Search

Objectives : Budgetary constraints force healthcare authorities to set priorities for optimal vaccine interventions. A comprehensive decision-making tool would help inform the best combination and sequence of introduction of vaccines within constrained budgets. Methods : Looking at available vaccines against pneumococcal infections in Taiwan (10/13-valent pneumococcal conjugate vaccines [PCV10, PCV13], and 23-valent pneumococcal polysaccharide vaccine [PPV23]), a constrained optimization (CO) model was used to assess the optimal combination of vaccines in children and older adults that would maximize the quality-adjusted life years under pre-defined budget constraints. Scenario analyses were carried out to evaluate the impact of vaccine efficacy (VE) on the optimized solution. Results : The CO model demonstrated that the optimal sequence of vaccine introduction was PPV23 in older adults and PCV10 in children. The optimal solution was mostly driven by the potential to reduce disease burden in the older adult population. VE of PPV23 in older adults and the VE of PCV vaccines against serotype 19A invasive pneumococcal disease had little impact on the optimal solution. Conclusions : The CO approach can be used to set priorities for introducing new vaccines to maximize health gains per age group within the constrained National Vaccine Fund for the prevention of pneumococcal disease in Taiwan.

Background Real-world data on eosinophilic chronic obstructive pulmonary disease (COPD)'s clinical burden, in exacerbating/stable states, and the stability of blood eosinophil count (BEC) measurements are limited. We described measured BEC distributions among general practice COPD patients in New Zealand (NZ). Methods This retrospective cohort study utilized the NZ-HealthStat primary care database. Participants were aged ≥40 years, with ≥1 BEC 6 months following a COPD diagnosis code during 2011–2012. Descriptive analyses included examinations of BEC stability and association with COPD exacerbations/treatments/comorbidities. Results The most frequent COPD comorbidity was asthma (n = 1180/2909, 40.56%). Among COPD patients: 65% had BECs >150 cells/μL; 35% had BECs >300 cells/μL (non-mutually exclusive threshold categories). Treatment patterns were similar, except for more frequent inhaled corticosteroid (ICS)/long-acting beta2-agonist use in COPD patients with asthma history (51%) than those without (31%). Factors associated with BECs >150 cells/μL in participants without ICS treatment included Māori/Pacific ethnicity, obesity, oral corticosteroid (OCS) use, and exacerbation history. When stratified by asthma history, ICS treatment, and neutrophil count above/below 5000 cells/μL, geometric mean BECs ranged from 136.70 to 398.52 cells/μL. Exploratory analyses showed a fair–good COPD/BEC measurement stability over 12 months. Conclusions Asthma was a common COPD comorbidity in NZ, particularly in Māori/Pacific patients. No overall relationship was observed between BEC/COPD exacerbations, which may reflect background ICS confounding. However, analyses in non-ICS treated participants suggested that Māori/Pacific patients with obesity and COPD, OCS treatment, exacerbation history, and/or elevated BECs are at the highest risk of COPD exacerbations. One BEC measurement appears a good indicator of a patient's BECs over time.

This paper presents a centimeter-grade metropolitan positioning (CGMP) framework for achieving lane-level localization through Internet of Vehicles (IoV) communications. The CGMP framework integrates vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications with installed roadside/intersection cameras to provide positioning information to vehicles on the road. This framework can be used to estimate the precise position of a vehicle and detect the lane of each vehicle. The estimated positions and detected lanes can be broadcast to nearby vehicles through V2R and V2V communications. According to our review of relevant research, CGMP is the first positioning system to provide the following features: centimeter-grade positioning accuracy through IoV communications that can detect a vehicle's current lane; the employment of existing roadside/intersection cameras, thus keeping additional construction costs low; and an innovative infrastructure for the application of next-generation intelligent transportation systems that can enable lane-level traffic control, collision avoidance, and vehicle navigation. A prototype based on wireless access in vehicular environments/dedicated short-range communications is implemented to verify the feasibility and effectiveness of the proposed framework. The experimental results indicate that fully deployed CGMP can achieve centimeter-grade positioning accuracy with the error around 10 cm, and partially deployed CGMP can significantly reduce the positioning errors of existing localization methods.

