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Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus. [ABSTRACT FROM AUTHOR]

Precise road segmentation is an essential part of many applications related to road information extraction from remote sensing data. The effect of color space on road detection has rarely been studied. In this paper, the effects of different color spaces of aerial images and multitask learning methods were experimented on road segmentation using three deep convolutional neural networks, UNet, DenseU-Net, and RoadVecNet. The color spaces included RGB, HSV, LAB, YCbCr, and YUV. The multitask learning methods adopted in this study involved utilizing multiple inputs, and multiple outputs. Multiple inputs were aerial images from the same area with different color spaces, and multiple outputs were road segmentation and road outline segmentation. As remote sensing data, National Land Survey of Finland's true orthophotos (from 2020), Massachusetts road imagery dataset, and Ottawa dataset were applied. Segmentation masks for National Land Survey of Finland's true orthophotos were extracted from Digiroad vectors with road width information. Road outline masks were generated from the segmentation masks. The studied neural networks were trained with the same data, learning rate, loss function, and optimizer for each color space, and pairs of color spaces. Multiple outputs were experimented with RGB color space. The comparative analysis assessed the performance of various neural networks across different color spaces using the F1-score metric. The experimental findings indicate that the choice of color space has little influence on the results of neural networks Deep learning methods can adapt to different color spaces well. In addition, the use of sharpening and edge enhancement augmentations had a slight effect on the results. [ABSTRACT FROM AUTHOR]

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality. • A traffic sensor location and path flow estimation joint optimization model is formulated. • A data-driven path flow measurement method based on Wasserstein metric is proposed. • A customized greedy algorithm by combining a search strategy is developed. • Numerical experiments are conducted based on the real-word transportation network. [ABSTRACT FROM AUTHOR]

Background: To address the need for interventions for families with parents with mental illness, the evidence-based intervention Let's Talk about Children (LTC) was adapted in the context of adult mental health services in the United States and reframed as the ParentingWell Practice Approach. This study focuses on the early implementation phase of the adapted practice in Massachusetts. Methods: As part of the adaptation and implementation process, practitioners from provider agencies serving adults with mental illness were invited to participate in the ParentingWell Learning Collaborative (PWLC), which included in-person learning collaborative sessions and follow-up virtual coaching sessions. This paper focuses on data obtained during and in response to the PWLC virtual coaching sessions, from 29 participants. Specific research questions included: (1) What themes emerged in coaching sessions related to practitioners' experiences during the early implementation of the ParentingWell Practice Approach (2) In what ways are coaching sessions helpful to the practitioners as they implement the ParentingWell Practice Approach? Coaching sessions were recorded, and transcribed, and the data were analyzed qualitatively to identify early implementation themes. Practitioners completed feedback surveys online (which included Likert scale items and open-ended questions) following virtual coaching sessions to evaluate the usefulness of coaching sessions. Results: Coaching sessions reflected the following themes related to practitioners' experiences during the early implementation of ParentingWell: (1) practitioners identify and share concrete approaches to supporting parents; (2) practitioners reflect on parents' needs related to support, advocacy, problemsolving, and parenting skills; (3) practitioners reflect on their own personal experiences; and (4) practitioners' recognize the importance of self-care strategies for themselves and for parents served. Practitioners indicated that coaching sessions were useful in supporting the implementation of a new practice. Conclusion: This study provides preliminary evidence for the benefits of coaching in the implementation of ParentingWell. Future research will explore the impact of ParentingWell on outcomes for parents and families served. [ABSTRACT FROM AUTHOR]

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively. [ABSTRACT FROM AUTHOR]

In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is of great interest. Such a suite generally consists of a large number of hurricane wind records, which may lead to highly computational cost for structural analysis. To reduce the computational demand while still preserving the accuracy of the uncertainty quantification process, this paper proposes a machine learning approach to select a representative subset of all collected hurricane wind records for a location. First, hurricane wind records, which are expressed as time series with information that includes both wind speed and direction, are collected from a synthetic hurricane catalog. The high dimensional hurricane wind records are then compressed into a set of low dimensional latent feature vectors using an artificial neural network, designated as an autoencoder. The latent feature vectors represent the important patterns of wind records such as duration, magnitude, and the changing of wind speeds and directions over time. The wind records are then clustered by applying the k -means algorithm on the latent features, and a subset of records is selected from each cluster. The wind records selected from each cluster are those whose latent feature points are closest to the centroid of all latent feature points in that cluster. In order to do regional analysis while taking into account that the hurricane wind records are site-specific, this paper suggests that a region can be discretized into a set of grids, with the proposed hurricane selection approach applied to each grid. This procedure is demonstrated using Massachusetts as a testbed. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932. [ABSTRACT FROM AUTHOR]

