Service Dataset Research Articles

Medicare Beneficiaries’ Perspectives on the Quality of Hospital Care and Their Implications for Value-Based Payment

Medicare's Hospital Value-Based Purchasing (HVBP) program adjusts hospital payments according to performance on 4 equally weighted quality domains: clinical outcomes, safety, patient experience, and efficiency. The assumption that performance on each domain is equally important may not reflect the preferences of Medicare beneficiaries. To estimate the relative importance (ie, weight) of the 4 quality domains in the HVBP program from the perspective of Medicare beneficiaries and the impact of using beneficiary value weights on incentive payments for hospitals enrolled in fiscal year 2019. An online survey was conducted in March 2022. A nationally representative sample of Medicare beneficiaries was recruited through Ipsos KnowledgePanel. Value weights were estimated using a discrete choice experiment that asked respondents to choose between 2 hospitals and indicate which they preferred. Hospitals were described using 6 attributes, including (1) clinical outcomes, (2) patient experience, (3) safety, (4) Medicare spending per patient, (5) distance, and (6) out-of-pocket cost. Data analysis was performed from April to November 2022. An effects-coded mixed logit regression model was used to estimate the relative importance of quality domains. HVBP program performance was linked to Medicare payment data in the Medicare Inpatient Hospitals by Provider and Service data set and hospital characteristics from the American Hospital Association Annual Survey data set, and the estimated impact of using beneficiary value weights on hospital payments was estimated. A total of 1025 Medicare beneficiaries (518 women [51%]; 879 individuals [86%] aged ≥65 years; 717 White individuals [70%]) responded to the survey. A hospital's performance on clinical outcomes was most highly valued by beneficiaries (49%), followed by safety (22%), patient experience (21%), and efficiency (8%). Nearly twice as many hospitals would see a payment reduction when using beneficiary value weights than would see an increase (1830 vs 922 hospitals); however, the average net decrease was smaller (mean [SD], -$46 978 [$71 211]; median [IQR], -$24 628 [-$53 507 to -$9562]) than the comparable increase (mean [SD], $93 243 [$190 654]; median [IQR], $35 358 [$9906 to $97 348]). Hospitals seeing a net reduction with beneficiary value weights were more likely to be smaller, lower volume, nonteaching, and non-safety-net hospitals located in more deprived areas that served less complex patients. This survey study of Medicare beneficiaries found that current HVBP program value weights do not reflect beneficiary preferences, suggesting that the use of beneficiary value weights may exacerbate disparities by rewarding larger, high-volume hospitals.

Testicular Cancer Incidence among Obstructive Sleep Apnea (OSA) Patients: South Korean National Health Insurance Data.

Obstructive sleep apnea (OSA) has been linked to an increased risk of acquiring many types of cancer. No data on the prevalence of testicular cancer in OSA patients have been reported in the literature. The goal of the present investigation is to find out the impact of OSA on the incidence of testicular cancer based on the Korea National Health Insurance Service (KNHIS) dataset. A cohort of adult male patients newly registered with OSA in the KNHIS data from 2007 to 2014 who had no history of any previous cancer diagnosis was included. The main outcome measure was newly diagnosed testicular cancer in the National Medical Expenses Support Program. The control group was set at five times larger than the OSA group, and it was matched with age and sex. The cumulative incidence and hazard ratio (HR) for the development of testicular cancer were compared between the OSA and control groups. Further subgroup analysis was conducted in the three different age groups. In the study period, a total of 152,801 male adult patients newly diagnosed with OSA were included, whereas 764,005 individuals were recruited as the control group. The HR of OSA for developing testicular cancer was 1.58 (95% confidence interval [CI]: 0.92-2.60), showing no significant HR regardless of confounding adjustment. However, the subgroup analysis revealed a significantly increased HR to develop testicular cancer of 3.39 (95% CI: 1.08-10.06) in groups aged more than 65, whereas the age ranges of 20-40 and 40-64 showed no significance (1.19 (0.44-2.75) and 1.27 (0.50-2.80), respectively). OSA may not influence the incidence of testicular cancer in the general adult population. However, compared to younger males, males over 65 may be more susceptible to OSA when it comes to developing testicular cancer.

