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Neighborhood built environment characteristics may encourage physical activity, but previous literature on the topic has been critiqued for its reliance on cross-sectional data. In this issue of the Journal, Knuiman et al. (Am J Epidemiol. 2014;180(5):453-461) present longitudinal analyses of built environment characteristics as predictors of neighborhood transportation walking. We take this opportunity to comment on self-selection, exposure measurement, outcome form, analyses, and future directions. The Residential Environments (RESIDE) Study follows individuals as they relocate into new housing. The outcome, which is neighborhood transportation walking, has several important limitations with regards to public health relevance, dichotomization, and potential bias. Three estimation strategies were pursued: marginal modeling, random-effects modeling, and fixed-effects modeling. Knuiman et al. defend fixed-effects modeling as the one that most effectively controls for unmeasured time-invariant confounders, and it will do so as long as confounders have a constant effect over time. Fixed-effects modeling requires no distributional assumptions regarding the heterogeneity of subject-specific effects. Associations of time-varying neighborhood characteristics with walking are interpreted at the subject level for both fixed- and random-effects models. Cross-sectional data have set the stage for the next generation of neighborhood research, which should leverage longitudinal changes in both place and health behaviors. Careful interpretation is warranted as longitudinal data become available for analysis., (© The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

Objectives: To determine the biopsy sensitivity to melanoma of dermatologists in Germany and the impact of MelaFind® on their decisions to biopsy melanomas., Design: Randomized two-armed online reader study presenting case information, clinical/dermatoscopic images of pigmented skin lesions and MelaFind results (Arm 2)., Methods: Each participant was asked to review 130 pigmented skin lesions. Biopsy decisions of dermatologists without MelaFind versus MelaFind and dermatologists without MelaFind versus dermatologists with MelaFind were compared., Results: Dermatologists without MelaFind had average sensitivity to melanoma of 69.5 % and average specificity of 55.9 %. MelaFind had greater sensitivity than dermatologists alone (96.9 % vs. 69.5 %, one-sided p < 0.00001) and lower specificity (9.2 % vs. 55.9 %, one-sided p < 0.00001). Dermatologists with MelaFind had higher sensitivity than those without MelaFind (78 % vs. 69.5 %, one-sided p < 0.00001) and a lower specificity (45.8 % vs. 55.9 %, one-sided p < 0.00001). The number of dermatologists detecting over 90 % of melanomas increased from 3 of 101 without MelaFind to 22 of 101 with MelaFind (p = 0.00006) while specificity remained relatively equivalent (23 % vs. 21 %, p = 0.99)., Conclusions: The MelaFind information, when incorporated into the final biopsy decision, can improve biopsy sensitivity with modest effect on biopsy specificity., (© 2014 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd.)

Background: Previous data from a recently conducted prospective, single blind randomized clinical trial among community dwelling older patients with heart failure with a preserved ejection fraction (HFPEF) and anemia randomized to treatment with epoetin alfa (erythropoiesis-stimulating agents, ESA) vs. placebo did not demonstrate significant benefits of therapy regarding left ventricular (LV) structure, functional capacity, or quality of life (QOL). However, several patients randomized to the treatment arm were non-responders with a suboptimal increase in hemoglobin. All patients in the trial also received oral ferrous gluconate, which could have contributed to increases in hemoglobin observed in those receiving placebo. Accordingly, we performed an analysis separating patients into responders vs. non-responders in order to determine if measured improvement in anemia would have any effect on clinical endpoints., Methods: A total of 56 patients (age 77 ± 11 years, 68% female) were recruited who had anemia defined as a hemoglobin of ≤ 12 g/dL (average, 10.4 ± 1 g/dL) with HFPEF defined as having NHANES-CHF (National Health And Nutrition Examination Survey: Congestive Heart Failure) criteria score of ≥ 3 and an ejection fraction of > 40% (average EF = 63% ± 15%). Patients were randomly allocated to receive either ESA and ferrous gluconate or ferrous gluconate only. In this analysis, a responder was defined as a patient with an increase of 1 g/dL in the first 4 weeks of the trial., Results: Nineteen subjects were classified as responders compared to 33 non-responders. While the average hemoglobin increased significantly at the end of 6 months for responders (1.8 ± 0.3 vs. 0.8 ± 0.2 g/dL, P = 0.004), 50% of the subjects assigned to ESA were non-responders. Left ventricular function including ejection fraction (P = 0.32) and end diastolic volume (P = 0.59) was unchanged in responders compared to non-responders. Responders also showed no significant improvements in New York Heart Association (NYHA) class, Six Minute Walk Test (6 MWT) and peak VO2. Though QOL improved significantly within each group, there was no difference between the two., Conclusions: A significant hemoglobin response to anemia treatment with ESA and oral iron does not lead to differences in LV remodeling, functional status, or QOL. Additionally, a significant percent of older adults with HFPEF and anemia do not respond to ESA therapy. Given the results of this small trial, it appears as though using objective improvements in anemia as a marker in older adult subjects with HFPEF does not have significant clinical utility.