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Roadway area calculation is a novel problem in remote sensing and urban planning. This paper models this problem as a two-step problem, roadway extraction, and area calculation. Roadway extraction from satellite images is a problem that has been tackled many times before. This paper proposes a method using pixel resolution to calculate the area of the roads covered in satellite images. The proposed approach uses novel U-net and Resnet architectures called U-net++ and ResNeXt. The state-of-the-art model is combined with the proposed efficient post-processing approach to improve the overlap with ground truth labels. The performance of the proposed road extraction algorithm is evaluated on the Massachusetts dataset and it is shown that the proposed approach outperforms the existing solutions which use models from the U-net family. [ABSTRACT FROM AUTHOR]

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements. [ABSTRACT FROM AUTHOR]

Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware network (MECA-Net) for road extraction. MECA-Net adopts an encoder–decoder structure and contains two core modules. One is the multiscale feature encoding module, which aggregates multiscale road features to improve the recognition ability of slender roads. The other is the long-range context-aware module, which consists of the channel attention module and the strip pooling module, and is used to obtain sufficient long-range context information from the channel dimension and spatial dimension to alleviate road occlusion. Experimental results on the open DeepGlobe road dataset and Massachusetts road dataset indicate that the proposed MECA-Net outperforms the other eight mainstream networks, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The HEALing Communities Study (HCS) is a large-scale multisite study testing community engagement using coalition facilitation as an approach to addressing the worsening overdose crisis. Within community engagement, community-based participatory research (CBPR) principles guide researchers on best practices for working in partnership with communities, yet these principles have not been well researched in large, complex, multisite studies. This paper uses ethnographic methods to explore how coalitions operationalized CBPR principles during early coalition formation. Two coders independently analyzed 101 ethnographies from HCS coalition meetings in eight Massachusetts communities held between November 2019 and December 2020. Themes were developed through consensus between the coders, followed by group discussions among the authorship team. We found that mutual trust, shared goals, addressing power dynamics, meeting structure, and attending to the sociopolitical community context are critical elements that can either hinder or advance the use of CBPR principles in practice. These findings provide unique suggestions for future community-engaged multisite studies, and demonstrate the importance of research teams mitigating inherent power imbalances by acknowledging and creating spaces for community ownership. The findings also highlight the value of a community engagement facilitator (CEF) role, as well as strategies like transparency, uniting over shared interests, and bringing in a wide range of stakeholders when operationalizing CBPR principles. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we aimed to explore the oral and emotional health challenges experienced by a sample of refugees in Massachusetts across different stages of resettlement using a mixed methods approach. Methods: We collaborated with two Federally Qualified Health Centers to identify and recruit participants for either surveys (n = 69) or semi-structured interviews (n = 12). Data collection was conducted in 2018. We performed descriptive statistics using STATA 14, and analyzed the interviews using qualitative methods. Results: Overall, cost and lack of structure were the largest barriers identified for accessing dental care in participants' home and host countries. In the US, participants reported receiving state-provided public health insurance, but still experienced disrupted access to dental care due to coverage limitations. We identified several mental health risk factors that may affect participants' oral health, including trauma, depression, and sleeping problems. Despite these challenges, participants also identified areas of resilience and adaptability in both attitude and actions. Conclusions: The themes identified in our study suggest that refugees have attitudes, beliefs, and experiences that contribute to their perspectives on oral health care. While some of the reported barriers to access dental care were attitudinal, others were structural. Access to dental care in the US was reported to be structured and available, but with limited coverage issues. This paper underscores the oral and emotional health aspects of refugees for future considerations and planning of appropriate, affordable and cost-effective policies in the global health care systems. [ABSTRACT FROM AUTHOR]

