Example code and spreadsheets to illustrate the calculations from the paper "Efficient analytical fragility function fitting using dynamic structural analysis."

Importance Recent US health care reforms incentivize improved population health outcomes and primary care functions. It remains unclear how much improving primary care physician supply can improve population health, independent of other health care and socioeconomic factors. Objectives To identify primary care physician supply changes across US counties from 2005-2015 and associations between such changes and population mortality. Design, Setting, and Participants This epidemiological study evaluated US population data and individual-level claims data linked to mortality from 2005 to 2015 against changes in primary care and specialist physician supply from 2005 to 2015. Data from 3142 US counties, 7144 primary care service areas, and 306 hospital referral regions were used to investigate the association of primary care physician supply with changes in life expectancy and cause-specific mortality after adjustment for health care, demographic, socioeconomic, and behavioral covariates. Analysis was performed from March to July 2018. Main Outcomes and Measures Age-standardized life expectancy, cause-specific mortality, and restricted mean survival time. Results Primary care physician supply increased from 196 014 physicians in 2005 to 204 419 in 2015. Owing to disproportionate losses of primary care physicians in some counties and population increases, the mean (SD) density of primary care physicians relative to population size decreased from 46.6 per 100 000 population (95% CI, 0.0-114.6 per 100 000 population) to 41.4 per 100 000 population (95% CI, 0.0-108.6 per 100 000 population), with greater losses in rural areas. In adjusted mixed-effects regressions, every 10 additional primary care physicians per 100 000 population was associated with a 51.5-day increase in life expectancy (95% CI, 29.5-73.5 days; 0.2% increase), whereas an increase in 10 specialist physicians per 100 000 population corresponded to a 19.2-day increase (95% CI, 7.0-31.3 days). A total of 10 additional primary care physicians per 100 000 population was associated with reduced cardiovascular, cancer, and respiratory mortality by 0.9% to 1.4%. Analyses at different geographic levels, using instrumental variable regressions, or at the individual level found similar benefits associated with primary care supply. Conclusions and Relevance Greater primary care physician supply was associated with lower mortality, but per capita supply decreased between 2005 and 2015. Programs to explicitly direct more resources to primary care physician supply may be important for population health.

Computer programs to model friction at high sliding velocities within water ice

These are the Matlab scripts used to produce the analysis and figures in the paper "Improving nonlinear and nonhydrostatic ocean lee wave drag parameterizations." Data files for use with these scripts can be found here: https://purl.stanford.edu/yc283rb5773 and https://purl.stanford.edu/zs656jt4944.

Model code, ancillary input files, and model output related to Martin et al., Global Biogeochemical Cycles in review

Background. Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. Methods. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Results. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30–0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant–$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. Limitations. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Conclusions. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine—evidence on efficacy, patient risks, and patient preferences—the modeling framework can provide personalized treatment decisions to improve patient health outcomes.

The rapid emergence of magnetic flux and free magnetic energy on the solar photosphere often indicates an increased probability of future flaring activity. For example, two active regions with the same total unsigned flux but different flux emergence rates may have different flaring activity. However, the nature of this change remains poorly understood. What patterns surround flux emergence and the temporal evolution of other magnetic variables governing eruptive activity? And how can we use these patterns to improve our predictions of solar flares? To answer this question, we took a three-part approach by creating time series data, featurizing these data, and using the resultant features in a machine learning model to determine which features govern eruptive activity. We found that more lag time (time series data length) increases model accuracy, indicating that important flare prediction data is encoded in the time series. We also found that certain individual variables, such as total unsigned flux and total free energy perform well on their own. This study represents a first step in time series analysis of flaring active regions.