The Role of Music Therapy in Supporting Intellectually Disabled Youth in Senegal

Introduction: Music therapy is a practice for helping and supporting people with intellectual and relational difficulties. This study illustrated the benefits of music therapy for young people living with intellectual disabilities (YLID) in an African context. Methodology: This study investigated six young individuals with intellectual disabilities who had undergone three years of music therapy. They were participants in the inclusive non-academic training program at the National School of Arts in Dakar from 2017 to 2019. Data collection utilized individual interviews with the youths, evaluation grids from teachers and psychiatrists. Guardians provided informed consent along with the assent of the young participants. Results: The six young were aged between 18 and 30 years old, with an average age of 24.6 years. Four of the YLID were male. Three young people with intellectual disabilities had delayed psychomotor development. Observations revealed the beneficial influence of music therapy on the health and well-being of young individuals. Music played a role in alleviating stress and anxiety among youth with intellectual disabilities (YLID), enhancing their mood and mental health. It assisted in navigating challenging situations and heightened alertness among YLID. Additionally, music therapy contributed to improvements in dyslexia, fine and gross motor skills, and memory development among intellectually disabled youth, ultimately facilitating their integration into society. Conclusion: In light of our results, music therapy makes a major contribution to the empowerment of YLID. Engaging in musical activities helps young people connect with others through instrumental expression and a sense of accomplishment. By facilitating music therapy, it becomes possible to combat discrimination and stigmatization, thus promoting the social inclusion of intellectually disabled youth. Therefore, it is important to promote music therapy in Senegal to meet the needs of YLID.

Associated Factors of Dental Caries among Intellectually Disabled Persons in Specialized Centers of Ouagadougou

Introduction: Several studies have reported a high prevalence of dental caries among the people with intellectual disabilities. In order to identify the sociodemographic, clinical and behavioral factors associated with it, we conducted a study in specialized centers welcoming subjects with intellectual disabilities in Ouagadougou (Burkina Faso). Objectives: The objective of this study was to identify the factors associated with it among intellectually disabled in the specialized centers of Ouagadougou. Methods: The study was cross-sectional analytical. The survey took place between November 2020 and January 2021 among people with intellectual disabilities in reception centers receiving a grant from the Ministry of Women, National Solidarity, Family and Humanitarian Action. Data collection was done with a questionnaire adapted from the World Health Organization (WHO) and dental caries was recorded according to WHO criteria in decayed, missing or filled teeth generating a DMF index. Logistic regression was used to identify factors associated with the presence of dental caries. Results: A total of 193 participants were included in the study with an average age of 12.4 years ± 7.9. The overall prevalence of caries measured with the DMF index was 58.6% 95% CI [51.2 - 65.8]. It was significantly associated with female gender (OR = 4.2;95% CI [1.4 - 12.5], p = 0,01), the clinical form of mental illness (epilepsy OR = 3.8 95%;CI [1.2 - 12.7], p = 0,02, trisomy OR = 5.0;95% CI [1.1 - 22.9], p = 0,03, motor autonomy OR = 0.2 95%;CI [0.1 - 0.7], p = 0,01) and at use of toothpaste OR = 9.33;95% CI [1.05 - 82.7], p = 0,04. Conclusion: Dental caries remains a very present pathology in most people living with an intellectual disability. Also, it is necessary to put in place prevention strategies to reduce its prevalence and improve access to oral care for these people.

Oral candidiasis and potential risk factors among disabled and nondisabled in Al-Baha region, Saudi Arabia

BACKGROUND Oral candidiasis(OC)is an oral health disease that could influence patients’oral health quality of life.AIM To estimate prevalence of OC among disabled and non-disabled individuals and its potential risk factors in the Al-Baha region,Saudi Arabia.METHODS An observational cross-sectional study was carried out among 148 disabled and non-disabled participants.The technique of concentrated oral rinse employing the Sabouraud Dextrose Agar medium accompanied with 0.05%chloramphenicol was conducted to assess and isolate candida.Oral examination using the World Health Organization guidelines was conducted to examine participants’oral hea-lth status.A pre-designed questionnaire was also used to evaluate sociodemo-graphic,medical history,and oral hygiene habits of the studied population.RESULTS Out of 148 participants(n=57,38%)had colonized candida.None of the studied population had visible Candida lesions.However,Candida was found in the oral rinses without the subject presenting any lesions or issues caused by Candida(asymptomatic colonization).The most common prevalent OC among participants were Candida albicans,Candida glabrata,Candida dubliniensis,Candida krusei,Candida tropicalis,and Candida parapsilosis(n=35,61%;n=8,14%;n=6,10%;n=5,9%;n=2,4%;and n=1,2%)respectively.Diabetes,smoking,poor plaque,and gingival status were key potential risk factors that significantly associated with candida’s density and presence(P=0.001,P=0.001,P=0.01,and P=0.01)respectively.Disability status had no statistically significant effect on presence and density of Candida.CONCLUSION The prevalence of OC is almost third of the studied population;thus,may provoke a need to develop preventive strategies to reduce the OC rate and establish solid treatment plans.