We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in less than one page in the Appendix. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects.

Functional data are data that can be represented by suitable functions, such as curves (potentially multi-dimensional) or surfaces. This paper gives an introduction to some basic but important techniques for the analysis of such data, and we apply the techniques to two datasets from biomedicine. One dataset is about white matter structures in the brain in multiple sclerosis patients; the other dataset is about three-dimensional vascular geometries collected for the study of cerebral aneurysms. The techniques described are smoothing, alignment, principal component analysis, and regression., (Copyright © 2013 John Wiley & Sons, Ltd.)

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian "scalar-on-image" regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients., (Copyright © 2013 Elsevier Inc. All rights reserved.)

We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study's primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.

Purpose: Researchers recommend a reorganization of the medical profession into larger groups with a multispecialty mix. We analyze whether there is evidence for the superiority of these models and if this organizational transformation is underway. DESIGN/METHODOLOGY APPROACH: We summarize the evidence on scale and scope economies in physician group practice, and then review the trends in physician group size and specialty mix to conduct survivorship tests of the most efficient models., Findings: The distribution of physician groups exhibits two interesting tails. In the lower tail, a large percentage of physicians continue to practice in small, physician-owned practices. In the upper tail, there is a small but rapidly growing percentage of large groups that have been organized primarily by non-physician owners., Research Limitations: While our analysis includes no original data, it does collate all known surveys of physician practice characteristics and group practice formation to provide a consistent picture of physician organization., Research Implications: Our review suggests that scale and scope economies in physician practice are limited. This may explain why most physicians have retained their small practices., Practical Implications: Larger, multispecialty groups have been primarily organized by non-physician owners in vertically integrated arrangements. There is little evidence supporting the efficiencies of such models and some concern they may pose anticompetitive threats., Originality/value: This is the first comprehensive review of the scale and scope economies of physician practice in nearly two decades. The research results do not appear to have changed much; nor has much changed in physician practice organization.

We describe and analyze a longitudinal diffusion tensor imaging (DTI) study relating changes in the microstructure of intracranial white matter tracts to cognitive disability in multiple sclerosis patients. In this application the scalar outcome and the functional exposure are measured longitudinally. This data structure is new and raises challenges that cannot be addressed with current methods and software. To analyze the data, we introduce a penalized functional regression model and inferential tools designed specifically for these emerging types of data. Our proposed model extends the Generalized Linear Mixed Model by adding functional predictors; this method is computationally feasible and is applicable when the functional predictors are measured densely, sparsely or with error. An online appendix compares two implementations, one likelihood-based and the other Bayesian, and provides the software used in simulations; the likelihood-based implementation is included as the lpfr() function in the R package refund available on CRAN.

Recent technological advances provide researchers with a way of gathering real-time information on an individual's movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called "movelets", and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage of our methods is that they allow identification of short activities, such as taking two or three steps and then stopping, as well as low frequency rare(compared with the whole time series) activities, such as sitting on a chair. Based on our results we provide simple and actionable recommendations for the design and implementation of large epidemiological studies that could collect accelerometry data for the purpose of predicting the time series of activities and connecting it to health outcomes.

We develop fast fitting methods for generalized functional linear models. The functional predictor is projected onto a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression; confidence intervals based on the mixed model framework are obtained. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. The approach can be implemented using standard mixed effects software and is computationally fast. The methodology is motivated by a study of white-matter demyelination via diffusion tensor imaging (DTI). The aim of this study is to analyze differences between various cerebral white-matter tract property measurements of multiple sclerosis (MS) patients and controls. While the statistical developments proposed here were motivated by the DTI study, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. An online appendix provides R implementations of all simulations.

Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a "penalized functional regression" procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a "tract abnormality score" for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6.4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation., (Published by Elsevier Inc.)

Under the Affordable Care Act, the new Center for Medicare and Medicaid Innovation will guide a number of experimental programs in health care payment and delivery. Among the most ambitious of the reform models is the accountable care organization (ACO), which will offer providers economic rewards if they can reduce Medicare's cost growth in their communities. However, the dismal history of provider-led attempts to manage costs suggests that this program is unlikely to accomplish its objectives. What's more, if ACOs foster more market concentration among providers, they have the potential to shift costs onto private insurers. This paper proposes a more flexible payment model for providers and private insurers that would divide health care services into three categories: long-term, low-intensity primary care; unscheduled care, including unscheduled emergency services; and major clinical interventions that usually involve hospitalization or organized outpatient care. Each category of care would be paid for differently, with each containing different elements of financial risk for the providers. Health plans would then be encouraged to provide logistical and analytic support to providers in managing health costs in these categories.

