The Affordable Care Act's “community first choice” option: Effect on long-term care expenditures

Objective

To empirically assess the effect of adopting Affordable Care Act's Community First Choice (CFC) option on overall state home and community-based services (HCBS) expenditures as well as distribution of HCBS expenditures across different HCBS mechanisms. We also explore the heterogeneous effect of CFC across adopting states.

Data Source

We used data from the Medicaid Long Term Services and Support (LTSS) expenditure reports prepared by Truven Analytics and Mathematica for the Centers for Medicare & Medicaid Services from 2008–2018 for all 48 states and the District of Columbia.

Study Design

An event-study difference-in-differences model was used to estimate the effect of CFC on HCBS expenditures using Medicaid LTSS expenditure reports from 2008–2018. We also employ the synthetic control method to unmask heterogeneity across CFC adopting states using data from 2008–2018.

Data Collection/Extraction Methods

Not applicable.

Principal Findings

Overall, CFC was not associated with a change in HCBS expenditures per capita or HCBS expenditures as a proportion of LTSS expenditures. However, there appears to be an increase in HCBS expenditures among states that were institutionally-oriented prior to CFC adoption. Additionally, CFC adoption was associated with an overall decrease in expenditures in alternative HCBS mechanisms (Personal Care Services State Plan Option and 1915(c) waivers), suggesting potential substitution across overlapping programs.

Conclusion

Results indicate heterogeneity across states adopting CFC. More institutionally-oriented states appear to use CFC to expand HCBS. In contrast, more HCBS-oriented states appear to employ CFC to strategically restructure their overall portfolio and processes.     

Motor dysfunction in mild cognitive impairment as tested by kinematic analysis and transcranial magnetic stimulation

ObjectivePrevious studies have demonstrated voluntary movement alterations as well as motor cortex excitability and plasticity changes in patients with mild cognitive impairment (MCI). To investigate the pathophysiology of movement abnormalities in MCI, we tested possible relationships between movement abnormalities and primary motor cortex alterations in patients.MethodsFourteen amnestic MCI (aMCI) patients and 16 healthy controls were studied. Cognitive assessment was performed using clinical scales. Finger tapping was recorded by a motion analysis system. Transcranial magnetic stimulation was used to test the input/output curve of motor evoked potentials, intracortical inhibition, and short-latency afferent inhibition. Primary motor cortex plasticity was probed by theta burst stimulation. We investigated correlations between movement abnormalities, clinical scores, and cortical neurophysiological parameters.ResultsMCI patients showed less rhythmic movement but no other movement abnormalities. Cortical excitability measures were normal in patients, whereas plasticity was reduced. Movement rhythm abnormalities correlated with frontal dysfunction scores.ConclusionOur study in MCI patients demonstrated abnormal voluntary movement and plasticity changes, with no correlation between the two. Altered rhythm correlated with frontal dysfunction.SignificanceOur results contribute to the understanding of pathophysiological mechanisms of motor impairment in MCI.     

补中益气汤对于肝再生线粒体能量代谢的研究概况＊

肝再生的机制非常复杂，线粒体功能障碍所引起的能量供给不足是影响因素之一，但其机理亟待研究。严重肝损害时肝细胞ATP供应减少、线粒体能量代谢异常，导致肝再生受到抑制。补中益气汤为李东垣所创，其具补中益气、升阳举陷之功，有实验证实补中益气汤具有保护线粒体功能、增加线粒体能量代谢的作用，从而促进肝再生。本文综述补中益气汤总方与其中各类中药对线粒体能量代谢的保护作用，从而为促进肝再生提供新的治疗手段并对改善病人预后有重要意义。     

Classification of needle-EMG resting potentials by machine learning

Introduction: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of characteristic resting EMG signals and application of machine learning algorithms could help classify various EMG discharges. Methods: Data files of 6 classes of resting EMG signals were divided into 2-s segments. Extraction of characteristic features (384 and 4,367 features each) was used to classify the 6 types of discharges using machine learning algorithms. Results: Across 841 audio files, the best overall accuracy of 90.4% was observed for the smaller feature set. Among the feature classes, mel-frequency cepstral coefficients (MFCC)-related features were useful in correct classification. Conclusions: We showed that needle EMG resting signals were satisfactorily classifiable by the combination of feature extraction and machine learning, and this can be applied to clinical settings. Muscle Nerve 59 :224–228, 2019     

Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies

Aberrations of large‐scale brain networks are found in the majority of neurodegenerative disorders. The brain connectivity alterations underlying dementia with Lewy bodies (DLB) remain, however, still elusive, with contrasting results possibly due to the pathological and clinical heterogeneity characterizing this disorder. Here, we provide a molecular assessment of brain network alterations, based on cerebral metabolic measurements as proxies of synaptic activity and density, in a large cohort of DLB patients (N = 72). We applied a seed‐based interregional correlation analysis approach (p < .01, false discovery rate corrected) to evaluate large‐scale resting‐state networks' integrity and their interactions. We found both local and long‐distance metabolic connectivity alterations, affecting the posterior cortical networks, that is, primary visual and the posterior default mode network, as well as the limbic and attention networks, suggesting a widespread derangement of the brain connectome. Notably, patients with the lowest visual and attention cognitive scores showed the most severe connectivity derangement in regions of the primary visual network. In addition, network‐level alterations were differentially associated with the core clinical manifestations, namely, hallucinations with more severe metabolic dysfunction of the attention and visual networks, and rapid eye movement sleep behavior disorder with alterations of connectivity of attention and subcortical networks. These multiple network‐level vulnerabilities may modulate the core clinical and cognitive features of DLB and suggest that DLB should be considered as a complex multinetwork disorder.