Artificial intelligence to automate assessment of ocular and periocular measurements.

PURPOSE: To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements. METHODS: Patients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient. RESULTS: A total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%. CONCLUSION: The automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.

Stratified assessment of treatment approach for craniocervical junction arteriovenous fistulas: a multicenter cohort study and literature review.

OBJECTIVE: Craniocervical junction arteriovenous fistulas (CCJ-AVFs) are complex vascular shunts that present a challenge for treatment. The aim of this study was to compare the clinical outcomes of microsurgery and endovascular embolization for CCJ-AVFs and to determine whether the treatment approach affected the obliteration rate and neurological improvement. METHODS: The authors conducted a retrospective analysis of 64 patients who had undergone microsurgery or endovascular embolization for CCJ-AVF at one of two neurosurgical centers from January 2014 to February 2022. Additionally, a pooled analysis of 68 patients from 38 studies was performed. Baseline characteristics, angioarchitectural features, and clinical outcomes were compared between two treatment groups. A subgroup analysis of CCJ-AVFs with carotid artery (CA) feeders was also performed. RESULTS: In the multicenter cohort, the complete obliteration rate was 95.1% with microsurgery, 81.8% with embolization via the CA, and 50.0% with embolization via the vertebral artery (VA). After adjusting for baseline and confounding features, the occlusion rate was significantly lower in the VA embolization group (adjusted OR 41.06, 95% CI 2.37-711.9, p = 0.01). No new-onset infarctions occurred in the microsurgical group, whereas 1 patient each in the CA and VA embolization groups experienced posttreatment infarction. Microsurgery demonstrated a neurological improvement rate similar to that in the CA embolization group (65.9% vs 63.6%, respectively). In the subgroup analysis of CCJ-AVF with CA feeders in the multicenter cohort, the occlusion rate and neurological improvement in the CA embolization group were comparable to those in the microsurgery group. The subgroup analysis in the pooled analysis revealed complete obliteration rates of 100.0% in the microsurgical group, 88.9% in the CA embolization group, and 66.7% in the VA embolization group. CONCLUSIONS: This study supports microsurgery as the best treatment modality for CCJ-AVFs, exhibiting the highest rates of complete obliteration. Conversely, embolization via the VA can result in a lower occlusion rate and less neurological improvement. In CCJ-AVFs with CA feeders, embolization via the CA can be a safe and effective alternative to microsurgery.

Clinical value of the Patient Global Assessment with Ankylosing Spondylitis: A cross-sectional study.

To analyze the factors associated with the overall patient condition and explore the clinical value of the Patient Global Assessment (PGA) index for assessing the disease state in patients with Ankylosing Spondylitis (AS). This cross-sectional study used a standardized questionnaire to record the basic information of patients with AS. The collected data included the Ankylosing Spondylitis Disease Activity Score (ASDAS)-C-reactive protein (CRP), ASDAS-erythrocyte sedimentation rate (ESR), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), PGA, and other clinical indicators. Statistical analysis was performed using SPSS 25.0 software, and the scale was assessed for retest reliability and structural validity. The Kruskal-Wallis H test and Spearman or Pearson correlation analysis were used to analyze the factors influencing PGA scores. The receiver operator characteristic (ROC) curve was used to identify the cutoff value of the PGA for predicting disease activity in AS. The patient age, disease duration, family history, and history of ocular inflammation significantly differed between PGA groups (P < .05). The median PGA was significantly lower in patients with disease remission than in those with disease activity (P < .01). The various clinical indexes significantly differed between PGA groups (P < .01). The PGA was significantly correlated with various clinical indicators (P < .01). The area under the ROC curve (AUC) for disease activity based on the ASDAS-CRP was 0.743 (P < .01) with a PGA cutoff value of 1.38; the AUC for disease activity based on the BASDAI was 0.715 (P < .01) with a PGA cutoff value of 1.63. The PGA was significantly correlated with patient-reported outcomes, disease activity, function, and psychological status, and may indicate the level of inflammation in patients with AS. A PGA of around 1.5 indicates disease activity.

Spontaneous delayed migration or shortening after pipeline embolization device treatment of intracranial aneurysm: incidence, management, and risk factors.