1228-P: Comparison between Combination of Low- or Moderate-Intensity Statin with Ezetimibe and High-Intensity Statin Monotherapy for Primary Prevention of Cardiovascular Disease and All-Cause Mortalities in Patients with Type 2 Diabetes—A Propensity-Matched Natio

Background and Aims: Here, we compared the effects of combination therapy of low- or moderate-statin with ezetimibe and high-intensity statin monotherapy in patients with type 2 diabetes without previous cardiovascular disease (CVD) on incident CVD and all cause mortalities in a real-world setting. Methods: Using the Korean National Health Insurance Service datasets, two cohorts comparing high intensity statin monotherapy with low- or moderate-intensity statin and ezetimibe combination therapy were constructed using a 1:1 propensity score matching procedure and were followed up for 3.4 years. The primary outcome was a composite of myocardial infarction (MI), stroke, and all-cause mortality. The secondary outcome was the occurrence of individual events. Results: Compared to high-intensity statin monotherapy, moderate-intensity statin with ezetimibe combination therapy significantly reduced the risk of composite outcomes (hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.74 - 0.98, P=0.029) as well as stroke (HR 0.70, 95% CI 0.52 - 0.93, P=0.014), but not MI or all cause mortalities. However, there was no significant risk reduction in composite outcomes following low-intensity statin therapy with ezetimibe (HR 1.02, 95% CI 0.75 - 1.39, P=0.882) than that after high-intensity statin monotherapy. The statin with ezetimibe combination showed a consistent efficacy regardless of diverse patients’ baseline characteristics. Conclusions: Moderate-intensity statin with ezetimibe combination therapy was superior to high-intensity statin monotherapy for CVD primary prevention, and low-intensity statin with ezetimibe showed a comparable efficacy to high-intensity statin monotherapy in patients with type 2 diabetes. Disclosure Y.Hwang: None. S.Park: None. I.Jeong: None. K.Ahn: None. H.Chung: None.

Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial

IntroductionIndividuals who access at-risk mental state (ARMS) services often have unusual sensory experiences and levels of distress that lead them to seek help. The Managing Unusual Sensory Experiences (MUSE) treatment...

COUNTY-LEVEL ANALYSIS OF EYE EXAM ACCESS AND UTILIZATION IN THE UNITED STATES

ABSTRACT Purpose The eye exam is a critical tool for the prevention, screening, and diagnosis of ocular and systemic conditions. In this study, we characterize county-level variation in eye exam access and utilization for Medicare patients in the United States. Methods This nationwide study uses the Medicare Physician & Other Practitioners – by Provider and Service dataset. We included all ophthalmologists and optometrists who performed eye exams on Medicare beneficiaries within a United States county in 2019. For every county where exams were performed, we calculated the number of practicing vision testing providers, percentage of providers classified as ophthalmologists, and the number of exams per 100 Medicare beneficiaries. Multiple linear regression was used to characterize associations between these variables and county characteristics, including measures of poverty, education, and income. Results In 2019, 28937,540 eye exams were performed by 46,000 providers in 2,291 U.S. counties. In the median county, 34.9 eye exams were provided per 100 Medicare beneficiaries. The average county had 20.1 exam providers, 16.5% of whom were ophthalmologists. There were a median 6.6 eye exam providers for every 10,000 Medicare beneficiaries in the average county. The average provider performed 517.8 exams. Regression showed counties with lower median household incomes, higher poverty rates, or fewer high-school graduates had fewer eye exam providers per 10,000 Medicare beneficiaries and fewer eye exams performed per 100 Medicare beneficiaries. Conclusions We find significant county-level variation in eye exam utilization and provider availability. This reflects broader, well-recognized trends in socioeconomic health disparities in the U.S.