Muscle Strength, Lumbar Curve, Fear of Movement and Functional Disability among Patients with Lumbar Disc Herniation: A Review

Introduction: Lumbar disc herniation (LDH) refers to the rupture of the fibrous annulus of the intervertebral discs. Lumbar curvature may lead to the occurrence of lumbar disc degeneration. Fear of movement may worsen their disc herniation and cause further pain and injury. LDH conditions impact the individuals’ quality of life, to explore the relationship between lumbar curve, muscle strength, fear of movement and functional disability among patients with LDH. Methods: An electronic search was conducted on PubMed, Medline, Science Direct, Springerlink, Google Scholar and a hand search from reference lists was reviewed. Publications were included in human studies, patients 25 - 85 years of age, original studies and published in English language journals from January 2002 to December 2023. Result: In total, 64 articles were researched through the online search engines, and 9 papers were found through manual searches of reference lists. As a result, a total of 11 articles were included for the purpose of this review. The comprehensive analysis revealed the presence of eight cross-sectional studies, two retrospective studies and one experimental study. A minimum of 25 participants and a maximum of 360 participants were included. Ten studies included both genders, only one studies included healthy adults and patients with LDH but these studies didn’t mention gender. Results showed that the factors influencing LDH can be categorized into non-modifiable factors, such as gender, age, height or others. Modifiable factors included increased BMI, DM, smoking, alcohol, employment status, lifestyle and health problems or psychology. Conclusion: Females with greater VASC may be at risk of LDH. The lumbar extensor muscles indicated a localized disc herniation or nerve root pathology in patients with LDH. The fear of movement may lead to psychological consequences and reduce functional disability among patients with LDH.

Effectiveness of Extended Care in Preventing Falls in Disabled Elderly and Its Impact on Quality of Life

Objective:To analyze the value of extended care interventions for disabled elderly in preventing falls and optimizing quality of life.Methods:A sample of 60 cases of disabled elderly in a tertiary hospital from May 2022 to May 2023 was selected and grouped by the random number table method.The observation group received extended care,while the control group adopted routine care.The differences in complication rate,fall rate,36-Item Short Form Health Survey(SF-36)score,health knowledge awareness score,and nursing satisfaction were compared.Results:The complication rate and fall rate of the disabled elderly in the observation group were lower than those in the control group,P<0.05;the SF-36 score,health knowledge score,and nursing satisfaction of the observation group were higher than those of the control group,P<0.05.Conclusion:Extended care for the disabled elderly can reduce the risk of falls and complications related to disability,as well as optimize their cognition and improve their quality of life,which is efficient and feasible.

Burden of disability in type 2 diabetes mellitus and the moderating effects of physical activity

The growing diabetic epidemic has created a substantial burden,not only on the people with diabetes but also on society at large.This mini-review discussed the limitations and patterns of disability in type 2 diabetes mellitus and put forward a case for the moderating effects of physical activity(PA)in the management of diabetes.The limitations and impairments associated with diabetes include vascular,neurological,cardiac,and renal impairments.Moreover,individuals participate less in their daily lives and in their instrumental activities of daily living,which negatively impacts the quality of life of individuals with diabetes.This often leads to a loss of quality of life due to disabilities,resulting in an increased rate of disability-adjusted life years among people with type 2 diabetes mellitus.Moreover,there are psychosocial sequelae of diabetes mellitus.This necessitates looking for moderating factors that may reduce the burden of the disease.PA has been shown to be one of the factors that can mitigate these burdens.PA does this in several ways,including through the benefits it confers,such as a reduction of hemoglobin A1c,a reduction of excess fat in the liver and pancreas,and the reduction of cardiovascular risk factors,all of which favorably affect glycemic parameters.Specifically,PA regulates or moderates diabetes disability through two mechanisms:The regulation of glucolipid metabolism disorders and the optimization of body mass index and systemic conditions.Therefore,efforts should be directed at PA uptake through identified strategies.This will not only prevent diabetes or diabetes complications but will reduce its burden.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

