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A wide range of density functional methods and basis sets are available to derive the electronic structure and properties of molecules. Quantum mechanical calculations are too computationally intensive for routine simulation of molecules in the condensed phase, prompting the development of computationally efficient force fields based on quantum mechanical data. Parametrizing general force fields, which cover a vast chemical space, necessitates the generation of sizable quantum mechanical data sets with optimized geometries and torsion scans. To achieve this efficiently, choosing a quantum mechanical method that balances computational cost and accuracy is crucial. In this study, we seek to assess the accuracy of quantum mechanical theory for specific properties such as conformer energies and torsion energetics. To comprehensively evaluate various methods, we focus on a representative set of 59 diverse small molecules, comparing approximately 25 combinations of functional and basis sets against the reference level coupled cluster calculations at the complete basis set limit.

Understanding and manipulating emergent phases, which are themes at the forefront of quantum-materials research, rely on identifying their underlying symmetries. This general principle has been particularly prominent in materials with coupled electronic and magnetic degrees of freedom, in which magnetic order influences the electronic band structure and can lead to exotic topological effects. However, identifying symmetry of a magnetically ordered phase can pose a challenge, particularly in the presence of small domains. Here we introduce a multimodal approach for determining magnetic structures, which combines symmetry-sensitive optical probes, scattering, and group-theoretical analysis. We apply it to EuIn2As2, a material that has received attention as a candidate axion insulator. While first-principles calculations predict this state on the assumption of a simple collinear antiferromagnetic structure, subsequent neutron-scattering measurements reveal a much more intricate magnetic ground state characterized by two coexisting magnetic wave vectors reached by successive thermal phase transitions. The proposed high- and low-temperature phases are a spin helix and a state with interpenetrating helical and Néel antiferromagnetic order termed a "broken helix,"respectively. Employing a multimodal approach, we identify the magnetic structure associated with these two phases of EuIn2As2. We find that the higher-temperature phase is characterized by a variation of the magnetic moment amplitude from layer to layer, with the moment vanishing entirely in every third Eu layer. The lower-temperature structure is similar to the broken helix, with one important difference: Because of local strain, the relative orientation of the magnetic structure and the lattice is not fixed. Consequently, the symmetry required to protect the axion phase is not generically protected in EuIn2As2, but we show that it can be restored if the magnetic structure is tuned with uniaxial strain. Finally, we present a spin Hamiltonian that identifies the spin interactions that account for the complex magnetic order in EuIn2As2. Our work highlights the importance of a multimodal approach in determining the symmetry of complex order parameters.

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

Abstract: During host infection, Toxoplasma gondii and related unicellular parasites move using gliding, which differs fundamentally from other known mechanisms of eukaryotic cell motility. Gliding is thought to be powered by a thin layer of flowing filamentous (F)-actin sandwiched between the plasma membrane and a myosin-covered inner membrane complex. How this surface actin layer drives the various gliding modes observed in experiments—helical, circular, twirling and patch, pendulum or rolling—is unclear. Here we suggest that F-actin flows arise through self-organization and develop a continuum model of emergent F-actin flow within the confines provided by Toxoplasma geometry. In the presence of F-actin turnover, our model predicts the emergence of a steady-state mode in which actin transport is largely directed rearward. Removing F-actin turnover leads to actin patches that recirculate up and down the cell, which we observe experimentally for drug-stabilized actin bundles in live Toxoplasma gondii parasites. These distinct self-organized actin states can account for observed gliding modes, illustrating how different forms of gliding motility can emerge as an intrinsic consequence of the self-organizing properties of F-actin flow in a confined geometry.

Objective In 2019, the EULAR/American College of Rheumatology developed classification criteria for systemic lupus erythematosus (SLE). A positive correlation between summary score at diagnosis and SLE disease activity at five years has been noted in adult patients with lupus, but little is known among the pediatric population. We evaluated the prognostic value of higher summary scores and number of extrarenal domains at diagnosis (low/moderate number [1–5] vs high number [6–9]) to renal outcomes after one year of treatment in pediatric patients with lupus nephritis (LN). Methods This retrospective, single‐center cohort study included 74 pediatric patients with LN. Published pediatric renal response definitions were used for our outcome measure (no, partial, and complete response). Descriptive statistics were reported, and an ordinal logistic regression estimated adjusted odds ratios (ORs) for renal response including 95% confidence intervals (CIs). Results Patients with high extrarenal domains had OR 1.47 (95% CI 0.55–2.91) of having a complete renal response compared to patients with low/moderate domains. Patients with a summary score