Engagement and outcomes of marginalised young people in an early intervention youth alcohol and other drug program: The Street Universities model.

Early intervention alcohol and drug (AOD) programs for disadvantaged young people have the potential to substantially decrease the need for future intervention, however there is little research about how young people use these programs or the substance use and other outcomes of such programs. This paper uses data from an Australian AOD early intervention program, The Street Universities, to: describe young people's participation; examine changes in substance use and wellbeing over 90 days; and assess which young people are most positively impacted. Data come from a prospective study of new attendees, measuring retention in and attendance patterns in an 'engagement' program focussed on arts and lifestyle activities (n = 95), and a routine service dataset collected from seven years of therapeutic intervention (n = 3,893), measuring substance dependence (SDS), psychological distress (K10) and quality of life (EQoL). Analysis reveals that young people were retained in the program at high proportion (63% at six months) and more than half of these returned at a frequency of weekly or more often. Young people participating in the therapeutic component of the program reported significant improvements in all key wellbeing indicators with SDS, K10 and EQoL scores significantly improving (at p < .001). These improvements occurred rapidly, within the first 30 days, and were maintained over the 90 day study period. Moreover, young people with the highest SDS and K10 scores and lowest quality of life at baseline experienced the most positive changes. Aligning engagement program with therapeutic intervention can provide comprehensive support to disadvantaged young people, producing substantial improvements in AOD use, distress and wellbeing.

Joint Intent Detection Model for Task-oriented Human-Computer Dialogue System using Asynchronous Training

How to accurately understand low-resource languages is the core of the task-oriented human-computer dialogue system. Language understanding consists of two sub-tasks, i.e., intent detection and slot filling. Intent detection still faces challenges due to semantic ambiguity and implicit intentions with users’ input. Moreover, separately modeling intent detection and slot filling significantly decrease the correctness and relevance between questions and answers. To address these issues, we propose a joint intent detection method using asynchronous training strategy. The proposed method firstly encodes local text information extracted by CNN and relationship information among words emphasized by attention structure. Later, a joint intent detection model with asynchronous training strategy is proposed by either fusing hidden states of intent detection and slot filling layers, or adopting the key information to fine-tune the whole network, greatly increasing the relevance of intent detection and slot filling subtasks. The accuracy achieved by the proposed method tested on an open-source airline travel dataset and a self-collected electricity service dataset, i.e., ATIS and ECSF, are 97.49% and 89.68%, respectively, which proves the effectiveness of joint learning and asynchronous training.

Connecting the composition of collaborative governance structure to community‐level performance in homeless services

AbstractCollaborative governance has become prevalent in public service provision as both government and nonprofit sectors face pressure to solve multidimensional social problems in communities while improving performance. Drawing on collaborative governance and homeless services literature, this article explores how providing services in a collaborative governance network through government and nonprofit service providers differentially relates to multiple dimensions of performance—effectiveness, internal efficiency, social efficiency, and service heterogeneity—at the community level. By using a two‐way fixed effects estimator and a unique nonprofit and homeless services dataset, the findings indicate that collaborative governance between government and nonprofit service providers relates to increased effectiveness (e.g., less homelessness), and mixed results for service heterogeneity relative to using one sector. The composition of collaborative governance networks matters for performance, but its precise relationship with community‐level performance depends on the specific aspect of performance.