A Smart Room to Promote Autonomy of Disabled People due to Stroke

A cerebral vascular accident,known as common language stroke,is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults.Those disabled people spend most of their time at home in their living rooms.In most cases,appliances of a living room(TV,light,cooler/heater,window blinds,etc.)are generally controlled by direct manipulation of a set of remote controls.Handling many remote controls can be disturbing and inappropriate for these people.In addition,in many cases these people could be alone at home and must open the door for visitors after their identification by either moving to the door or using an intercom system which requires in both cases a physical activity.Furthermore,these people need a continuous health monitoring especially blood pressure to avoid a recurrent stroke.Smart spaces and assisted technologies would be beneficial to assist person with disabilities to live indepen-dently,enhance their quality of life and empower their autonomy.A complete sys-tem which improves and facilitates the daily life and covers all aspects such as appliances automation,appropriate interaction mode and health monitoring of these people is still lacking.The aim of this work is to create a safe and high-quality living environment for persons with disabilities to enable them to live more independently by automating the operation of a living room appliances according to the current context without the need to use remote control devices,the use of a suitable interaction modality with appliances that require direct interaction and a remote health monitoring system which can alert relatives and caregivers in case of an emergency.

Disability to Admit as a Change of Life after a Road Crash: Estimates and Related Factors in Benin for Prevention

Background: Disability is an underestimated issue in public health, with road crashes as leading cause. In Africa, motorcyclists disproportionately bear the burden of road crash injuries, including disability. To contribute to decision-making for disability prevention, this study aims to determine the prevalence and factors associated with disability at 12 months among motorcyclists involved in road crashes in Benin. Methods: This is a prospective, cross-sectional, analytical study based on 12-month follow-up data from a cohort of road crash victims set up in five hospitals in Benin. Data were collected from November 2020 to January 2021. Sample used for this analysis size was 297 motorcyclists. Disability was assessed using the Washington Group on Disabilities Statistics question set. Logistic regression analysis was used to identify risk factors for disability in victims 12 months after the crash. Results: The prevalence of disability was 12.5% 95% CI (9.2 - 16.7). Disability occurrence was associated with being over 45 years old (OR = 3.1;95% CI = 1.5 - 6.6), severity of initial injury (OR = 3.3;95% CI = 1.5 - 7.3) and hospitalisation of the victim (OR = 6.9;95% CI = 2.0 - 24). Conclusion: Age over 45 years, severity of initial injuries and hospitalisation of the victim were risk factors for the occurrence of disability among motorcyclists who were victims of road crashes in Benin. User awareness, law enforcement, holistic and early management of road crash victims could contribute to reducing the prevalence of disability among victims in Benin.

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

Organized physical activity and sedentary behaviors in children and adolescents with autism spectrum disorder,cerebral palsy,and intellectual disability

BACKGROUND There is little data on physical activity(PA),organized PA(OPA),and sedentary behaviors in autism spectrum disorders(ASD)and other neurodevelopmental disorders in developing countries.AIM To examine OPA,non-OPA,and sedentary behaviors and their associated factors in children and adolescents with ASD,cerebral palsy(CP),and intellectual disability(ID).METHODS A total of 1020 children and adolescents with ASD,CP,and ID were assessed regarding the child and family information as well as the Children’s Leisure Activities Study Survey.RESULTS The results showed that the OPA level was significantly lower than non-OPA in all groups.Furthermore,the OPA level was significantly lower in the CP group compared to ASD and ID groups(P<0.001).Also,moderate(P<0.001),vigorous(P<0.05),and total(P<0.001)physical activity levels were significantly different between all three groups,with the values being higher in the ASD group compared to the other two.The mean of the total sedentary behavior duration in the ASD group(1819.4 min/week,SD:1680)was significantly lower than in the CP group(2687 min/week,SD:2673)(P=0.007)but not ID group(2176 min/week,SD:2168.9)(P=0.525).CONCLUSION Our findings remark on the participation rate of PA,OPA,and sedentary behaviors of children and adolescents with ASD,CP,and ID in a developing country.In contrast,the need for developing standards of PA/OPA participation in neurodevelopmental disorders is discussed.

Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

Elderly or disabled people can be supported by a human activity recognition(HAR)system that monitors their activity intervenes and pat-terns in case of changes in their behaviors or critical events have occurred.An automated HAR could assist these persons to have a more indepen-dent life.Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system.With the fast development of neural networks,computing,and machine learning algorithms,HAR system based on wearable sensors has gained popularity in several areas,such as medical services,smart homes,improving human communication with computers,security systems,healthcare for the elderly,mechanization in industry,robot monitoring system,monitoring athlete train-ing,and rehabilitation systems.In this view,this study develops an improved pelican optimization with deep transfer learning enabled HAR(IPODTL-HAR)system for disabled persons.The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living.The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data.Besides,EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer.Finally,the IPO with deep belief network(DBN)model is utilized for the recognition and classification of human activities.The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively.The experimental validation of the IPODTL-HAR method is tested using benchmark dataset.Extensive comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures.

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with others.It helps them to convey the required data by the use of sign language with no issues.The latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled people.American Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher complexity.This article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled People.The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s communica-tion.The presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL models.In the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(ResNet)model is applied for feature extraction.Besides,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process.At last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC model.The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

Falls are the most common concern among older adults or disabled peo-ple who use scooters and wheelchairs.The early detection of disabled persons’falls is required to increase the living rate of an individual or provide support to them whenever required.In recent times,the arrival of the Internet of Things(IoT),smartphones,Artificial Intelligence(AI),wearables and so on make it easy to design fall detection mechanisms for smart homecare.The current study devel-ops an Automated Disabled People Fall Detection using Cuckoo Search Optimi-zation with Mobile Networks(ADPFD-CSOMN)model.The proposed model’s major aim is to detect and distinguish fall events from non-fall events automati-cally.To attain this,the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction process.Next,the CSO-based hyperparameter tuning process is executed for the MobileNet model,which shows the paper’s novelty.Finally,the Radial Basis Function(RBF)clas-sification model recognises and classifies the instances as either fall or non-fall.In order to validate the betterment of the proposed ADPFD-CSOMN model,a com-prehensive experimental analysis was conducted.The results confirmed the enhanced fall classification outcomes of the ADPFD-CSOMN model over other approaches with an accuracy of 99.17%.

Research on the Integrated Development of Disability Services in the Greater Bay Area From the Perspective of Media Functions

As the main window for the public to understand the outside world,mass media has an undeniable influence on economic and social development.The way in which the media reports on issues related to people with disabilities not only affects the voice of the disabled community,which determines the public image of the“disappearing”disabled community,but also affects the public awareness and recognition of the disability cause.To further explore the current status of mainstream media’s coverage of the disability cause,this article combines relevant concepts from media function theory and analyzes reports related to the integrated development of the disability cause in the Guangdong-Hong Kong-Macao Greater Bay Area by using the examples of the Zhuhai Special Zone Daily,Southern Metropolis Daily,and Shenzhen Special Zone Daily since the release of the Development Plan Outline for the Guangdong-Hong Kong-Macao Greater Bay Area in 2019.The results show that the“Integrated Development of the Disability Cause in the Bay Area”presented in the reports has the characteristics of service-oriented and cooperation-based,which is in line with the overall trend of the development of China’s disability cause.However,due to factors such as a small overall quantity of related reports,a relatively single style of reporting and information sources,and a strong promotional tone,there still exists a certain difference between the“media reality”and objective reality constructed by these reports.Therefore,this article suggests that institutional media should fully strengthen their coverage of relevant topics,while enhancing the timeliness and readability of reports by enriching various reporting elements.By further enhancing the effectiveness of reporting and communication,the integrated development of the disability cause in the Bay Area can be effectively promoted.

缺血性脑卒中患者伤残接受度的相关影响因素分析

目的 研究影响缺血性脑卒中患者伤残接受度的相关影响因素。方法 选取2021年1月至2022年10月河北医科大学第二医院神经内科住院的缺血性脑卒中患者696例,采用一般资料调查表、伤残接受度量表、自我管理能力量表、社会支持量表及焦虑自评量表进行调查。结果 缺血性脑卒中患者伤残接受度量表得分为(65.63±11.30)分。多元线性回归结果显示,影响缺血性脑卒中患者伤残接受度的主要因素为文化程度、经济水平、对病情了解情况(P<0.05),自我管理能力与其伤残接受度呈正相关(r=0.557,P<0.01),社会支持度与其伤残接受度呈正相关(r=0.363,P<0.05),焦虑与其伤残接受度呈负相关(r=-0.665,P<0.01)。结论 缺血性脑卒中患者伤残接受度接受水平较低,与患者自我管理能力、社会支持呈正相关,与焦虑呈负相关,医务人员应给予重视,及时对患者进行心理干预,促进伤残接受度水平的提高,改善其身心健康。

