Leveraging Electronic Health Records and Administrative Datasets to Understand Social Determinants of Health: Opportunities and Challenges

Background Electronic health records (EHRs) are ubiquitous in modern health care systems and contain granular detail about an individual’s medical care. EHRs, however can be extremely limited in the amount of information about the patient’s socioeconomic status and other non-clinical factors that relate to social determinants of health (SDOH). As increasing evidence supports the role of socioeconomic and environmental factors having a greater impact on overall health than health care services, it is important to create novel data analysis methods that can improve our understanding of SDOH factors for each patient and the greater population a health system cares for. Main Aim To demonstrate the feasibility and value of linking EHRs with administrative datasets to produce SDOH insights and better support research, quality improvement initiatives and operational decision making. Methods/Approach We linked EHR’s at the Children’s Hospital of Philadelphia, between 2015-2019, with administrative datasets including the US Census and the American Community Survey Datasets for over 40,000 pediatric patients. This required the validation of geocoding addresses and spatial join techniques to provide clinical insight into the individual patient along with SDOH insights into their neighborhood environment. We then conducted several studies that combined individual clinical factors and neighborhood socioeconomic risk factors to improve our understanding and care pathways for patients. Results Linking EHR data and administrative datasets required novel methods with unique challenges. Challenges included conducting such analysis with concern for the protection of patient privacy, overcoming technical data linkage challenges, and understanding the limitations of administrative dataset granularity when compared to detailed granularity of individual medical records. Presentation of results to further ensure patient confidentiality and privacy was also critical to our analysis. Conclusion EHRs and administrative datasets can be successfully linked together to leverage the strength of both traditional clinical data and SDOH factors. While there can be technical and privacy related challenges, there are many benefits of using administrative datasets to provide unique multidimensional insights into socioeconomic and environmental impacts of health

Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo

We present a Markov-chain Monte-Carlo (MCMC) technique to study the source parameters of gravitational-wave signals from the inspirals of stellar-mass compact binaries detected with ground-based gravitational-wave detectors such as LIGO and Virgo, for the case where spin is present in the more massive compact object in the binary. We discuss aspects of the MCMC algorithm that allow us to sample the parameter space in an efficient way. We show sample runs that illustrate the possibilities of our MCMC code and the difficulties that we encounter.Comment: 10 pages, 2 figures, submitted to Classical and Quantum Gravit

Antibodies targeting epitopes on the cell-surface form of NS1 protect against Zika virus infection during pregnancy

Zika virus is an arthropod-transmitted flavivirus that can cause microcephaly and other fetal abnormalities during pregnancy. Here Wessel et al. develop antibodies against the Zika virus nonstructural protein 1 that protect non-pregnant and pregnant mice against infection, and define particular antibody epitopes and mechanisms underlying this protection

Customs Law

This article summarizes important developments in 2014 in customs law, including U.S. judicial decisions, trade, legislative, administrative, and executive developments, as well as Canadian and European legal developments

Carbapenem resistance in Enterobacterales bloodstream infections among children with cancer or post-haematopoietic stem cell transplant: a retrospective cohort study

Background Risk factors for carbapenem resistance in Enterobacterales bloodstream infections among children with cancer or post-HSCT have not been thoroughly explored. Methods All children with cancer or post-HSCT who developed Enterobacterales bloodstream infections in two cancer referral centres in major Colombian cities between 2012 and 2021 were retrospectively examined. When the infection episode occurred, carbapenem resistance mechanisms were evaluated according to the available methods. Data were divided in a training set (80%) and a test set (20%). Three internally validated carbapenem-resistant Enterobacterales (CRE) prediction models were created: a multivariate logistic regression model, and two data mining techniques. Model performances were evaluated by calculating the average of the AUC, sensitivity, specificity and predictive values. Results A total of 285 Enterobacterales bloodstream infection episodes (229 carbapenem susceptible and 56 carbapenem resistant) occurred [median (IQR) age, 9 (3.5–14) years; 57% male]. The risk of CRE was 2.1 times higher when the infection was caused by Klebsiella spp. and 5.8 times higher when a carbapenem had been used for ≥3 days in the previous month. A model including these two predictive variables had a discriminatory performance of 77% in predicting carbapenem resistance. The model had a specificity of 97% and a negative predictive value of 81%, with low sensitivity and positive predictive value. Conclusions Even in settings with high CRE prevalence, these two variables can help early identification of patients in whom CRE-active agents are unnecessary and highlight the importance of strengthening antibiotic stewardship strategies directed at preventing carbapenem overuse.Q1Q1Los factores de riesgo de resistencia a los carbapenémicos en las infecciones del torrente sanguíneo por Enterobacterales entre niños con cáncer o después de un TCMH no se han explorado a fondo. Métodos Se examinaron retrospectivamente todos los niños con cáncer o post-TCMH que desarrollaron infecciones del torrente sanguíneo por Enterobacterales en dos centros de referencia de cáncer en las principales ciudades de Colombia entre 2012 y 2021. Cuando ocurrió el episodio de infección, se evaluaron los mecanismos de resistencia a los carbapenémicos según los métodos disponibles. Los datos se dividieron en un conjunto de entrenamiento (80%) y un conjunto de prueba (20%). Se crearon tres modelos de predicción de Enterobacterales resistentes a carbapenémicos (CRE) validados internamente: un modelo de regresión logística multivariante y dos técnicas de minería de datos. El rendimiento del modelo se evaluó calculando el promedio del AUC, la sensibilidad, la especificidad y los valores predictivos. Resultados Se produjeron un total de 285 episodios de infección del torrente sanguíneo por Enterobacterales (229 susceptibles a carbapenémicos y 56 resistentes a carbapenémicos) [mediana de edad (RIQ), 9 (3,5 a 14) años; 57% hombres]. El riesgo de CRE fue 2,1 veces mayor cuando la infección fue causada por Klebsiella spp. y 5,8 veces mayor cuando se había utilizado un carbapenem durante ≥3 días en el mes anterior. Un modelo que incluía estas dos variables predictivas tuvo un rendimiento discriminatorio del 77% en la predicción de la resistencia a los carbapenémicos. El modelo tuvo una especificidad del 97% y un valor predictivo negativo del 81%, con baja sensibilidad y valor predictivo positivo. Conclusiones Incluso en entornos con una alta prevalencia de CRE, estas dos variables pueden ayudar a la identificación temprana de pacientes en quienes los agentes activos de CRE son innecesarios y resaltar la importancia de fortalecer las estrategias de administración de antibióticos dirigidas a prevenir el uso excesivo de carbapenémicos.N/AS

Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

Background: Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. Methods: We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. Findings: The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies. Interpretation: In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. Funding: Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses. © 2009 IOP Publishing Ltd

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