Service Selection Using an Ensemble Meta-Learning Classifier for Students with Disabilities

Students with special needs should be empowered to use assistive technologies and services that suit their individual circumstances and environments to maximize their learning attainment. Fortunately, modern distributed computing paradigms, such as the Internet of Things (IoT), cloud computing, and mobile computing, provide ample opportunities to create and offer a multitude of digital assistive services and devices for people with disabilities. However, choosing the appropriate services from a pool of competing services while satisfying the unique requirements of disabled learners remains a challenging research endeavor. In this article, we propose an ensemble meta-learning model that ranks and selects the best IoT services while considering the diverse needs of disabled students within the educational context. We train and test our deep ensemble meta-learning model using two synthetically generated assistive services datasets. The first dataset incorporates 50,000 records representing the possible use of 12 learning activities, fulfilled by 60 distinct assistive services. The second dataset includes a range of 120,000 service ratings of seven quality features, including response, availability, successibility, latency, cost, quality of service, and accessibility. Our deep learning model uses an ensemble of multiple input learners fused using a meta-classification network shared by all the outputs representing individual assistive services. The model achieves significantly better results than traditional machine learning models (i.e., support vector machine and random forest) and a simple feed-forward neural network model without the ensemble technique. Furthermore, we extended our model to utilize the accessibility rating of services to suggest appropriate educational services for disabled learners. The empirical results show the acceptability of our assistive service recommender for learners with disabilities.

Investigating changes in mental health services utilisation in England and their impact on health outcomes and wellbeing during the COVID-19 pandemic: Protocol for a health data-linkage study.

Linking routinely collected health care system data records for the same individual across different services and over time has enormous potential for the NHS and its patients. The aims of this data linkage study are to quantify the changes to mental health services utilisation in responses to the COVID-19 pandemic and determine whether these changes were associated with health-related outcomes and wellbeing among people living in the most deprived communities in North East and North Cumbria, England. We will assemble a retrospective cohort of people having referred or self-referred to NHS-funded mental health services or Improving Access to Psychological Therapies (IAPT) services between 23rd March 2019 and 22nd March 2020 in the most deprived areas in England. We will link together data from retrospective routinely collected healthcare data including local general practitioner (GP) practice data, Hospital Episode Statistics admitted patient care outpatients, and A&E, Community Services Data Set, Mental Health Services Data Set, and Improving Access to Psychological Therapies Data Set. We will use these linked patient-level data to 1) describe the characteristics of the cohort prior to the lockdown; 2) investigate changes to mental health services utilised between multiple time periods of the COVID-19 lockdown including out of lockdown; 3) explore the relationship between these changes and health outcomes/wellbeing and factors that confound and mediate this relationship among this cohort. This study comprises a deprived population-based cohort of people having referred or self-referred to NHS-funded secondary mental health services or Improving Access to Psychological Therapies (IAPT) services over an extended period of the lockdown in England (2019-2022).This study will utilise a new longitudinal data resource that will link together detailed data from a cohort of individual participants and retrospective administrative data relating to the use of primary, secondary, and community care services.The study period covers pre-lockdown, different lockdown and post-lockdown, and out of lockdown periods up to March 2022.Routinely collected administrative data contain limited contextual information and represent an underestimate of total health outcomes for these individuals.Routinely collected datasets can often been incomplete or contain missing data, which can make it difficult to accurately analyse the data and draw meaningful conclusions.Intervention and treatment for mental health conditions are not wholly captured across these data sources and may impact health outcomes.

Epidemiology of porocarcinoma in England 2013-2018: apopulation-based registry study.