1990-2019年中国成年人归因于糖尿病的心血管疾病负担研究

背景在全球老龄化不断深化的背景下,慢性病患者不断增加,多病共存现象愈加严重。在普遍认知中,心血管疾病与2型糖尿病大多属于老年病,但随着居民生活节奏与饮食等行为方式的改变,很多疾病呈现年轻化趋势。现也有研究表明,与一般人群相比,年轻时患糖尿病的患者的心血管疾病风险及死亡率会相对增加。目的探究1990—2019年中国成年人归因于糖尿病的心血管疾病负担情况,为共病预防提供依据。方法基于2019年全球疾病负担研究(GBD)数据,主要采用死亡率、伤残调整寿命年(DALY)率和计算获取的年估计变化百分比(EAPC)等指标评估我国心血管疾病(包括缺血性心脏病、脑卒中和外周动脉疾病3类)归因于糖尿病的疾病负担情况,并对年龄段(25~49岁、50~69岁、≥70岁)及性别进行分层分析,最后对疾病负担的时间趋势进行分析。结果中国25岁及以上人群归因于糖尿病的心血管疾病死亡数从1990年的29.805万例上升到2019年的70.034万例。男性归因于糖尿病的心血管疾病标化死亡率较1990年升高,而女性标化死亡率有所降低,且男性标化死亡率始终高于女性。2019年归因于糖尿病的心血管疾病DALY为1358.585万人年,按年龄划分的死亡率和DALY率随年龄增长而增加。女性标化DALY率下降趋势较男性明显(女性:EAPC=-0.32%,95%CI=-0.49%~-0.11%;男性:EAPC=-0.01%,95%CI=-0.26%~0.29%)。2019年归因于糖尿病的缺血性心脏病、外周动脉疾病患者3个年龄段的死亡率和DALY率较1990年大部分上升,而2019年归因于糖尿病的脑卒中患者3个年龄段的死亡率较1990年下降。1990—2019年心血管疾病中3类心血管疾病归因于糖尿病的标化DALY率占比呈波动性变化,但在2019年归因于糖尿病的3类心血管疾病标化DALY率占比均高于1990年。结论1990—2019年我国成年人归因于糖尿病的心血管疾病的死亡率和DALY率总体呈现上升趋势,人群糖尿病及心血管疾病共病风险较大,应注重在糖尿病患者中筛查心血管疾病或患心血管疾病风险高的个体,重点关注男性、高龄人群以及不良生活习惯较多的年轻人,对其进行早期的健康干预,减少共病疾病负担。

我国融合教育政策变迁的三维审视与展望

融合教育保障了残疾学生进入普通学校接受教育的权利,让每一位学生享有公平而有质量的教育。融合教育改革与发展受政策推动、引导和调整,政策反映了社会中潜在的意识形态和假设。运用“工具—要素—时间”三维分析框架对我国融合教育政策变迁历程进行分析发现:我国融合教育政策经历了解决残疾儿童入普通学校难的问题、扩大融合教育规模、兼顾规模与质量、向高质量融合教育发展迈进四个阶段。伴随政府职能转变以及融合教育发展,融合教育政策中的工具选择及其组合更为科学有效,融合教育政策中的支持要素也更加适应残疾学生的个性化需求。为实现高质量融合教育发展目标,未来的融合教育政策应做好顶层设计以推进融合教育法治化建设,合理组合多种政策工具以解决融合教育发展困境,完善融合教育政策内容以优化融合教育支持要素。

长期护理保险制度试点中的老年人权益保障

基于我国现行的长期护理保险政策和实践,梳理总结了长期护理保险试点阶段的基本情况、主要经验与基本制度框架;结合实地调研资料,分析试点过程中老年人的权益保障问题。结果显示,筹资机制的多元性不强,参保范围与保障对象的城乡覆盖、失能失智覆盖不足,服务需求评估的精准度还有待提高,服务过程中老年人合法权益易受损害且申诉难度较高。结合国内外长期护理保险制度的实践经验,从老年人权益保障的公平性等视角出发提出了相应对策建议:拓宽筹资渠道;逐步扩大制度覆盖面;加大对农村及特殊困难群体的资源倾斜力度;以老年人需求为导向提供长期护理服务;加强服务标准监管与人才队伍建设;完善长期护理保险的法律规制与投诉机制。