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

ImportanceCommunication about patients' goals and planned and potential treatment is central to advance care planning. Undertaking or confirming advance care plans is also essential to preoperative preparation, particularly among patients who are frail or will undergo high-risk surgery.ObjectiveTo evaluate the association between patient risk of hospitalization or death and goals-of-care conversations documented with a completed Life-Sustaining Treatment (LST) Decisions Initiative note among veterans undergoing surgery.Design, setting, and participantsThis retrospective cross-sectional study included 190 040 veterans who underwent operations between January 1, 2017, and February 28, 2020. Statistical analysis took place from November 1, 2021, to November 17, 2022.ExposurePatient risk of hospitalization or death, evaluated with a Care Assessment Need (CAN) score (range, 0-99, with a higher score representing a greater risk of hospitalization or death), dichotomized as less than 80 or 80 or more.Main outcomes and measuresPreoperative LST note completion (30 days before or on the day of surgery) or no LST note completion within the 30-day preoperative period prior to or on the day of the index operation.ResultsOf 190 040 veterans (90.8% men; mean [SD] age, 65.2 [11.9] years), 3.8% completed an LST note before surgery, and 96.2% did not complete an LST note. In the groups with and without LST note completion before surgery, most were aged between 65 and 84 years (62.1% vs 56.7%), male (94.3% vs 90.7%), and White (82.2% vs 78.3%). Compared with patients who completed an LST note before surgery, patients who did not complete an LST note before surgery tended to be female (9.3% vs 5.7%), Black (19.2% vs 15.7%), married (50.2% vs 46.5%), and in better health (Charlson Comorbidity Index score of 0, 25.9% vs 15.2%); to have a lower risk of hospitalization or death (CAN score

The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein-ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.390.77 to 0.420.280.59 kcal/mol and R2 correlation improved from 0.720.350.87 to 0.930.840.97).

Abstract Rare-earth pyrochlore iridates host two interlocking magnetic sublattices of corner-sharing tetrahedra and can harbour a unique combination of frustrated moments, exotic excitations and highly correlated electrons. They are also the first systems predicted to display both topological Weyl semimetal and axion insulator phases. We have measured the transport and magnetotransport properties of single-crystal Sm2Ir2O7 up to and beyond the pressure-induced quantum critical point for all-in-all-out (AIAO) Ir order at p c = 63 kbar previously identified by resonant X-ray scattering and close to which Weyl semimetallic behavior has been previously predicted. Our findings overturn the accepted expectation that the suppression of AIAO order should lead to metallic conduction persisting down to zero temperature. Instead, the resistivity-minimum temperature, which tracks the decrease in the AIAO ordering temperature for pressures up to 30 kbar, begins to increase under further application of pressure, pointing to the presence of a second as-yet unidentified mechanism leading to non-metallic behavior. The magnetotransport does track the suppression of Ir magnetism, however, with a strong hysteresis observed only within the AIAO phase boundary, similar to that found for Ho2Ir2O7 and attributed to plastic deformation of Ir domains. Around p c we find the emergence of a new type of electronic phase, characterized by a negative magnetoresistance with small hysteresis at the lowest temperatures, and hysteresis-free positive magnetoresistance above approximately 5 K. The temperature dependence of our low-temperature transport data are found to be best described by a model consistent with a Weyl semimetal across the entire pressure range.

Abstract In the rapidly expanding field of topological materials there is growing interest in systems whose topological electronic band features can be induced or controlled by magnetism. Magnetic Weyl semimetals, which contain linear band crossings near the Fermi level, are of particular interest owing to their exotic charge and spin transport properties. Up to now, the majority of magnetic Weyl semimetals have been realized in ferro- or ferrimagnetically ordered compounds, but a disadvantage of these materials for practical use is their stray magnetic field which limits the minimum size of devices. Here we show that Weyl nodes can be induced by a helical spin configuration, in which the magnetization is fully compensated. Using a combination of neutron diffraction and resonant elastic x-ray scattering, we find that below T N = 14.5 K the Eu spins in EuCuAs develop a planar helical structure which induces two quadratic Weyl nodes with Chern numbers C = ±2 at the A point in the Brillouin zone.