Porocarcinoma (PC) is a cutaneous malignancy that differentiates towards (possibly arises from) the sweat ducts and glands. Lack of histological diagnostic markers makes clinical and pathological diagnosis complex. The limited data available suggest the incidence is increasing; however, this remains to be established in national epidemiological studies. To report the incidence, treatment and survival of patients with PC in England from 1 January 2013 to 31 December 2018 using national cancer registry data. PC diagnoses in England during 2013-2018 were identified from the National Disease Registration Service using morphology and behaviour codes. These were registered from routinely collected pathology reports and cancer outcomes and services datasets. The 2013 European age standardized incidence rates (EASRs), Kaplan-Meier all-cause survival and log-rank test were calculated. In total, 738 tumours (396 in males and 342 in females) were diagnosed. The median age at diagnosis was 82 years old (interquartile range 74-88). The most frequently affected site were lower limbs (35.4%), followed by the face (16%). The majority of the cohort received surgical excision (73.0%). The Kaplan-Meier all-cause survival was 45.4% at 5 years, which was lower than in previous studies. The EASR for the whole population was 0.25 [95% confidence interval (CI) 0.23-0.27] per 100 000 person-years (PY)]. PC incidence rates in the East of England (EASR of 0.54, 95% CI 0.47-0.63 per 100 000 PY) were three times higher than the South West (EASR of 0.14, 95% CI 0.10-0.19 per 100 000 PY) where the regional rates were the lowest. This study shows that there is large variation in the EASRs of PC across England. This may reflect differences in how PC is diagnosed and registered in different regions in England. These data support national assessment of the management of PC, which will inform future studies and guideline development.

Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) Parameters of IoT-Based Web Services Data

Background: QoS parameters are volatile in nature and possess high nonlinearity, thus making the IoT-based service and recommendation process challenging. Methods: An efficient and accurate forecasting model is lacking in this area and needs to be explored. Though an artificial neural network is a prominent option for capturing such nonlinearities, its efficiency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input weights and biases without further modification. Only output layer weights are calculated iteratively by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low time complexity and can handle complex domain problems without compromising accuracy. Motivated by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences. Results: Two real-world IoT-enabled web service dataset series are used in developing and evaluating the effectiveness of RVFLN-based forecasts in terms of three performance metrics. Conclusion: Experimental results, comparative studies, and statistical tests are conducted to establish the superiority of the proposed approach over four other similar forecasting techniques.

Novel aeroengine fault diagnosis method based on feature amplification

Data-driven fault diagnosis methods have high requirements for data samples. The ideal state is that the input samples have good separability. However, because of the harsh working environment and complex operating conditions, the collected data are highly nonlinear inseparability. The existing methods often cannot achieve a good classification effect. To solve this problem, two feature amplification methods that make full use of the useful information from the aeroengine service data are proposed. One is a high-dimensional mapping method based on explicit mapping of kernel function to amplify features. The other is an experiential method to amplify features. Both methods map the input samples to high-dimensional space to make them more separable in high-dimensional space and retain the useful information of raw data. Each feature after feature amplification still covers strong independent information that can describe fault feature information from different dimensions. Then, four deep learning algorithms that have a good effect on processing time-series data are selected as the classifier. The aeroengine service dataset and the bearing vibration dataset from Case Western University are used to verify the effectiveness of the two feature amplification methods. The experimental results show that the fault diagnosis accuracy can be improved if sample features after high-dimensional mapping possess good orthogonality, otherwise, the accuracy will be reduced.

Women's preconception health in England: a report card based on cross-sectional analysis of national maternity services data from 2018/2019.

To present the first national-level report card on the state of women's preconception health in England. Cross-sectional population-based study. Maternity services, England. All pregnant women in England with a first antenatal (booking) appointment recorded in the national Maternity Services Dataset (MSDS) from April 2018 to March 2019 (n= 652 880). We analysed the prevalence of 32 preconception indicator measures in the overall population and across socio-demographic subgroups. Ten of these indicators were prioritised for ongoing surveillance based on modifiability, prevalence, data quality and ranking by multidisciplinary UK experts. The three most prevalent indicators were the proportion of the 22.9% of women who smoked 1year before pregnancy who did not quit smoking before pregnancy (85.0%), those who had not taken folic acid supplementation before pregnancy (72.7%) and previous pregnancy loss (38.9%). Inequalities were observed by age, ethnicity and area-based deprivation level. The ten indicators prioritised were not taking folic acid supplementation before pregnancy, obesity, complex social factors, living in the most deprived areas, smoking around the time of conception, overweight, pre-existing mental health condition, pre-existing physical health condition, previous pregnancy loss and previous obstetric complication. Our findings suggest important opportunities to improve the state of preconception health and reduce socio-demographic inequalities for women in England. In addition to MSDS data, other national data sources that record further and possibly better quality indicators could be explored and linked to build a comprehensive surveillance infrastructure.

