Robust topology optimization of three-dimensional photonic-crystal band-gap structures

We perform full 3D topology optimization (in which "every voxel" of the unit cell is a degree of freedom) of photonic-crystal structures in order to find optimal omnidirectional band gaps for various symmetry groups, including fcc (including diamond), bcc, and simple-cubic lattices. Even without imposing the constraints of any fabrication process, the resulting optimal gaps are only slightly larger than previous hand designs, suggesting that current photonic crystals are nearly optimal in this respect. However, optimization can discover new structures, e.g. a new fcc structure with the same symmetry but slightly larger gap than the well known inverse opal, which may offer new degrees of freedom to future fabrication technologies. Furthermore, our band-gap optimization is an illustration of a computational approach to 3D dispersion engineering which is applicable to many other problems in optics, based on a novel semidefinite-program formulation for nonconvex eigenvalue optimization combined with other techniques such as a simple approach to impose symmetry constraints. We also demonstrate a technique for \emph{robust} topology optimization, in which some uncertainty is included in each voxel and we optimize the worst-case gap, and we show that the resulting band gaps have increased robustness to systematic fabrication errors.Comment: 17 pages, 9 figures, submitted to Optics Expres

Development and validation of assessments of adolescent health literacy: a Rasch measurement model approach.

BACKGROUND Health literacy (HL) is implicated in improved health decision-making and health promotion, and reduced racial, ethnic, and socioeconomic health disparities. Three major areas of HL include functional, interactive, and critical HL. HL skills develop throughout the lifespan as individuals' psychosocial and cognitive capacities develop and as they accumulate experiences with navigating health systems. Though adolescence is marked by increased involvement in health decision-making, most HL studies and measures of HL have focused on adults. Both the adult and adolescent HL literature are also limited by the paucity of validated test-based measures for assessing HL. The existing test-based validated HL measures for adolescents were originally designed for adults. However, adolescents are at an earlier phase of developing their HL skills (e.g., fewer experiences navigating the health system) compared to adults and measures originally designed for adults may assume prior knowledge that adolescents may lack therein underestimating adolescents' HL. This study developed and validated test-based assessments of adolescents' functional, interactive, and critical HL. METHODS Items were generated in an iterative process: focus groups with adolescents informed item content, cognitive interviews with adolescents and expert consultation established content and face validity of the initial items, and items were revised or removed where indicated. High school students (n = 355) completed a measurement battery including the revised HL items. The items were evaluated and validated using Rasch measurement models. RESULTS The final 6-item functional, 10-item interactive, and 7-item critical HL assessments and their composite (23 items) fit their respective Rasch models. Item-level invariance was established for gender (male vs. female), age (12-15-year-olds vs. 16-18-year-olds), and ethnicity in all assessments. The assessments had good convergent validity with an established measure of functional HL and scores on the assessments were positively related to reading instructions before taking medicine and questioning the truthfulness of health information found online. CONCLUSIONS These assessments are the first test-based measures of adolescents' interactive and critical HL, the first test-based measure of functional HL designed for adolescents, and the first composite test-based assessment of all three major areas of HL. These assessments should be used to inform strategies for improving adolescents' HL, decision-making, and behaviors

Healthcare preferences among lesbians: a focus group analysis

OBJECTIVE: The healthcare needs of lesbians are not well understood. We sought to characterize lesbians\u27 experiences with, and preferences for, women\u27s healthcare. METHODS: We conducted three age-stratified focus groups (18-29, 30-50, and \u3e50 years) with a total of 22 participants using a semistructured interview guide to elicit lesbians\u27 experiences and preferences. We analyzed transcripts of these audiotaped sessions using the constant comparative method of grounded theory. Community-dwelling women who self-identified as lesbian and responded to advertisements were selected on first-come basis. RESULTS: Participants voiced experiences and preferences for healthcare that emerged into three themes: desired models of care, desired processes of care, and desired patient-provider relationship. Each theme was further developed into multiple subthemes. Within the subthemes we identified issues that were specific to lesbians and those that were general women\u27s health issues. Participants preferred, but did not always receive, care that is comprehensive in scope, person centered, nondiscriminatory, and inclusive of them as lesbians. CONCLUSIONS: Healthcare providers, institutions, and society should adopt an inviting, person-centered approach toward lesbians seeking healthcare, assure them access to healthcare information, and establish healthcare delivery systems that take all aspects of health into account

Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management

Translating research into practice: Protocol for a community-engaged, stepped wedge randomized trial to reduce disparities in breast cancer treatment through a regional patient navigation collaborative

BACKGROUND: Racial and socioeconomic disparities in breast cancer mortality persist. In Boston, MA, Black, Non-Hispanic women and Medicaid-insured individuals are 2-3 times more likely to have delays in treatment compared to White or privately insured women. While evidence-based care coordination strategies for reducing delays exist, they are not systematically implemented across healthcare settings. METHODS: Translating Research Into Practice (TRIP) utilizes community engaged research methods to address breast cancer care delivery disparities. Four Massachusetts Clinical and Translational Science Institute (CTSI) hubs collaborated with the Boston Breast Cancer Equity Coalition (The Coalition) to implement an evidence-based care coordination intervention for Boston residents at risk for delays in breast cancer care. The Coalition used a community-driven process to define the problem of care delivery disparities, identify the target population, and develop a rigorous pragmatic approach. We chose a cluster-randomized, stepped-wedge hybrid type I effectiveness-implementation study design. The intervention implements three evidence-based strategies: patient navigation services, a shared patient registry for use across academic medical centers, and a web-based social determinants of health platform to identify and address barriers to care. Primary clinical outcomes include time to first treatment and receipt of guideline-concordant treatment, which are captured through electronic health records abstraction. We will use mixed methods to collect the secondary implementation outcomes of acceptability, adoption/penetration, fidelity, sustainability and cost. CONCLUSION: TRIP utilizes an innovative community-driven research strategy, focused on interdisciplinary collaborations, to design and implement a translational science study that aims to more efficiently integrate proven health services interventions into clinical practice

O5‐08‐01: Trends In Racial And Ethnic Differences In Dementia Prevalence Rates And Disease Awareness

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153266/1/alzjjalz2019064876.pd

A Qualitative Analysis of Women's Satisfaction with Primary Care from a Panel of Focus Groups in the National Centers of Excellence in Women's Health

Health issues unique to women and differences in healthcare experiences have recently gained attention as health plans and systems seek to extend and improve health promotion and disease prevention in the population. Successful efforts focused on enhancing quality of care will require information from the patient's perspective on how to improve such services to best support women's attempts to lead healthy and productive lives. The National Centers of Excellence in Women's Health program (CoE), sponsored by the Office on Women's Health within the Department of Health and Human Services, is based on an integrated model uniting research, training, healthcare, and community education and outreach. To examine women's concept and definitions of healthcare quality, 18 focus groups comprising 137 women were conducted nationwide on experiences and attributes of healthcare that women value in primary care. Following the focus groups, a woman-focused healthcare satisfaction instrument was developed for the purpose of assessing and improving healthcare delivery. We describe the qualitative results of the focus group study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63264/1/15246090152563515.pd

RE: How the Coronavirus Disease-2019 May Improve Care: Rethinking Cervical Cancer Prevention

Feldman and Haas have written a timely piece on the potential to enhance cancer prevention and cancer care delivery in the COVID-19 era. Using cervical cancer prevention as a use case, the commentary describes clinical care provided via virtual platforms and in nontraditional settings, such as the patient’s home, as areas needing creative approaches to ensure care is provided safely and efficiently. As we consider factors that are relevant to delivering effective cancer prevention and cancer care post-COVID, we suggest that addressing social determinants of health, an often forgotten dimension of lived experience, should be prioritized as a strategy to enhance the equity of care provision. Social determinants of health, including food and housing insecurity have been shown to impact outcomes of patients with cancer, through a number of mechanisms including delays and incomplete care

Racial and Ethnic Differences in Knowledge About One’s Dementia Status

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156478/1/jgs16442.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156478/3/jgs16442_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156478/2/jgs16442-sup-0001-supinfo.pd

Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction

Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system. More generally, if designing explainable machine learning models is key to check the sanity and robustness of a decision process and improve their efficiency, it however remains a challenge for complex architectures, especially deep neural networks that are often deemed "black-box". In this paper, we propose a novel formulation of interpretable deep neural networks for the attribution task. Differently to popular post-hoc methods, our approach is interpretable by design. Using masked weights, hidden features can be deeply attributed, split into several input-restricted sub-networks and trained as a boosted mixture of experts. Experimental results on synthetic data and real-world recommendation tasks demonstrate that our method enables to build models achieving close predictive performances to their non-interpretable counterparts, while providing informative attribution interpretations.Comment: 14th ACM Conference on Recommender Systems (RecSys '20