BACKGROUND: Studies reporting spontaneous delayed migration or shortening (SDMS) after treatment with the Pipeline Embolization Device (PED) are limited. This study aimed to evaluate the incidence of SDMS after PED treatment, propose management strategies, and identify the risk factors contributing to its occurrence. METHODS: We retrospectively reviewed consecutive patients with an intracranial aneurysm (IA) treated with PEDs at three institutions. SDMS was classified as type I or II based on whether the PED covered the aneurysm neck. RESULTS: The total cohort comprised 790 patients. SDMS was identified in 24 (3.04%) patients. Eighteen of the 24 patients had type I SDMS and did not require retreatment, while the remaining six patients had type II SDMS and all received retreatment. Multivariate logistic regression showed that the difference between the proximal and distal parent artery diameters (DPAD) (adjusted OR 2.977; 95% CI 1.054 to 8.405; P=0.039) and device tortuosity index (DTI) (adjusted OR 8.059; 95% CI 2.867 to 23.428; P<0.001) were independent predictors of SDMS after PED treatment, while the difference in length (DL) (adjusted OR 0.841; 95% CI 0.738 to 0.958; P=0.009) and PED plus coiling (adjusted OR 0.288; 95% CI 0.106 to 0.785; P=0.015) were protective factors. CONCLUSION: The incidence of SDMS after PED treatment of IA was 3.04%. For patients with type I SDMS with incomplete aneurysm occlusion we recommend continuous imaging follow-up while, for patients with type II SDMS, we recommend aggressive retreatment. The DPAD and DTI were independent risk predictors of SDMS after PED treatment, while the DL and PED plus coiling were protective factors.

Echocardiographic assessment of pediatric heart transplantation: A single-center experience in China.

BACKGROUND: Pediatric heart transplant (HT) has become the standard of care for end-stage heart failure in children worldwide. Serial echocardiographic evaluations of graft anatomy and function during follow-up are crucial for post-HT management. However, evolution of cardiac structure and function after pediatric HT has not been well described, especially during first year post-HT. This study aimed to characterize the evolution of cardiac structure and function after pediatric HT and investigate the correlation between biventricular function with adverse clinical outcomes. METHODS: A single-center retrospective study of echocardiographic data obtained among 99 pediatric HT patients was conducted. Comprehensive echocardiographic examination was performed in all patients at 1-, 3-, 6-, 9- and 12-months post-HT. We obtained structural, functional and hemodynamic parameters from both left- and right-side heart, such as left ventricular stroke volume (LVSV), left ventricular ejection fraction (LVEF), right ventricular fractional area change (RVFAC), etc. The cardiac evolution of pediatric HT patients during first post-HT year was described and compared between different time points. We also explored the correlation between cardiac function and major adverse transplant events (MATEs). RESULTS: 1) Evolution of left heart parameters: left atrial length, mitral E velocity, E/A ratio, LVSV and LVEF significantly increased while mitral A velocity significantly decreased over the first year after HT (P < .05). Compared with 1 month after HT, interventricular septum (IVS) and left ventricular posterior wall (LVPW) decreased at 3 months but increased afterwards. (2) Evolution of right heart parameters: right ventricular base diameter and mid-diameter; right ventricular length diameter, tricuspid E velocity, E/A ratio, tricuspid annular velocity e' at free wall, and RVFAC increased, while tricuspid A velocity decreased over the first year after HT (P < .05). (3) Univariate logistic regression model suggests that biventricular function parameters at 1-year post-HT (LVEF, RVFAC, tricuspid annular plane systolic excursion and tricuspid lateral annular systolic velocity) were associated with MATEs. CONCLUSION: Gradual improvement of LV and RV function was seen in pediatric HT patients within the first year. Biventricular function parameters associated with MATEs. The results of this study pave way for designing larger and longer follow-up of this population, potentially aiming at using multiparameter echocardiographic prediction of adverse events.

Ecological Risk and Pollution Assessment of Heavy Metals in Farmland Soil Profile with Consideration of Atmosphere Deposition in Central China.