We introduce variational Bayes methods for fast approximate inference in functional regression analysis. Both the standard cross-sectional and the increasingly common longitudinal settings are treated. The methodology allows Bayesian functional regression analyses to be conducted without the computational overhead of Monte Carlo methods. Confidence intervals of the model parameters are obtained both using the approximate variational approach and nonparametric resampling of clusters. The latter approach is possible because our variational Bayes functional regression approach is computationally efficient. A simulation study indicates that variational Bayes is highly accurate in estimating the parameters of interest and in approximating the Markov chain Monte Carlo-sampled joint posterior distribution of the model parameters. The methods apply generally, but are motivated by a longitudinal neuroimaging study of multiple sclerosis patients. Code used in simulations is made available as a web-supplement.

We are concerned with the estimation of the exterior surface and interior summaries of tube-shaped anatomical structures. This interest is motivated by two distinct scientific goals, one dealing with the distribution of HIV microbicide in the colon and the other with measuring degradation in white-matter tracts in the brain. Our problem is posed as the estimation of the support of a distribution in three dimensions from a sample from that distribution, possibly measured with error. We propose a novel tube-fitting algorithm to construct such estimators. Further, we conduct a simulation study to aid in the choice of a key parameter of the algorithm, and we test our algorithm with validation study tailored to the motivating data sets. Finally, we apply the tube-fitting algorithm to a colon image produced by single photon emission computed tomography (SPECT) and to a white-matter tract image produced using diffusion tensor imaging (DTI).

When a physician who isn't a radiologist holds an ownership interest in an advanced imaging machine and refers patients for diagnostic procedures on that machine, this act of self-referral presents a conflict of interest. Numerous studies demonstrate greater use of high-tech imaging when physicians can financially benefit from such referrals. This overview summarizes the issues surrounding imaging self-referral and reviews the history of attempts to control it, as well as options for limiting its impact on spending. Recent payment reductions by Medicare limited the rewards for imaging self-referral, and the Affordable Care Act mandated disclosure of physicians' ownership interests. However, the rewards for imaging self-referral remain strong. Policy makers continue to search for the right mechanisms for containing the practice and for assuring that Medicare beneficiaries receive only medically necessary imaging studies, regardless of who owns or operates the equipment.

A key consideration in implementing the Patient Protection and Affordable Care Act of 2010 will be changing Medicare payments to providers to slow the growth in costs and spur improvements in health care delivery. In addition to the technical feasibility of new payment models, a crucial issue will be the capacity of the health care system to assume more economic risk. This article analyzes some of the major models under consideration and assesses how feasible their implementation would be.

Three big risks for hospitals under healthcare reform are: The inevitable deficit reduction initiatives beginning in FFY12. The estimated one-third increase in Medicaid enrollment. The dramatic increase in regulation of private health insurance.

HIPAA's administrative simplification provisions so far have not eased the burden of the healthcare industry's complex payment system. Some organizations have implemented claims management innovations through payer-provider collaborations, e.g., NEHEN, Availity, United Healthcare Online, and AthenaHealth's application suite. The Medicare program needs to change the payment system and make it easier to use electronic claims management.

CDHPs can stabilize growth in health costs, but the health plan-subscriber relationship should be more transparent. CFOs should ensure that increased cost exposure in CDHPs is paired with broad, deep disease management and employee assistance support. Hospitals should plan for the likelihood that, one way or another, consumers will be paying more of their healthcare bill.

U.S. hospitals have proved remarkably adept at altering their service offerings to incorporate new technologies. New technologies threatened to undercut hospitals' central role in health care delivery in the 1980s. An array of new technologies promise yet again to alter the boundaries of hospitals' franchise. These technologies will not only continue the shift away from acute, inpatient care that we have seen for the past thirty years but will also challenge hospitals to collaborate more effectively with physicians and technology developers. How hospitals and policymakers respond to these emerging technologies will help determine whether hospitals remain at the center of the U.S. health system.

To ensure that clinical IT implementation is accomplished properly, healthcare organization executives should involve care givers and gain medical staff support. CFOs need to stretch beyond their own comfort zones and become technically literate and involved in clinical IT issues.

Computerized patient records (CPRs) have reached a state of technical maturity that makes them an essential component of modern patient care. However, because uniform technical standards do not exist, CPRs constructed by different vendors do not convey clinical information easily from provider to provider. Moreover, unequal access to capital may mean a two-tier clinical information environment in the future. HIPAA, while important, did not anticipate the CPR revolution. New federal activism is required to assure not only interoperability of clinical data systems, but also that providers who lack capital and technical resources can make the needed digital conversion.

The demand for inpatient care is currently undergoing a surge. Trustees face a serious dilemma: How do they allocate limited capital between upgrading facilities and information technology? Each has a unique and significant impact on cost and quality of care.