Introduction: Academic libraries are facing two trends: the rising cost of journal subscriptions--the ongoing "serials crisis"--and the increasing availability of scholarly journal articles through free, open access channels. Methods: This study analyzed the bibliographies from 116 research projects created by students across disciplines at a small community college and classified citations to journal articles based on the simplest way students could have accessed them. Results: More than 60% of the scholarly journal articles that students used are available to them for free, and an additional 18% are available through state-sponsored database subscriptions available to any Massachusetts resident. Only 17% of the journal articles used were available to students solely through the library's database packages, and more than half of those articles were used by students in the associate degree of nursing (ADN) program. Discussion: These findings call into question the value of expensive subscription databases to community college libraries and suggest a pattern of diminishing return on investment and value. The trend toward cancellation of "big deal" journal packages at large institutions addresses similar concerns, although the dynamics are somewhat different at small institutions. Conclusion: The paper concludes by suggesting some steps that small academic libraries might take to adapt to this changing publishing environment, in the areas of collection development and information literacy instruction, in order to better serve students. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring, and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains—the first being coastal waters—with many obstacles, structures, and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, long wave infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2900 LWIR segmented images captured in coastal maritime environment over a period of 2 years. The images are labeled using instance segmentation and classified into seven categories—sky, water, obstacle, living obstacle, bridge, self, and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain, it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment. [ABSTRACT FROM AUTHOR]

The Markforged Metal X (MfMX) printing machine (Markforged Inc., Massachusetts, USA) is one of the latest introduced additive manufacturing (AM) devices. It is getting popular because of its safety, simplicity, and ability to utilize various types of powders/filaments for printing. Despite this, only a few papers have so far reported the various properties and performances of the components fabricated by the MfMX printer. In this study, the microstructure and mechanical properties of MfMX-fabricated 17-4 stainless steel (ss) in the as-printed and heat-treated conditions were investigated. XRD and microscopy analyses revealed a dominant martensitic microstructure with some retained austenite phase. The microstructure is generally characterized by patterned voids that were unfilled due to a lack of fusion between the adjacent filaments. Disregarding these defects (voids), the porosity of the dense region was less than 4%. Depending on the heat treatment conditions, the hardness and tensile strength were enhanced by 17–28% and 21–27%, respectively. However, the tensile strength analyzed in this work was low compared with some previous reports for L-PBF-fabricated 17-4 ss. In contrast, the hardness of the as-printed (331 ± 28 HV) and heat-treated samples under the H900 condition (417 ± 29 HV) were comparable with (and even better than) some reports in the literature, despite the low material density. The results generally indicated that the Markforged printer is a promising technology when the printing processes are fully developed and optimized. [ABSTRACT FROM AUTHOR]

Background: The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate-constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests.Methods: The primary outcome, the number of opioid overdose deaths, is count data assessed at the community level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the type I error rates and power for 3 tests: (1) Wald-type t-test with small-sample corrected empirical standard error estimates, (2) Wald-type z-test with model-based standard error estimates, and (3) permutation test with test statistics calculated by the difference in average residuals for the two groups.Results: Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved type I error rates if the constrained space was not too small. For all tests, the power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA).Conclusions: Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study.Trial Registration: ClinicalTrials.gov http://www.Clinicaltrials: gov ; Identifier: NCT04111939. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low building segmentation accuracy and blurred edges in high-resolution remote sensing images, an improved fully convolutional neural network is proposed based on the SegNet network. First, GELU, which performs well in deep learning tasks, is selected as the activation function to avoid neuron deactivation. Second, the improved residual bottleneck structure is used in the encoding network to extract more building features. Then, skip connections are used to fuse images The low-level and high-level semantic features are used to assist image reconstruction. Finally, an improved edge correction module is connected at the end of the decoding network to further correct the edge details of the building and improve the edge integrity of the building. Experiments are carried out on the Massachusetts building dataset, and the precision rate, recall rate, and F1 value reach 93.5%, 79.3%, and 81.9%, respectively, and the comprehensive evaluation index F1 value is improved by about 5% compared with the basic network. [ABSTRACT FROM AUTHOR]