A Multi-source Information Graph-based Web Service Recommendation Framework for a Web Service Ecosystem

Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.

Household Composition and Inequalities in COVID-19 Vaccination in Wales, UK.

The uptake of COVID-19 vaccination in Wales is high at a population level but many inequalities exist. Household composition may be an important factor in COVID-19 vaccination uptake due to the practical, social, and psychological implications associated with different living arrangements. In this study, the role of household composition in the uptake of COVID-19 vaccination in Wales was examined with the aim of identifying areas for intervention to address inequalities. Records within the Wales Immunisation System (WIS) COVID-19 vaccination register were linked to the Welsh Demographic Service Dataset (WDSD; a population register for Wales) held within the Secure Anonymised Information Linkage (SAIL) databank. Eight household types were defined based on household size, the presence or absence of children, and the presence of single or multiple generations. Uptake of the second dose of any COVID-19 vaccine was analysed using logistic regression. Gender, age group, health board, rural/urban residential classification, ethnic group, and deprivation quintile were included as covariates for multivariable regression. Compared to two-adult households, all other household types were associated with lower uptake. The most significantly reduced uptake was observed for large, multigenerational, adult group households (aOR 0.45, 95%CI 0.43-0.46). Comparing multivariable regression with and without incorporation of household composition as a variable produced significant differences in odds of vaccination for health board, age group, and ethnic group categories. These results indicate that household composition is an important factor for the uptake of COVID-19 vaccination and consideration of differences in household composition is necessary to mitigate vaccination inequalities.

A novel method for aeroengine performance model reconstruction based on CDAE model

Aeroengine is a complex multi-module system. Due to the limitation of sensor cost and sensor installation conditions, it is usually impossible to install a large number of sensors to measure the physical parameters of the aeroengine modules to establish the accurate module characteristic models to achieve the purpose of module performance evaluation. To address this issue, the high-dimensional physical field reconstruction strategy base on limited measurement data is developed, which is of great significance to the modeling of module characteristics. A reconstruction framework of a high-dimensional physical field based on limited measurement data is built. The mapping relationship between limited measurement data and high-dimensional physical field data is established, and the relevant learning strategies based on the deep learning network are designed. To verify the effectiveness of the proposed method, the simulation dataset generated by the multi-component closed-loop simulation system and the aeroengine service dataset are used for experimental verification, and the mean and variance of mean square error are used as evaluation indexes. Experimental results show that the proposed method can obtain high-dimensional physical field distribution based on limited measurement data.

Deception detection in conversations using the proximity of linguistic markers

Detecting the elements of deception in a conversation takes years of study and experience, and it is a skill set primarily used in law-enforcement agencies. In ever-growing business opportunities, organisations employ teleoperators to provide support and services to their large customer base, which is a potential platform for fraud. With technological advancements, it is desirable to have an automated system that spots the deceptive elements in the conversation, and provides this information to the teleoperators to better support them in their interactions. We propose the Decision Engine to detect deceptive conversation based on the proximity of linguistic markers present, which produces a deception score for a conversation and highlights the potential deceptive elements of the conversation. In collaboration with behavioural experts, we have selected ten linguistic markers that potentially indicate deception. We have built a variety of models to detect the trigger terms for selected linguistic markers without ambiguity, using either regular expressions or the BERT model. The BERT model has been trained on a conversational dataset that we collated and was labelled by our behavioural experts. The proposed Decision Engine employs the BERT model and regular expressions to detect the linguistic markers and compute the proximity features to further estimate the deception score. We evaluated the proposed approach on the Columbia-SRI-Colorado (CSC) dataset and a real-world Financial Services dataset. In addition to accuracy, we have also employed the True Positive Rate metric, with a high enough threshold to avoid any false-positive cases, which we indicate as TPRF0. The Decision Engine achieves 69% accuracy and 46% TPRF0 for the CSC dataset and 72% accuracy and 60% TPRF0 for the Financial Services dataset. In contrast, a baseline model, which uses non-proximity features achieves 67% accuracy and 32% TPRF0 for the CSC dataset and 67% accuracy and 10% TPRF0 for the Financial Services dataset. Furthermore, using the Decision Engine, the impact of the proximity of markers on the deception score has been analysed by our behavioural experts to provide insight into linguistic behaviour in relation to deception.

