The thermosphere-ionosphere regions are mainly controlled by the solar, but also by geomagnetic activity. In this case study, the Earth’s ionospheric response to the 25-26 August 2018 intense geomagnetic storm is investigated using the International GNSS System (IGS) Total Electron Content (TEC) observations. During this major storm, the minimum disturbance storm time (Dst) index reached -174 nT. We use observations and model simulations to analyse the ionospheric response during the initial phase and the main phase of the magnetic storm. A significant difference between storm day and quiet day TEC is observed. The O/N2 ratio observed from the GUVI instrument onboard the TIMED satellite is used to analyse the storm effect. The result shows a clear depletion of the O/N2 ratio in the high latitude region, and an enhancement in the low latitude region during the main phase of the storm. Furthermore, the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model simulations were used. The results suggest that the CTIPe model can capture the ionospheric variations during storms. / Die Regionen der Ionosphären und Thermosphäre werden hauptsächlich von der Sonne sowie auch von geomagnetische Aktivität beeinflusst. In dieser Fallstudie wurde die ionosphärische Reaktion der Erde auf den starken geomagnetischen Sturm vom 25./26. August 2018 unter Verwendung der Gesamtelektronengehaltsdaten (Total Electron Content, TEC) vom Internationalen GNSS Service untersucht. Während dieses großen Sturms wurde ein ”Disturbance Storm Time Index” Dst von -174 nT erreicht. Beobachtungen und Modellsimulationen wurden verwendet, um die ionosphärische Reaktion während der Anfangsphase und der Hauptphase des magnetischen Sturms zu untersuchen. Ein signifikanter Unterschied zwischen TEC während eines Sturmtages und eines ruhigen Tages wurde beobachtet. Das vom GUVI-Instrument an Bord des TIMED-Satelliten beobachtete O/N2 -Verhältnis wurde verwendet, um den Sturmeffekt weiter zu untersuchen. Das Ergebnis zeigt eine deutliche Abnahme/Zunahme des O/N2 Verhältnis in hohen/niedrigen Breiten während der Hauptphase des Sturms. Darüber hinaus wurde das Coupled Thermosphere Ionosphere Plasmasphere ectrodynamics (CTIPe) Modell verwendet. Die Ergebnisse legen nahe, dass das CTIPe-Modell die ionosphärischen Schwankungen während eines Sturms erfassen kann.

Background: Shifting the healthcare payment system from a volume-based to a value-based model has been a significant effort to improve the quality of care and reduce healthcare costs in the US. In 2018, Massachusetts Medicaid launched Accountable Care Organizations (ACOs) as part of the effort. Constructing, assessing, and risk-adjusting quality measures are integral parts of the reform process. Methods: Using data from the MassHealth Data Warehouse (2016-2019), we assessed the loss of community tenure (CTloss) as a potential quality measure for patients with bipolar, schizophrenia, or other psychotic disorders (BSP). We evaluated various statistical models for predicting CTloss using deviance, Akaike information criterion, Vuong test, squared correlation and observed vs. expected (O/E) ratios. We also used logistic regression to investigate risk factors that impacted medication nonadherence, another quality measure for patients with bipolar disorders (BD). Results: Mean CTloss was 12.1 (±31.0 SD) days in the study population; it varied greatly across ACOs. For risk adjustment modeling, we recommended the zero-inflated Poisson or doubly augmented beta model. The O/E ratio ranged from 0.4 to 1.2, suggesting variation in quality, after adjusting for differences in patient characteristics for which ACOs served as reflected in E. Almost half (47.7%) of BD patients were nonadherent to second-generation antipsychotics. Patient demographics, medical and mental comorbidities, receiving institutional services like those from the Department of Mental Health, homelessness, and neighborhood socioeconomic stress impacted medication nonadherence. Conclusions: Valid quality measures are essential to value-based payment. Heterogeneity implies the need for risk adjustment. The search for a model type is driven by the non-standard distribution of CTloss.