Heavy metals (HMs) in agricultural land have caused serious environmental problems, resulting in severe contamination of the food chain and posing potential health threats. This study aims to investigate the pollution levels and potential ecological risks of HMs in farmland soils in central China, taking into account atmospheric deposition. Several indices were used to assess the status of HMs and compare surface soil with deeper soil. Descriptive statistics, Pearson correlation, and UMAP clustering methods were utilized to identify the characteristics of HMs. Additionally, stepwise linear regression models were employed to quantify the contributions of different variables to the potential ecological risks of HMs. The results showed that the average content of Zn in surface soil (289.41 ± 87.72 mg/kg) was higher than in the deeper soil (207.62 ± 37.81 mg/kg), and similar differences were observed in the mean values of related Igeo (1.622 ± 0.453 in surface soil and 1.183 ± 0.259 in deeper soil) and PEI (0.965 ± 0.292 in surface soil and 0.692 ± 0.126 in deeper soil) indices. This indicates that surface soil is more heavily polluted. The UMAP results confirmed the high variability of HMs in the surface soil, while PCA results suggested the importance of pollution and ecological risk indices. The stepwise linear model revealed that different variable structures contribute differently to the risk. In conclusion, Cr and Zn were found to be the major contaminants in the local farmland soil, with higher concentrations in the surface soil. The geoaccumulation and total potential ecological risk were classified as low risk. High variability of HMs was observed in the surface soil. Therefore, HM-related pollution indices and ecological risk indices are important for assessing the contamination status of local HMs. The local potential ecological risk can be attributed to specific heavy metals, each of which can have different effects on the local ecological risk.

Current-use pesticides monitoring and ecological risk assessment in vegetable soils at the provincial scale.

Pesticides represent one of the largest intentional inputs of potentially hazardous compounds into agricultural soils. However, as an important vegetable producing country, surveys on pesticide residues in soils of vegetable production areas are scarce in China. This study presented the occurrence, spatial distribution, correlation between vegetable types and pesticides, and ecological risk evaluation of 94 current-use pesticides in 184 soil samples from vegetable production areas of Zhejiang province (China). The ecological risks of pesticides to soil biota were evaluated with toxicity exposure ratios (TERs) and risk quotient (RQ). The pesticide concentrations varied largely from below the limit of quantification to 20703.06 µg/kg (chlorpyrifos). The situation of pesticide residues in Jiaxing is more serious than in other cities. Soils in the vegetable areas are highly diverse in pesticide combinations. Eisenia fetida suffered exposure risk from multiple pesticides. The risk posed by chlorpyrifos, which exhibited the highest RQs at all scenarios, was worrisome. Only a few pesticides accounted for the overall risk of a city, while the other pesticides make little or zero contribution. This work will guide the appropriate use of pesticides and manage soil ecological risks, achieving green agricultural production.

Trends and prediction in the incidence rate of hepatitis C in Shandong Province in China from 2004 to 2030.

BACKGROUND: Hepatitis C threatens human health and brings a heavy economic burden. Shandong Province is the second most populous province in China and has uneven regional economic development. Therefore, we analyzed the incidence rate trend and regional differences of hepatitis C in Shandong Province from 2004 to 2021. METHODS: The monthly and annual incidence rates of hepatitis C in Shandong Province from 2022 to 2030 were predicted by fitting Autoregressive Integrated Moving Average model (ARIMA), Long Short-Term Memory (LSTM) and ARIMA-LSTM combined model. RESULTS: From 2004 to 2021, annual new cases of hepatitis C in Shandong Province increased from 635 to 5834, with a total of 61,707 cases. The incidence rate increased from 0.69/100 thousand in 2004 to 6.40/100 thousand in 2019, with a slight decrease in 2020 and 2021. The average annual incidence rate was 3.47/100 thousand. In terms of regional distribution, the hepatitis C incidence rate in Shandong Province was generally high in the west and low in the east. It is estimated that the hepatitis C incidence rate in Shandong Province will be 9.21 per 100 thousand in 2030. CONCLUSION: The hepatitis C incidence rate in Shandong Province showed an increasing trend from 2004 to 2019 and a decreasing trend in 2020 and 2021. Significant regional variations in incidence rate existed. An upward trend in incidence rate is predicted from 2022 to 2030. It is necessary to strengthen the prevention and control of hepatitis C to achieve the goal of eliminating viral hepatitis by 2030.