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle–mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To examine outpatient care fragmentation and its association with future hospitalization among patients at high risk for hospitalization. DATA SOURCES: Veterans Affairs (VA) and Medicare data. STUDY DESIGN: We conducted a longitudinal study, using logistic regression to examine how outpatient care fragmentation in FY14 (as measured by number of unique providers, Breslaus Usual Provider of Care (UPC), Bice-Boxermans Continuity of Care Index (COCI), and Modified Modified Continuity Index (MMCI)) was associated with all-cause hospitalizations and hospitalizations related to ambulatory care sensitive conditions (ACSC) in FY15. We also examined how fragmentation varied by patients age, gender, race, ethnicity, marital status, rural status, history of homelessness, number of chronic conditions, Medicare utilization, and mental health care utilization. DATA EXTRACTION METHODS: We extracted data for 130,704 VA patients ≥65 years old with a hospitalization risk ≥90th percentile and ≥ four outpatient visits in the baseline year. PRINCIPAL FINDINGS: The mean (SD) of FY14 outpatient visits was 13.2 (8.6). Fragmented care (more providers, less care with a usual provider, more dispersed care based on COCI) was more common among patients with more chronic conditions and those receiving mental health care. In adjusted models, most fragmentation measures were not associated with all-cause hospitalization, and patients with low levels of fragmentation (more concentrated care based on UPC, COCI, and MMCI) had a higher likelihood of an ACSC-related hospitalization (AOR, 95% CI = 1.21 (1.09-1.35), 1.27 (1.14-1.42), and 1.28 (1.18-1.40), respectively). CONCLUSIONS: Contrary to expectations, outpatient care fragmentation was not associated with elevated all-cause hospitalization rates among VA patients in the top 10th percentile for risk of admission; in fact, fragmented care was linked to lower rates of hospitalization for ACSCs. In integrated settings such as the VA, multiple providers, and dispersed care might offer access to timely or specialized care that offsets risks of fragmentation, particularly for conditions that are sensitive to ambulatory care.

ABSTRACTIntracellular infectious agents, like the malaria parasite, Plasmodium falciparum, face the daunting challenge of how to invade a host cell. This problem may be even harder when the host cell in question is the enucleated red blood cell, which lacks the host machinery co-opted by many pathogens for internalization. Evolution has provided P. falciparum and related single-celled parasites within the phylum Apicomplexa with a collection of organelles at their apical end that mediate invasion. This apical complex includes at least two sets of secretory organelles, micronemes and rhoptries, and several structural features like apical rings and a putative pore through which proteins may be introduced into the host cell during invasion. We perform cryogenic electron tomography (cryo-ET) equipped with Volta Phase Plate on isolated and vitrified merozoites to visualize the apical machinery. Through tomographic reconstruction of cellular compartments, we see new details of known structures like the rhoptry tip interacting directly with a rosette resembling the recently described rhoptry secretory apparatus (RSA), or with an apical vesicle docked beneath the RSA. Subtomogram averaging reveals that the apical rings have a fixed number of repeating units, each of which is similar in overall size and shape to the units in the apical rings of tachyzoites of Toxoplasma gondii. Comparison of these polar rings in Plasmodium and Toxoplasma parasites also reveals them to have a structurally conserved assembly pattern. These results provide new insight into the essential and structurally conserved features of this remarkable machinery used by apicomplexan parasites to invade their respective host cells.IMPORTANCEMalaria is an infectious disease caused by parasites of the genus Plasmodium and is a leading cause of morbidity and mortality globally. Upon infection, Plasmodium parasites invade and replicate in red blood cells, where they are largely protected from the immune system. To enter host cells, the parasites employ a specialized apparatus at their anterior end. In this study, advanced imaging techniques like cryogenic electron tomography (cryo-ET) and Volta Phase Plate enable unprecedented visualization of whole Plasmodium falciparum merozoites, revealing previously unknown structural details of their invasion machinery. Key findings include new insights into the structural conservation of apical rings shared between Plasmodium and its apicomplexan cousin, Toxoplasma. These discoveries shed light on the essential and conserved elements of the invasion machinery used by these pathogens. Moreover, the research provides a foundation for understanding the molecular mechanisms underlying parasite-host interactions, potentially informing strategies for combating diseases caused by apicomplexan parasites.

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulations as a route to gaining a fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we retrain a select number of Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems and assess the performance of each of these combinations. We show that retraining against the mixture data improves the force field's ability to reproduce mixture properties, including solvation free energies, correcting some systematic errors that exist when training vdW interactions against properties of pure systems only.