Healthcare policy makers use wait-time metrics to encourage hospital managers to improve patient experience. In 2002, Massachusetts mandated that hospital managers develop processes to respond to boarding crises, which occur when emergency department (ED) patients experience long waits for inpatient beds. Performance improvement theory suggests that patients would be better served by preventing boarding crises rather than responding urgently after they occur. To empirically test this theory, we use data from a Massachusetts hospital that has two physician-based processes related to boarding and patient flow. First, to comply with the state mandate, the hospital developed processes to identify when the hospital is in a boarding crisis, a code yellow (CY), and subsequently request that physicians prioritize patient discharge (urgent response). Second, physicians can use predischarge orders, optional written communication about discharge barriers, to avoid discharge delays for patients approaching discharge (prevention response). Our data supports the existence of a trade-off between these two responses. Counter to our hypothesis, the state-mandated urgent response does not have any impact on length of stay (LOS). We also find that a CY has no impact on ED hourly occupancy, marginally decreases ED wait times, and increases boarding time. The prevention response is associated with a 26% reduction in LOS. Furthermore, we find that the urgent response reduces the likelihood of physicians' ability to use the prevention response by 27.3%. We conclude that the state policy has unintended negative consequences that stymie hospital efforts to create longer term improvement. This paper was accepted by Stefan Scholtes, healthcare management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4724. [ABSTRACT FROM AUTHOR]

Road segmentation is one of the essential tasks in remote sensing. Large-scale high-resolution remote sensing images originally have larger pixel sizes than natural images, while the existing models based on Transformer have the high computational cost of square complexity, leading to more extended model training and inference time. Inspired by the long text Transformer model, this paper proposes a novel hybrid attention mechanism to improve the inference speed of the model. By calculating several diagonals and random blocks of the attention matrix, hybrid attention achieves linear time complexity in the token sequence. Using the superposition of adjacent and random attention, hybrid attention introduces the inductive bias similar to convolutional neural networks (CNNs) and retains the ability to acquire long-distance dependence. In addition, the dense road segmentation result of remote sensing image still has the problem of insufficient continuity. However, multiscale feature representation is an effective means in the network based on CNNs. Inspired by this, we propose a multi-scale patch embedding module, which divides images by patches with different scales to obtain coarse-to-fine feature representations. Experiments on the Massachusetts dataset show that the proposed HA-RoadFormer could effectively preserve the integrity of the road segmentation results, achieving a higher Intersection over Union (IoU) 67.36% of road segmentation compared to other state-of-the-art (SOTA) methods. At the same time, the inference speed has also been greatly improved compared with other Transformer based models. [ABSTRACT FROM AUTHOR]

This paper examines the role of universal healthcare on patient choice and quality of care. Lack of health insurance is a leading contributor to emergency department (ED) visits from the uninsured. Using a natural experiment from Massachusetts, we follow previously uninsured individuals over time—both before and after the implementation of universal healthcare—to study the effects of insurance availability on their healthcare seeking behavior. We find that the availability of health insurance has a significant impact on the type of healthcare sought by the previously uninsured. Specifically, individuals are less likely to continue visiting the ED and instead more likely to choose hospital inpatient and outpatient services. We find that frequent fliers and patients with chronic conditions, who are both known to account for a substantial share of healthcare costs, exhibit heterogeneity in this behavior. Finally, we examine the effect of the policy on quality of care, as measured by short‐term ED revisits. Although the overall revisit rates remain unchanged, frequent ED users and chronic patients experience a decline in short‐term ED revisits. With key features of the Affordable Care Act being frequently criticized for repeal, understanding how these changes influence patient choice and quality of care is significantly policy relevant. [ABSTRACT FROM AUTHOR]