Background: This retrospective cohort study aimed to characterize epidemiology, medication use and healthcare resource utilization (HCRU) of patients diagnosed with severe eosinophilic asthma (SEA) compared to other patients with asthma in New Zealand.Methods: Adult patients with asthma with no concurrent diagnosis of Chronic Obstructive Lung Disease (COPD) were identified from the HealthStat primary care database and the National Minimum Dataset using asthma diagnosis, hospital codes and prescriptions. Patients with SEA were identified using a 1-year baseline period (2011) and were those with: inhaled corticosteroid prescription above medium dose (including high dose) plus controller medication, ≥2 exacerbations, and eosinophils ≥300 cells/µl (or ≥150 in 6 weeks prior to index date); patients were followed for 1 year (2012).Results: 160/3276 (4.9%) asthmatics with available eosinophil counts met SEA criteria. Patients with SEA were more likely to be Māori, former smokers, have more comorbidities, higher mean BMI and higher neutrophil counts compared with other patients with asthma. In the follow-up period, SEA patients had over 4 times as many exacerbations; incidence of exacerbations of the same frequency were highest in Māori patients.Conclusions: Compared with other patients with asthma, SEA patients had over 1.5 times as many respiratory treatment prescriptions and higher all-cause HCRU and total healthcare costs; asthma-related healthcare costs were 3.6 times greater.

In this paper, we propose a cyber-physical signage interacting framework for the interaction between digital signage and mobile users using smart handheld devices, such as smartphones and tablets. The proposed framework can provide diverse multimedia/feedback services to mobile users interacting with digital signage through face detection, classification, and recognition techniques based on mobile cloud computing. Mobile users only need to click and drag the interested service over the face of advertising celebrities/endorsers displayed on digital signage and can obtain supplementary multimedia information or feedback related comments. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage through their handheld devices. Our framework reveals an innovative human–machine interface for signage interacting between digital signage and mobile users. In addition, we integrate our framework with a face cache mechanism that can make the interaction delay as small as possible for popular signage. Furthermore, an Android-based signage interacting system is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms the existing methods and can significantly reduce the average consumption time of obtaining interested multimedia contents from digital signage.

Background: Severe eosinophilic asthma (SEA) is an important asthma phenotype that is poorly described in clinical practice in Asia. Aim and objectives: To characterize the epidemiology, medication dispensation and healthcare resource utilization (HRU) of adult SEA patients in Taiwan. Methods: This retrospective cohort study was carried out within Taichung Veterans General Hospital’s electronic medical record database. Asthma patients aged ≥18 years were identified from 1Jul13-30Jun16. Patients were classified as having SEA if the following criteria were met at baseline: Prescription per GINA Step 4/5 treatment guidelines, 2+ exacerbations, and a blood eosinophil (EOS) count of ≥300 cells/µL or ≥150 cells/µL at baseline. Patients were followed for 1 year post index to examine medication use, HRU and related costs. Results: Of 2,601 eligible asthma patients, 162 (6.2%) patients met the eligibility criteria for SEA. Higher prevalence of SEA was found in current or past smokers (23.5% vs. 16.0%, P=0.0305). The median blood EOS count for SEA patients was higher compared with those without SEA (467.5 cells/µl vs. 167.3 cells/µl, p Conclusions: Although the overall prevalence of SEA was low in this cohort, these patients accounted for higher health care resources than general asthma patients. Funding: GSK (PRJ2409)

Background: Patients with severe eosinophilic asthma (SEA) remain poorly described. Aim and objectives: This study aimed to characterize the epidemiology, medication use and healthcare resource utilization (HRU) of SEA patients compared to non-SEA asthma patients in New Zealand (NZ). Methods: A retrospective case-only cohort study was conducted to investigate adults (age 18+) enrolled in the HealthStat primary care database in NZ. Asthma patients were identified using diagnostic codes and prescriptions for inhaled corticosteroids (ICS). SEA was defined using the following criteria during a 1-year baseline period (2011): ICS prescription above the medium dose plus a controller medication, 2+ exacerbations, and an eosinophil cell count ≥300 cells/µL (or ≥150 in 6 weeks prior to index). SEA and non-SEA patients identified during baseline were followed for 1-year (2012) to evaluate HRU and related costs. Results: Among 3737 asthma patients, 6.2% (n=230) met the criteria for SEA. SEA prevalence was highest for individuals with Maori ethnicity (9.4%), age 60-69 years (9.3%), and former smokers (8.0%). Compared to the non-SEA group, SEA patients were more likely to have diabetes (23% v 15%) or heart failure (10% vs 5%) and a higher mean for BMI (33 vs 31 kg/m2) and neutrophil counts (5.2 vs 4.6 109/L). During baseline, SEA patients had six times as many exacerbations and twice as many respiratory treatment prescriptions than the non-SEA group. SEA patients had 70% more HRU and 3.4 times higher annual HRU costs compared to non-SEA patients. Conclusions: SEA patients had more comorbidities and medication usage and higher HRU and associated costs compared to non-SEA asthma patients in NZ. Funding: GSK (208970)