Developing accurate classical force field representations of molecules is key to realizing the full potential of molecular simulations, both as a powerful route to gaining fundamental insights into a broad spectrum of chemical and biological phenomena and for predicting physicochemical and mechanical properties of substances. The Open Force Field Consortium is an industry-funded open science effort to this end, developing open-source tools for rapidly generating new high-quality small-molecule force fields. An integral aspect of this is the parameterization and assessment of force fields against high-quality, condensed-phase physical property data, curated from open data sources such as the NIST ThermoML Archive, alongside quantum chemical data. The quantity of such experimental data in open data archives alone would require an onerous amount of human and computational resources to both curate and estimate manually, especially when estimations must be obtained for numerous sets of force field parameters. Here, we present an entirely automated, highly scalable framework for evaluating physical properties and their gradients in terms of force field parameters. It is written as a modular and extensible Python framework, which employs an intelligent multiscale estimation approach that allows for the automated estimation of properties from simulation and cached simulation data, and a pluggable API for estimation of new properties. In this study, we demonstrate the utility of the framework by benchmarking the OpenFF 1.0.0 small-molecule force field and GAFF 1.8 and GAFF 2.1 force fields against a test set of binary density and enthalpy of mixing measurements curated using the framework utilities. Further, we demonstrate the framework's utility as part of force field optimization by using it alongside ForceBalance, a framework for systematic force field optimization, to retrain a set of nonbonded van der Waals parameters against a training set of density and enthalpy of vaporization measurements.

Abstract Background Body dissatisfaction (BD) is a growing concern in Latin America; reliable and culturally appropriate scales are necessary to support body image research in Spanish speaking Latin American countries. We sought to validate a Latin-American Spanish version of the Body Esteem Scale for Adolescents and Adults (BESAA; Mendelson et al. 2001). Methods The BESAA was translated, culturally adapted, and validated in a sample of adults in Colombia (N = 525, 65% women, M age 24.4, SD = 9.28). We assessed factor structure (using confirmatory factor analysis (CFA), exploratory factor analysis (EFA) and exploratory structural equation model (ESEM)), internal reliability (using Cronbach’s alpha and omega), validity (using the Body Appreciation Scale BAS and Sociocultural Attitudes Towards Appearance Questionnaire SATAQ), test–retest stability in a small subsample (N = 84, using Intraclass correlations ICC) and measurement invariance across gender. To evaluate the generalizability of the scale, we assessed reliability, validity, and factor structure in a second sample from rural Nicaragua (N = 102, 73% women, M age 22.2, SD = 4.72), and assessed measurement invariance across Nicaraguan and Colombian participants. Results The scale showed good internal reliability and validity in both samples, and there was evidence of adequate test–retest stability in the Colombian sample. EFA showed a three-factor structure with subscales we labelled ‘appearance-positive’, ‘appearance-negative’ and ‘weight’, that was confirmed using CFA and ESEM in the Colombian sample. Measurement invariance was confirmed across the Colombian and Nicaraguan samples, and across gender within the Colombian sample. Conclusion The Latin-American Spanish version of the BESAA (BESAA-LA) appears to be a psychometrically sound measure with good reliability, validity and invariance across gender and countries. These results support the use of this scale to measure body satisfaction/dissatisfaction in Latin American adult populations.

PurposeWe describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties.MethodsUsing an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance.ResultsWe enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata.ConclusionsOur stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

AbstractU.S. Veterans Affairs (VA) patients' multi-system use can create challenges for VA clinicians who are responsible for coordinating Veterans' use of non-VA care, including VA-purchased care ("Community Care") and Medicare.To examine the relationship between drive distance and time-key eligibility criteria for Community Care-and VA reliance (proportion of care received in VA versus Medicare and Community Care) among Veterans at high risk for hospitalization. We used prepolicy data to anticipate the impact of the 2014 Choice Act and 2018 Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION Act), which expanded access to Community Care.Cross-sectional analysis using fractional logistic regressions to examine the relationship between a Veteran's reliance on VA for outpatient primary, mental health, and other specialty care and their drive distance/time to a VA facility.Thirteen thousand seven hundred three Veterans over the age of 65 years enrolled in VA and fee-for-service Medicare in federal fiscal year 2014 who were in the top 10th percentile for hospitalization risk.Key explanatory variables were patients' drive distance to VA > 40 miles (Choice Act criteria) and drive time to VA ≥ 30 minutes for primary and mental health care and ≥60 minutes for specialty care (MISSION Act criteria).Veterans at high risk for hospitalization with drive distance eligibility had increased odds of an outpatient specialty care visit taking place in VA when compared to Veterans who did not meet Choice Act eligibility criteria (odds ratio = 1.10, 95% confidence interval 1.05-1.15). However, drive time eligibility (MISSION Act criteria) was associated with significantly lower odds of an outpatient specialty care visit taking place in VA (odds ratio = 0.69, 95% confidence interval 0.67, 0.71). Neither drive distance nor drive time were associated with reliance for outpatient primary care or mental health care.VA patients who are at high risk for hospitalization may continue to rely on VA for outpatient primary care and mental health care despite access to outside services, but may increase use of outpatient specialty care in the community in the MISSION era, increasing demand for multi-system care coordination.