Background: Despite the importance of family and parent-focused practice, there has been a dearth of research on interventions for parents with mental illness. This paper describes the process and outcome of adapting an evidence-based intervention, Let's Talk about Children (LTC), in the context of adult mental health services in Massachusetts, United States. Methods: Specific objectives included: (1) to specify the core components, functions, and principles of LTC essential to adapting the intervention (i.e., program theory), (2) to consider contextual factors related to the new setting; (3) to pre-test the adapted materials with diverse practitioners; and (4) to compile the program model and materials (i.e., the practice profile) for use by adult mental health service providers in Massachusetts. The Adaptation Team included individuals with expertise in psychiatric rehabilitation and clinical care, policymaking, program development and research, and parents. Activities occurred between 2015–2019 and included: (1) consulting with experts to specify the core elements and theory behind the selected intervention (i.e., with the LTC purveyor and international experts); (2) consulting with key stakeholders for input regarding the Massachusetts target population and context to inform adaptations (i.e., individual and group key informant interview sessions); (3) pretesting the initial adapted materials (i.e., training and coaching sessions with adult mental health practitioners); and (4) using feedback to refine and compile the final intervention manual (i.e., the ParentingWell Practice Profile). Participants reflected diverse, oftentimes multiple roles and perspectives, including those of parents with mental illness, adult children, and family members. Results: ParentingWell is practitioner- and setting-agnostic, addresses parenting across the lifespan, fits into the routine workflow, and builds on practitioners' existing skills. Eight themes emerged, which were translated into four core elements (engage, explore, plan, access and advocate) consistent with Self-Determination Theory and four underlying principles (trauma-informed, strengths-based, family-focused, culturally sensitive) in keeping with the LTC model. The ParentingWell Practice Profile operationalizes each core element and addresses the underlying principles. Conclusion: ParentingWell makes talking about parenting and family experiences a routine part of the therapeutic conversation with adults with mental illness. Future research will test the adaptation, implementation, and impact of ParentingWell. [ABSTRACT FROM AUTHOR]

Hospital and healthcare administrators name high prescription drug costs as one of their largest problems. A significant body of research demonstrates that meals and honoraria from pharmaceutical firms to physicians leads to higher prescribing of expensive, brand name drugs, despite little difference in efficacy. Some administrators and scholars have advocated for mandatory disclosure of these payments in order to reduce this conflict of interest, but many practitioners believe disclosure has little effect on prescribing, and the empirical evidence is mixed. This paper uses a quasi-experiment of a 2009 payment disclosure policy in Massachusetts to estimate the causal impact of public disclosure on prescribing. The comprehensive data set includes all retail prescriptions for 262 drugs in nine drug classes written by 5,730 physicians in five states over 48 months. We show a significant postdisclosure reduction in brand name drug prescriptions by Massachusetts physicians, relative to control physicians in other states. These effects are driven by heavy prescribers of brand name drugs in the prepolicy period, particularly for drugs with large prepolicy sales forces. Effects are also detected before the first data were released, implying that the effects are not because patients or administrators responded to the disclosed payments. Instead, some physicians may have changed their payments and prescriptions behavior to avoid appearing biased. Taken in tandem with the many studies showing that pharmaceutical industry payments influence prescribing, this study suggests a strong role for mandatory public disclosure in reducing conflicts of interest in medicine and costly prescribing of brand name drugs. This paper was accepted by Stefan Scholtes, healthcare management. [ABSTRACT FROM AUTHOR]

While scholars have found that undergraduate involvement in research is beneficial, the lack of such experiences in the social sciences and humanities is glaring. This paper analyzes how an emphasis on community through cohort models impacts undergraduate student experience in research, taking from the Emerging Scholars Program, an interdisciplinary research program where cohorts of undergraduates are matched with faculty and attend meetings, workshops, and presentations together. We find that the cohort model created a robust community that fosters positive relationships that develop professional skills and emotional support, allowing students to collaborate and aim more toward their professional goals. The results from this study offer valuable insights into how universities can ensure that students have meaningful research experience beyond the classroom.

Land conservation efforts throughout the United States sustain ecological benefits while generating wealth in the housing market through capitalization of amenities. This paper estimates the benefits of conservation that are capitalized into proximate home values and quantifies how those benefits are distributed across demographic groups. Using detailed property and household-level data from Massachusetts, we estimate that new land conservation led to $62 million in new housing wealth equity. However, houses owned by low-income or Black or Hispanic households are less likely to be located near protected areas, and hence, these populations are less likely to benefit financially. Direct study of the distribution of this new wealth from capitalized conservation is highly unequal, with the richest quartile of households receiving 43%, White households receiving 91%, and the richest White households receiving 40%, which is nearly 140% more than would be expected under equal distribution. We extend our analysis using census data for the entire United States and observe parallel patterns. We estimate that recent land conservation generated $9.8 billion in wealth through the housing market and that wealthier and White households benefited disproportionately. These findings suggest regressive and racially disparate incidence of the wealth benefits of land conservation policy. [ABSTRACT FROM AUTHOR]

