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States’ Adoption of Evidence-Based Treatment for Opioid Use Disorder Varies by Medicaid Expansion Status
No full textBetween 1998 and 2018, 450,000 Americans have died of overdose from opioid use disorder (OUD). In wake of the pandemic, there was a 42% increase in opioid overdoses in May 2020, compared with May 2019. The annual cost tosociety of prescription drug use disorder is estimated at $74 billion dollars. Despite the significant fiscal and societal burden of this disease, access to evidence-based treatments as outlined Federal Code 42 and the American Societyof Addiction Medicin eremains limited. Of those who sought treatment for OUD in 2016, 38% were covered by Medicaid, while 20% were uninsured. In 2017, there were 2.3 million Americans with OUD, yet there was a 25% decrease in OUD treatment. By 2019, less than 17% of patient diagnosed with OUD received treatment. Given state variation in Medicaid coverage of OUD treatment and the most important barrier to treatment is inadequate insurance coverage, understanding state adoption policies for OUD treatment is crucial to addressing this public health crisis
Effects of Caregiver Oral Health Interventions on Quality-of-Life Outcomes in Long-Term Care Facilities
No full textDissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Health Sciences.Background: An increasing older adult population presents challenges to the health care system at large. Older adults often face comorbidities that accelerate the need for long-term care dependence when the older adult’s physical or cognitive limitations become unmanageable. Long-term care facility residents commonly suffer from poor oral health, which often worsens or is exacerbated by comorbidities. Thus, long-term care facility residents are often victims of oral health disparities whereby poor oral health leads to poor overall health and quality-of-life outcomes. Long-term care facility caregivers play a critical role in sustaining and even improving long-term care residents’ oral health by providing assistance with daily oral health interventions. The purpose of this literature review was to research the effects of caregiver-assisted oral health interventions on long-term care facility residents’ quality of life.
Methods: The review of the literature addressed the following topics: (a) geriatric oral health, (b) oral health interventions, (c) quality-of-life outcomes, and (d) caregiver oral health training.
Results: A review of the literature demonstrates oral health interventions provided by long-term care facility caregivers as necessary activities of daily living to maintain long-term care facility residents’ oral health. Most importantly, the oral health interventions provided by long-term care facility caregivers have been shown to improve long-term care facility residents’ quality-of-life outcomes. Long-term care facility caregivers are active participants in ensuring the successful aging of long-term care facility residents.
Conclusion: Long-term care facility caregivers play a valuable role in decreasing the number of oral health disparities incurred by long-term care facility residents. Long-term caregiver-assisted oral health interventions provided to long-term care facility residents promote the improvement of residents’ quality-of-life outcomes. Daily mouth care is deemed to be an essential activity of daily living that care-dependent long-term care facility residents must receive based upon their individualized care plans
Inconsistencies of Spurling’s Test in Chiropractic Education: A Survey Study Design
No full textObjectives: To explore the in consistencies of Spurling’s test in chiropractic education. To identify areas of discrepancy in methodology, interpretation and utility of Spurling’s test in the chiropractic profession. To encourage other fields of practice to evaluate Spurling’s test in both clinical and educational settings. To contribute valuable data in efforts to make Spurling’s test a more dependable orthopedic exam
The Efficacy of Silver Nanoparticles as a Treatment for Bacterial Biofilms
No full textBiofilms provide a protected environment in which bacteria can grow and thrive. These biofilms are especially resistant to antibiotic treatments. This poses a huge problem as 80% of infections that are of clinical significance grow within biofilms. Silver has been known to have antimicrobial properties. Only within the past 15 years, however, has the nanoparticle form of silver been seriously researched for the purpose of treating bacterial biofilms. The purpose of this dissertation was to determine the efficacy of silver nanoparticles in combating bacterial biofilms. A literature review was performed by collecting peer-reviewed research articles that addressed each of the following sub-questions: i) are silver nanoparticles effective against antibiotic-resistant biofilms, ii) by what mechanisms do silver nanoparticles act, iii) is the efficacy of silver nanoparticles dependent on dose or morphology, iv) can resistance develop to silver nanoparticles, v) are there risks or side effects associated with silver nanoparticles or unknown safety concerns, and vi) what avenues of silver nanoparticles administration are the most promising. The findings were combined in table form. Silver nanoparticles stand out as a promising treatment, as they have been found to be effective against MDR bacterial biofilms. The size, shape, surface properties, and environment of the nanoparticle will greatly determine its efficacy. It is possible and necessary to engineer the properties of the nanoparticle to the desired function. While safety concerns about silver nanoparticles still remain, they appear to be a promising treatment for improved drug delivery, wound dressings, and coatings on medical implants, dental implants, catheters and other medicals devices
Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain
No full textManufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.https://doi.org/10.3390/su1213541
An Action Research Study of the Impact of Professional Development on Unit Planning and Curriculum Alignment: At the Middle School Level
No full textThis action research study of teacher instructional planning and professional development was conducted using qualitative and quantitative data in the needs assessment, Iteration 1 and 2. The purpose of the needs assessment was to uncover teacher perspectives regarding instructional common planning time and unit planning and teacher behaviors at the building, department, and district level in order to inform the intervention iterations. Iteration 1 and 2 focused on determining if “Understanding by Design” (UbD) framework affected teacher unit planning instructional practices at the building level. The UbD framework was based on the Marzano Unit Planning Rubric and applicable checklist. Through the professional development intervention framework (Cooper, 2009), the coach-researcher worked alongside teachers in both iterations when developing a unit of study aligned to curriculum standards. Teachers in both iterations indicated the professional development intervention and the Understanding by Design framework positively influenced their unit planning instructional practices and transformed their planning thinking. The findings indicate the importance of understanding teacher instructional challenges to inform the improvement of instructional planning professional development. In addition, the findings suggest the Understanding by Design framework was an effective tool in improving teacher unit planning instructional practice at the building level
Energy Consumption Prediction for 3-RRR PPM through Combining LSTM Neural Network with Whale Optimization Algorithm
No full textIn the process of minimizing the energy consumption of a 3-RRR planar parallel manipulator (3-RRR PPM) and even general parallel kinematic manipulators, obtaining optimal results usually depends on particular functional relation between the instantaneous position of the moving platform and the kinetic time, which is called a displacement model (DM). Nevertheless, it is likely that although the movement time and path of a moving platform are the same, different amounts of energy are consumed for different DMs of the moving platform. To address this, a method of using long short-term memory neural network (LSTM-NN) instead of a complex theoretical model to predict the energy consumption of a 3-RRR PPM was presented. Subsequently, inverse dynamic equations of 3-RRR PPM were established based on the Newton–Euler method and solved using QR decomposition. Meanwhile, energy consumption between any two points in workspace of the 3-RRR PPM was programmed to provide the LSTM-NN with abundant precise training data. In view of time-varying characteristics of energy consumption prediction, the network architecture was developed based on the principle of LSTM-NN, and root-mean-square error (RMSE) was taken as the loss function. After acquiring training data, the RMSE of the LSTM-NN reached 0.00041 using whale optimization algorithm (WOA) with no need for the gradient of the loss function, so the lack of solving precision in training LSTM-NN was effectively improved. Finally, two different DMs of a moving platform with the same path and movement time were chosen to compare the total energy consumption of the 3-RRR PPM from the simulations, predictions, and experiments. The results showed that the relative error between predicted and experimental data was less than 2.50%. Therefore, the energy consumption prediction based on the LSTM-NN will be useful for achieving the intelligent application of 3-RRR PPMs.https://doi.org/10.1155/2020/659039
Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps
No full textRecent technological developments along with advances in smart healthcare have been rapidly changing the healthcare industry and improving outcomes for patients. To ensure reliable smartphone-based healthcare interfaces with high levels of efficacy, a system dynamics model with sustainability indicators is proposed. The focus of this paper is smartphone-based breathing monitoring systems that could possibly use breathing sounds as the data acquisition input. This can especially be useful for the self-testing procedure of the ongoing global COVID-19 crisis in which the lungs are attacked and breathing is affected. The method of investigation is based on a systems engineering approach using system dynamics modeling. In this paper, first, a causal model for a smartphone-based respiratory function monitoring is introduced. Then, a systems thinking approach is applied to propose a system dynamics model of the smartphone-based respiratory function monitoring system. The system dynamics model investigates the level of efficacy and sustainability of the system by studying the behavior of various factors of the system including patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded software and hardware breathing monitoring system design and performance metrics (e.g., accuracy, real-time response, etc.). The sustainability level is also studied through introducing various indicators that directly relate to the three pillars of sustainability. Various scenarios have been applied and tested on the proposed model. The results depict the dynamics of the model for the efficacy and sustainability of smartphone-based breathing monitoring systems. The proposed ideas provide a clear insight to envision sustainable and effective smartphone-based healthcare monitoring systems.https://doi.org/10.3390/su1212506
Misinformation, chiropractic, and the COVID-19 pandemic
Get PDFBackground: In March 2020, the World Health Organization elevated the coronavirus disease (COVID-19) epidemic to a pandemic and called for urgent and aggressive action worldwide. Public health experts have communicated clear and emphatic strategies to prevent the spread of COVID-19. Hygiene rules and social distancing practices have been implemented by entire populations, including ‘stay-at-home’ orders in many countries. The long-term health and economic consequences of the COVID-19 pandemic are not yet known. Main text: During this time of crisis, some chiropractors made claims on social media that chiropractic treatment can prevent or impact COVID-19. The rationale for these claims is that spinal manipulation can impact the nervous system and thus improve immunity. These beliefs often stem from nineteenth-century chiropractic concepts. We are aware of no clinically relevant scientific evidence to support such statements. We explored the internet and social media to collect examples of misinformation from Europe, North America, Australia and New Zealand regarding the impact of chiropractic treatment on immune function. We discuss the potential harm resulting from these claims and explore the role of chiropractors, teaching institutions, accrediting agencies, and legislative bodies. Conclusions: Members of the chiropractic profession share a collective responsibility to act in the best interests of patients and public health. We hope that all chiropractic stakeholders will view the COVID-19 pandemic as a call to action to eliminate the unethical and potentially dangerous claims made by chiropractors who practise outside the boundaries of scientific evidence.https://doi.org/10.1186/s12998-020-00353-
An Enhanced Design of Sparse Autoencoder for Latent Features Extraction Based on Trigonometric Simplexes for Network Intrusion Detection Systems
No full textDespite the successful contributions in the field of network intrusion detection using machine learning algorithms and deep networks to learn the boundaries between normal traffic and network attacks, it is still challenging to detect various attacks with high performance. In this paper, we propose a novel mathematical model for further development of robust, reliable, and efficient software for practical intrusion detection applications. In this present work, we are concerned with optimal hyperparameters tuned for high performance sparse autoencoders for optimizing features and classifying normal and abnormal traffic patterns. The proposed framework allows the parameters of the back-propagation learning algorithm to be tuned with respect to the performance and architecture of the sparse autoencoder through a sequence of trigonometric simplex designs. These hyperparameters include the number of nodes in the hidden layer, learning rate of the hidden layer, and learning rate of the output layer. It is expected to achieve better results in extracting features and adapting to various levels of learning hierarchy as different layers of the autoencoder are characterized by different learning rates in the proposed framework. The idea is viewed such that every learning rate of a hidden layer is a dimension in a multidimensional space. Hence, a vector of the adaptive learning rates is implemented for the multiple layers of the network to accelerate the processing time that is required for the network to learn the mapping towards a combination of enhanced features and the optimal synaptic weights in the multiple layers for a given problem. The suggested framework is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyber-attacks. Experimental results demonstrate that the proposed architecture for intrusion detection yields superior performance compared to recently published algorithms in terms of classification accuracy and F-measure results.https://doi.org/10.3390/electronics902025