Predictors of Participation Difficulties in Autistic Children.

Participation in meaningful occupations supports quality of life and health. Because quality of life is lower in autistic children than in children without this diagnosis, it is important to consider aspects contributing to the participation difficulties this population experiences. To identify predictors of participation difficulties in a large data set from autistic children to inform professionals about potential intervention targets. Retrospective cross-sectional design using a large data set with multivariate regression models for home life, friendships, classroom learning, and leisure activities. 2011 Survey of Pathways to Diagnosis and Services data set. Parents or caregivers of 834 autistic children with co-occurring intellectual disability (ID) and 227 autistic children with no ID. The strongest participation predictors within the scope of occupational therapy practice were sensory processing, emotional regulation, behavioral variables, and social variables. Our results are consistent with those of smaller previous studies and indicate the importance of addressing these areas in occupational therapy intervention in line with client priorities. Focusing interventions with autistic children on sensory processing, emotional regulation, behavioral skills, and social skills to address their underlying neurological processing can support their increased participation in home life, friendships, classroom learning, and leisure activities. What This Article Adds: Our findings support a focus in occupational therapy interventions on sensory processing and social skills to increase activity participation in autistic children with and without ID. Emotional regulation and behavioral skills can be supported by interventions that target cognitive flexibility. Positionality Statement: This article uses the identity-first language autistic people. This nonableist language describes their strengths and abilities and is a conscious decision. This language is favored by autistic communities and self-advocates and has been adopted by health care professionals and researchers (Bottema-Beutel etal., 2021; Kenny etal., 2016).

Cognitive decline sensitivity by educational level and residential area: A descriptive study using long-term care insurance dementia registration data in South Korea.

In South Korea Long-Term Care Insurance (LTCI) system, the special dementia rating (SDR) is a registration grading for dementia patients who do not have a physical disability or functional restrictions and is the first applicable registration following the diagnosis of dementia. We investigated the differences in age of registration of SDR and age of dementia diagnosis according to the educational level and residential area. This was a retrospective, cross-sectional study using the Korean National Health Insurance Service dataset. Applications for SDR between July 2014 and December 2016 were identified for participant selection, and 32,352 patients with dementia were included. Educational levels were defined as follows: the illiterate, only-reading, 1 to 6 years, 6 to 12 years, and ≥12 years. Urban residents were those who lived in the city, as ascertained from the Korean administrative district system. The primary outcomes were ages at the time of dementia diagnosis and SDR registration. A lower education level significantly correlated with a higher proportion of older adults, but a higher number of years of education significantly increased with the proportion of males and urban residents (P < .001 for all). A higher education level was inversely associated with the age at diagnosis of dementia (P < .001) and at the registration of SDR (P < .001). Urban residents were diagnosed with dementia at a significantly lower age and registered for SDR earlier than rural residents (P < .001 for both). Both urban and rural residents consistently showed that a higher educational level was associated with lower age at the dementia diagnosis and SDR registration. Patients who were highly educated and living in urban areas were diagnosed with dementia and registered on SDR when they were relatively younger, indicating that cognitive decline sensitivity and medical accessibility are related to earlier dementia diagnosis and registration.