This article expands upon a session, titled "Implications of Pediatric Initiatives on CNS Drug Development for All Ages--2020 and Beyond," that was presented as part of a two-day meeting on pediatric drug development at the International Society for Central Nervous System (CNS) Clinical Trials and Methodology (ISCTM) Autumn Conference in Boston, Massachusetts, in October 2020. Speakers from various areas of pediatric drug development addressed a variety of implications of including children in drug development programs. The speakers wrote summaries of their talks, which are included here. The session's lead chair was Dr. Gahan Pandina, who wrote introductory and closing comments. Dr. Joseph Horrigan addressed the current landscape of pediatric development programs. Dr. Gahan Pandina addressed how the approach to research in pediatric populations affects the drug development process and vice versa. Dr. Alison Bateman-House discussed the ethical implications of research in the pediatric population. Dr. Luca Pani discussed some of the global regulatory issues and challenges concerning research in pediatric patients. Dr. Judith Kando served as a discussant and posed new questions about means of facilitating pediatric research. Finally, Dr. Gahan Pandina provided closing comments and tied together the presented issues. This paper should serve as an expert-informed reference to those interested and involved in CNS drug development programs that are aimed at children and/or required, through regulations, to include children as part of the approval process. [ABSTRACT FROM AUTHOR]

Background and aims: Nongenerative speech is the rote repetition of words or phrases heard from others or oneself. The most common manifestations of nongenerative speech are immediate and delayed echolalia, which are a well-attested clinical feature and a salient aspect of atypical language use in autism. However, there are no current estimates of the frequency of nongenerative speech, and the individual characteristics associated with nongenerative speech use in individuals across the autistic spectrum are poorly understood. In this study, we aim to measure and characterize spontaneous and nongenerative speech use in minimally verbal and verbally fluent autistic children and adolescents. Methods: Participants were 50 minimally verbal and 50 verbally fluent autistic individuals aged 6 to 21 years. Spontaneous and nongenerative speech samples were derived from SALT transcripts of ADOS-2 assessments. Participants' intelligible speech utterances were categorized as spontaneous or nongenerative. Spontaneous versus nongenerative utterances were compared between language subgroups on frequency of use and linguistic structure. Associations between nongenerative speech use and a series of individual characteristics (ADOS-2 subscale scores, nonverbal IQ, receptive vocabulary, and chronological age) were investigated over the whole sample and for each language subgroup independently. Results: Almost all participants produced some nongenerative speech. Minimally verbal individuals produced significantly more nongenerative than spontaneous utterances, and more nongenerative utterances compared to verbally fluent individuals. Verbally fluent individuals produced limited rates of nongenerative utterances, in comparison to their much higher rates of spontaneous utterances. Across the sample, nongenerative utterance rates were associated with nonverbal IQ and receptive vocabulary, but not separately for the two language subgroups. In verbally fluent individuals, only age was significantly inversely associated with nongenerative speech use such that older individuals produced fewer nongenerative utterances. In minimally verbal individuals, there were no associations between any of the individual characteristics and nongenerative speech use. In terms of linguistic structure, the lexical diversity of nongenerative and spontaneous utterances of both language subgroups was comparable. Morphosyntactic complexity was higher for spontaneous compared to nongenerative utterances in verbally fluent individuals, while no differences emerged between the two utterance types in minimally verbal individuals. Conclusions: Nongenerative speech presents differently in minimally verbal and verbally fluent autistic individuals. Although present in verbally fluent individuals, nongenerative speech appears to be a major feature of spoken language in minimally verbal children and adolescents. Implications: Our results advocate for more research on the expressive language profiles of autistic children and adolescents who remain minimally verbal and for further investigations of nongenerative speech, which is usually excluded from language samples. Given its prevalence in the spoken language of minimally verbal individuals, nongenerative speech could be used as a way to engage in and maintain communication with this subgroup of autistic individuals. [ABSTRACT FROM AUTHOR]

Background: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. Methods: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. Results: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. Conclusion: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk. [ABSTRACT FROM AUTHOR]