In this paper, we design and implement a face-based signage interacting system, called AdYou, for mobile users using smartphones. By using the AdYou system, mobile users only need to drag the service icon on the face of a particular target in a digital signage. AdYou is able to provide diverse services to mobile users through face detection and recognition techniques. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage. We further integrate the AdYou server with a face cache mechanism that can make the system response time as small as possible for popular signage. AdYou reveals an innovative user interface for signage interacting between mobile users and digital signage. We have implemented an Android-based AdYou system that can be interacted by mobile users as fast as possible.

Mobile communication and handheld devices are currently extremely popular, and provide people with convenient and instant platforms for social networking. However, existing social networking services cannot offer efficient human-machine interfaces or intuitive user experiences. Mobile users must manually input account information and find targets from search results when attempting to add someone to their friend list on social networking sites, such as Facebook and Twitter. Additionally, mobile users may not be able to identify correct targets because some usernames are identical. Typos may occur during the input process due to unfamiliar identifiers, further increasing the total operation time. To encourage social initiation between mobile users, we design an instant social networking framework, called SocialYou, to minimize the startup time based on mobile cloud computing. SocialYou proposes an efficient architecture and innovative human-machine interfaces to alleviate the complexity and difficulty for mobile users using handheld devices. In particular, we implement an Android-based prototype to verify the feasibility and superiority of SocialYou. The experimental results show that SocialYou outperforms the existing methods and saves substantial amounts of operation time for mobile social networking.

In this paper, we propose a video-based metropolitan positioning system (V-MPS) for providing centimeter-grade localization in vehicular networks. The V-MPS system integrates vehicular networks with installed roadside/intersection cameras to provide positioning information to vehicles on the road. V-MPS can estimate the precise position of a vehicle and detect the located lane for each vehicle. To the best of our knowledge, V-MPS is the first positioning system which provides the following features: 1) it achieves the centimeter-grade positioning accuracy for vehicular networks that can detect the located lanes of vehicles, 2) it employs existing roadside/intersection cameras that can keep additional construction cost low, and 3) it establishes an innovative infrastructure for applications of next-generation Intelligent Transportation System (ITS) that can make lane-level traffic control, collision avoidance, and vehicle navigation possible. With vehicular networks, the estimated positions can be broadcast to nearby vehicles. With roadside/intersection cameras, the lane positions of vehicles can be detected even if GPS is not accurate enough to provide lane-level localization. In addition, the traffic condition can be continually monitored to optimize driving efficiency for next-generation ITS. This paper demonstrates our current prototype.

Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with {\em bounded} relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1\% in server consolidation quality for a reduction about 50\% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.

Summary Most methods of file transfer between mobile devices have to take considerable time to obtain and input target identifiers. To speed up file sharing of mobile devices, an intelligent file transfer framework is designed based on mobile cloud computing. Only single-finger action is performed in the proposed framework, which just drags the file to the target face on the touchscreen of a mobile device. The dragged file can be transmitted to the target receiver who can either get the file immediately as her/his mobile device is online or receive the file later after connecting to the Internet. Our framework provides the following features: (i) users do not need to know the target identifier in advance; (ii) users do not need to input identity information by themselves; and (iii) users can select a specific target among multiple candidates from the camera of a mobile device. The intelligent file transfer framework reveals an efficient architecture and innovative user interfaces to transfer files between mobile devices, which can significantly reduce the complexity and difficulty of file sharing. An Android-based prototype is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms existing schemes and can save large amounts of time in file sharing for mobile users. Copyright © 2015 John Wiley & Sons, Ltd.