Hospital and Physician Capacity Update

Offers an alternative view of healthcare costs by examining trends in hospital capacity and healthcare labor across regions. Outlines how effective management of healthcare capacity would enable affordable quality care that meets patient needs and wants

In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

Consensus clustering approach to group brain connectivity matrices

A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (i) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (ii) cluster the distance matrix for each node; (iii) build the consensus network from the corresponding partitions; (iv) extract groups of subjects by finding the communities of the consensus network thus obtained. Differently from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pre-training step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real data sets, show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix

Association Between Sedentary Time and Quality of Life From the Osteoarthritis Initiative: Who Might Benefit Most From Treatment?

Objective To investigate the relationship between sedentary behavior and quality-adjusted life years (QALYs) among participants in the Osteoarthritis Initiative. Design Longitudinal, observational design. Setting Osteoarthritis Initiative cohort. Participants Individuals (N=1794) from a prospective, multicenter longitudinal cohort were classified into quantile groups based on average daily sedentary time (most sedentary, quartile 1 [Q1] ≥11.6h; 10.7h≤ Q2 Interventions Not applicable. Main Outcome Measures Individual QALYs were estimated over 2 years from the area under the curve of health-related utility scores derived from the Medical Outcomes Study 12-Item Short-Form Health Survey versus time. The relationship between baseline sedentary behavior and median 2-year QALYs was estimated using quantile regression adjusted for socioeconomic factors and body mass index. Results Lower QALYs over 2 years were more frequently found among the most sedentary (Q1, median 1.59), and QALYs increased as time spent in baseline sedentary behavior decreased (median QALYs for Q2, 1.64; Q3, 1.65; Q4, 1.65). The relationship of sedentary time and median QALY change was only significant for the most sedentary Q1 group, where an additional hour of sedentary behavior significantly reduced QALYs by −.072 (95% confidence interval, −.121 to −.020). Conclusions Our findings suggest that individuals with the most extreme sedentary profiles may be vulnerable to additional losses of quality of life if they become more sedentary. Targeting these individuals to decrease sedentary behavior has the potential to be cost-effective