patient-level model to predict whether the end of observation was due to death in US claims data., Methods: We used a claims dataset with full death records, Optum © De-Identified Clinformatics ® Data-Mart-Database-Date of Death mapped to the Observational Medical Outcome Partnership common data model, to develop a model that classifies the end of observations into death or non-death. A regularized logistic regression was trained using 88,514 predictors (recorded within the prior 365 or 30 days) and externally validated by applying the model to three US claims datasets., Results: Approximately 25 in 1000 end of observations in Optum are due to death. The Discriminating End of observation into Alive and Dead (DEAD) model obtained an area under the receiver operating characteristic curve of 0.986. When defining death as a predicted risk of > 0.5, only 2% of the end of observations were predicted to be due to death and the model obtained a sensitivity of 62% and a positive predictive value of 74.8%. The external validation showed the model was transportable, with area under the receiver operating characteristic curves ranging between 0.951 and 0.995 across the US claims databases., Conclusions: US claims data often lack complete death records. The DEAD model can be used to impute death at various sensitivity, specificity, or positive predictive values depending on the use of the model. The DEAD model can be readily applied to any observational healthcare database mapped to the Observational Medical Outcome Partnership common data model and is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/DeadModel .

The case-control design is widely used in retrospective database studies, often leading to spectacular findings. However, results of these studies often cannot be replicated, and the advantage of this design over others is questionable. To demonstrate the shortcomings of applications of this design, we replicate two published case-control studies. The first investigates isotretinoin and ulcerative colitis using a simple case-control design. The second focuses on dipeptidyl peptidase-4 inhibitors and acute pancreatitis, using a nested case-control design. We include large sets of negative control exposures (where the true odds ratio is believed to be 1) in both studies. Both replication studies produce effect size estimates consistent with the original studies, but also generate estimates for the negative control exposures showing substantial residual bias. In contrast, applying a self-controlled design to answer the same questions using the same data reveals far less bias. Although the case-control design in general is not at fault, its application in retrospective database studies, where all exposure and covariate data for the entire cohort are available, is unnecessary, as other alternatives such as cohort and self-controlled designs are available. Moreover, by focusing on cases and controls it opens the door to inappropriate comparisons between exposure groups, leading to confounding for which the design has few options to adjust for. We argue that this design should no longer be used in these types of data. At the very least, negative control exposures should be used to prove that the concerns raised here do not apply., (© 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Objectives: Smoking status is poorly record in US claims data. IBM MarketScan Commercial is a claims database that can be linked to an additional health risk assessment with self-reported smoking status for a subset of 1,966,174 patients. We investigate whether this subset could be used to learn a smoking status phenotype model generalizable to all US claims data that calculates the probability of being a current smoker., Methods: 251,643 (12.8%) had self-reported their smoking status as 'current smoker'. A regularized logistic regression model, the Current Risk of Smoking Status (CROSS), was trained using the subset of patients with self-reported smoking status. CROSS considered 53,027 candidate covariates including demographics and conditions/drugs/measurements/procedures/observations recorded in the prior 365 days, The CROSS phenotype model was validated across multiple other claims data., Results: The internal validation showed the CROSS model achieved an area under the receiver operating characteristic curve (AUC) of 0.76 and the calibration plots indicated it was well calibrated. The external validation across three US claims databases obtained AUCs ranging between 0.82 and 0.87 showing the model appears to be transportable across Claims data., Conclusion: CROSS predicts current smoking status based on the claims records in the prior year. CROSS can be readily implemented to any US insurance claims mapped to the OMOP common data model and will be a useful way to impute smoking status when conducting epidemiology studies where smoking is a known confounder but smoking status is not recorded. CROSS is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/SmokingModel., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a complete evaluation of PAs, i.e., determining sensitivity, specificity, and positive predictive value (PPV), is rarely performed. In this study, we propose a tool (PheValuator) to efficiently estimate a complete PA evaluation., Methods: We used 4 administrative claims datasets: OptumInsight's de-identified Clinformatics™ Datamart (Eden Prairie,MN); IBM MarketScan Multi-State Medicaid); IBM MarketScan Medicare Supplemental Beneficiaries; and IBM MarketScan Commercial Claims and Encounters from 2000 to 2017. Using PheValuator involves (1) creating a diagnostic predictive model for the phenotype, (2) applying the model to a large set of randomly selected subjects, and (3) comparing each subject's predicted probability for the phenotype to inclusion/exclusion in PAs. We used the predictions as a 'probabilistic gold standard' measure to classify positive/negative cases. We examined 4 phenotypes: myocardial infarction, cerebral infarction, chronic kidney disease, and atrial fibrillation. We examined several PAs for each phenotype including 1-time (1X) occurrence of the diagnosis code in the subject's record and 1-time occurrence of the diagnosis in an inpatient setting with the diagnosis code as the primary reason for admission (1X-IP-1stPos)., Results: Across phenotypes, the 1X PA showed the highest sensitivity/lowest PPV among all PAs. 1X-IP-1stPos yielded the highest PPV/lowest sensitivity. Specificity was very high across algorithms. We found similar results between algorithms across datasets., Conclusion: PheValuator appears to show promise as a tool to estimate PA performance characteristics., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Hepatocellular carcinoma (HCC) has emerged as a major cause of cancer deaths globally. The landscape of systemic therapy has recently changed, with six additional systemic agents either approved or awaiting approval for advanced stage HCC. While these agents have the potential to improve outcomes, a survival increase of 2-5 months remains poor and falls short of what has been achieved in many other solid tumor types. The roles of genomics, underlying cirrhosis, and optimal use of treatment strategies that include radiation, liver transplantation, and surgery remain unanswered. Here, we discuss new treatment opportunities, controversies, and future directions in managing HCC., (©2019 American Association for Cancer Research.)

Community gardens offer numerous benefits, but there are also potential risks from exposure to chemical contaminants in the soil. Through the lens of the Theory of Planned Behavior, this mixed methods study examined community gardeners' beliefs and intentions to conduct heavy metal soil testing. The qualitative component involved five focus groups of community garden leaders in Atlanta, Georgia. Qualitative analysis of the focus group data revealed that heavy metal soil contamination was not frequently identified as a common gardening hazard and several barriers limited soil testing in community gardens. The focus group results informed the development of a questionnaire that was administered to 500 community gardeners across the United States. Logistic regression analysis revealed that the soil testing intention was associated with attitude (aOR = 2.46, 95% CI: 1.34, 4.53), subjective norms (aOR = 3.39 95% CI: 2.07, 5.57), and perceived behavioral control (aOR = 1.81, 95% CI: 1.10, 2.99). Study findings have implications for interventions involving community garden risk mitigation, particularly gardens that engage children and vulnerable populations.