Return on Investment Comparison of Three Payment Models for Chronic Care Management Under Medicare in a Northwestern Physician Hospital Organization

Chronic disease is the most prevalent and costly health condition. The coordination of care provided to those with multiple chronic conditions (MCCs) is suboptimal and fragmented. This population is among the highest utilizers of healthcare, and accounts for the majority of Medicare expenditures annually. Chronic care management (CCM) programs represent evidence-based initiatives shown to improve outcomes, reduce hospital and emergency department utilization, and reduce healthcare costs. Centers for Medicare and Medicaid Services (CMS) have provided various payment models for reimbursement of CCM services. Primary care practices have stated that inadequate reimbursement and confusing payment models are barriers to CCM implementation. This project was a return-on-investment (ROI) comparison of three different payment models for CCM services under Medicare for a northwest Michigan physician hospital organization. The Agency for Healthcare Research and Quality (AHRQ) ROI toolkit served as the implementation framework for the project. An estimation of the cost of the ongoing operation of the CCM program and projected revenue for 2017 was conducted. The results of the ROI analysis demonstrated a ROI of $1.39 for CPC+ track one practice participating in the comprehensive primary care plus (CPC+) initiative;$1.55 as a practice participating in CPC+ as a member of an accountable care organization having met the minimum savings rate, $1.34 if the minimum savings rate was not met; and$0.44 as a practice utilizing current procedural terminology billing for every dollar spent. This analysis provides the necessary knowledge on the cost-effectiveness of CCM management and reimbursement models under CMS at the practice level. This report discusses the background of MCCs, CCM, and the implementation, evaluation, outcomes, and limitations of the ROI analysis

Testing cold and hot cognitive control as moderators of a network of comorbid psychopathology symptoms in adolescence

Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents (n = 849). We implemented a method for assessing nonparametric moderation of psychopathology networks to evaluate differences in network structure across levels of intelligence and emotional control. Symptoms generally clustered by clinical diagnoses, but specific between-cluster bridge connections emerged. Internalizing symptoms demonstrated unique connections with aggression symptoms of interpersonal irritability, whereas externalizing symptoms showed more diffuse interconnections. Aggression symptoms identified as bridge nodes in the cross-sectional network were enriched for longitudinal associations with internalizing symptoms. Cross-domain connections did not significantly vary across intelligence but were weaker at lower emotional control. Our findings highlight transdiagnostic symptom relationships that may underlie co-occurrence of clinical diagnoses or higher-order factors of psychopathologyPsycholog

Complementation of un-16 and the development of a selectable marker for transformation of Neurospora crassa

Although nearly sixty temperature-sensitive lesions have been mapped in Neurospora crassa, most of their functions have not been identified. These loci are called unknown (un). As part of an effort to identify the open reading frame associated with one of these, we undertook to walk to un-16 using the complementation of temperature-sensitivity as a selection. Cosmids complementing un-16 were identified and the un-16 gene was subcloned. DNA sequence analysis of un-16 revealed that it encodes the highly conserved S9 protein of the 40S ribosomal subunit. This gene has proven useful as a selectable marker and may provide a simple mechanism for the controlled alteration of protein synthesis in N. crassa

Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction

Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates