Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0<ples1. The N most important coefficients in that expansion allow reconstruction with lscr2 error O(N1/2-1p/). It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of "random" linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel'fand n-widths of lscrp balls in high-dimensional Euclidean space in the case 0<ples1, and give a criterion identifying near- optimal subspaces for Gel'fand n-widths. We show that "most" subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces

Compressed sensing

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Health literacy has been associated with health-related knowledge and patient comprehension. This systematic review updates a 2004 review and found 96 eligible studies that suggest that low health ...

Low Health Literacy and Health Outcomes: An Updated Systematic Review

Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus.

https://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(13)70145-1.pdf

Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis.

Most surveys or data collection efforts in health services research have multiple goals. Frequently several researchers or institutions will jointly conduct a study, each with a different primary analytical interest. Even within a single researcher's data collection effort there may be several analysis priorities of differing importance with each suggesting a different study design. Multiple goal surveys are a sensible compromise in an era of limited research budgets.

Studies also may face design constraints. Beyond the obvious constraint of a limited budget, there are often practical constraints on portions of the study design. Limitations on the availability of the surveyed institutions or participants' time are typical examples. The typical sampling design in these multiple goal situations is to consider the optimal design (Cochran 1977), if known, for each of the goals and develop a hybrid of the individual optimal designs. This can be difficult if the optimal designs for the competing goals are very different. Real but nonstandard constraints can also complicate the design problem.

In this article we develop a simulation-based method to design samples that increase the efficiency of the study design. There are two main ideas underpinning the simulation sampling method:


When confronted by a study with competing goals and/or complicated structure many alternative designs should be considered. In some complicated study design problems there is no obvious optimal solution among the well-known sampling designs. Even if there is a known solution for one analysis goal, it may be quite different from the best approach for another goal. Many designs should be considered and evaluated, including designs that are nonstandard but that might capitalize on unique features of the specific problem.


There is no longer a compelling reason to restrict our attention to the easily evaluated designs from classical sampling texts. Almost any design that can be conceived can be evaluated via simulation. Sampling probabilities can be approximated even in cases where the multiplicity of ways that a unit could be sampled would stymie analytical calculation. Features of the sample that are difficult to calculate in closed form can be simulated even for complicated statistics.



This article introduces a sampling methodology for multistage sampling of health services users. The approach described here can be applied to any complex sampling problem where the levels of the structure are nested or can be redefined as nested.

Sampling Patients Within Physician Practices and Health Plans: Multistage Cluster Samples in Health Services Research

The Nonapproved Use of Medications

day. No AE was reported with rivastigmine treatment at 4.6mg /day dosing. At 9.5mg/day dosing, the reported AE were tiredness / sleepiness in 18 (15.5%) patients and skin rash in 3 (2.5%) patient. Noticeable cognitive deterioration was reported by caregivers in 66 (22.4%) patients during the first month of treatment. All such patients were treated with donepezil 10mg/day before switch. Conclusions: Next day crossover from donepezil (Aricept) to rivastigmine (Exelon) patch was well tolerated. A noticeable cognitive deterioration was reported by caregivers in 22% of patients whose treatment was switched from donepezil 10mg/day to rivastigmine patch 4.6mg/day.

P2-409: Results of a two-step crossover study of donepezil (Aricept) to rivastigmine patch (Exelon Patch) in Alzheimer's disease patients: A two-month clinical experience

C OMPARATIVE EFFECTIVENESS RESEARCH IS “the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care.” It is a term that has been used in different ways and with different methodologies including: (1) randomized, controlled trials to compare the efficacy of alternative approaches; (2) large simple trials, clinical databases, and claims analyses to determine the effectiveness of different interventions in real-world settings; and (3) various approaches to incorporating costs and/or benefits in costeffectiveness analyses. The focus has evolved from determining whether something works, to evaluating what works better, and finally to, “is it worth the expenditure for the value conferred?” The impetus to compare treatments arises not only from scientific questions of demonstrating relative performance but also from the seemingly inexorable rise of health care costs in the United States. In 2010, US national health expenditures are estimated to have been more than $2.6 trillion, approximately 17.6% of the country’s gross domestic product, and they are expected to grow to $4.6 trillion, or 19.8% of the GDP, by 2020. Thus, while the current approach to comparing treatments embodied in the Patient Protection and Affordable Care Act is in terms of clinical effectiveness and prohibits policy decisions based on costs of treatment, cost-effectiveness data will likely become more prominent as comparative effectiveness grows in importance to generate useful data to help patients, clinicians, and payors choose among a myriad of possible therapies.

Paul P. Lee

Papers

Sampling Patients Within Physician Practices and Health Plans: Multistage Cluster Samples in Health Services Research

The Nonapproved Use of Medications

P2-409: Results of a two-step crossover study of donepezil (Aricept) to rivastigmine patch (Exelon Patch) in Alzheimer's disease patients: A two-month clinical experience

Comparative effectiveness: insights on treatment options for open-angle glaucoma.

Assessing the Relationship Between Open-Angle Glaucoma and Alzheimer's Disease in the Health Retirement Survey