Broiler Mobility Assessment via a Semi-Supervised Deep Learning Model and Neo-Deep Sort Algorithm.

Mobility is a vital welfare indicator that may influence broilers' daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model's mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers' hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.

Solar Efficiency of Azo-Photoswitches for Energy Conversion: A Comprehensive Assessment.

Photoswitches can absorb solar photons and store them as chemical energy by photoisomerization, which is regarded as a promising strategy for photochemical solar energy storage. Although many efforts have been devoted to photoswitch discovery, the solar efficiency, a critical fundamental parameter assessing the solar energy conversion ability, has attracted little attention and remains to be studied comprehensively. Here we provide a systematic evaluation of the solar efficiency of typical azo-switches including azobenzenes and azopyrazoles, and gain a comprehensive understanding on its decisive factors. All the efficiencies are found below 1.0 %, far from the proposed limits for molecular solar thermal energy storage systems. Azopyrazoles exhibit remarkably higher solar efficiencies (0.59-0.94 %) than azobenzenes (0.11-0.43 %), benefiting from largely improved quantum yield and photoisomerization yield. Light filters can be used to improve the isomerization yield but inevitably narrow the usable range of solar spectrum, and these two contradictory effects ultimately reduce solar efficiencies. We envision this conflict could be resolved through developing azo-switches that afford high isomerization yields by absorbing wide-spectrum solar energy. We hope this work could promote more efforts to improve the solar efficiency of photoswitches, which is highly relevant to the prospect for future applications.

Code-switching costs from Chinese-English relative clauses processing.

Introduction: The source of costs is a primary concern in code-switching, yet a consensus has not yet been reached. This study investigates whether code-switching during syntactic processing in Chinese-English dual languages results in a cost. Methods: We use Chinese and English relative clauses in either object (Experiment 1) or subject (Experiment 2, which has a more complex structure) positions to test the costs in syntactic processing. Forty-seven Chinese-English bilinguals and 17 English-Chinese bilinguals participated in acceptability judgment tests and self-paced reading experiments. Results: The statistical findings indicate that syntactic processing is a source of the costs incurred in code-switching, as evidenced by the code-switching costs observed in the head movement during relative clause comprehension. Discussion: The outcomes are consistent with the implications of the 4-Morpheme Model and the Matrix Language Framework. Additionally, the experiment shows that the processing of relative clauses depends on the underlying structures, which is consistent with the Dependency Locality Theory.

Stratifying the risks of venous thromboembolism in the lateral skull base surgery using Caprini risk assessment model.

PURPOSE: To analyze and stratify the possible risk factors of venous thromboembolism (VTE) in lateral skull base surgery (LSBS) using the Caprini risk assessment model. METHODS: In a single center, a retrospective study was conducted with patients who underwent LSBS from June 2016 to August 2021. The clinical characteristics and blood chemistry tests were collected. The incidence of VTE within 30 days of surgery was recorded. The Caprini risk score was calculated to assess the postoperative VTE risk. RESULTS: Among the 123 patients in this study, the VTE incidence within 30 postoperative days was 8.9%. The total Caprini risk score in VTE patients (5.6 ± 1.9 points) was significantly higher than that of non-VTE patients (4.6 ± 1.4 points; p = 0.028). The binary logistic regression showed the total Caprini score as the only independent indicator of postoperative VTE. The receiver operating characteristic curve analysis showed that the Caprini score at 6.5 points had low sensitivity (36.4%) but high specificity (91.1%), with the largest area under the curve being 0.659. The VTE rate was significantly higher in patients with a total Caprini score ≥ 7 points (28.6%) compared to those with a total Caprini score ≤ 6 points (7.3%; p = 0.022). CONCLUSION: LSBS patients have a high risk of developing postoperative VTE. Patients with a Caprini score ≥ 7 points had a significantly higher risk of developing VTE after LSBS. The Caprini risk system was useful in assessing the VTE risk in LSBS. However, more data, calibration, and validation are necessary to establish an exclusive Caprini risk system for LSBS.

The interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention for floating population in China.

Limited studies examined the interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention of floating population. We used binary logistic model to examine the association of PM2.5, per capita GDP (PGDP), PM2.5 [Formula: see text] PGDP on the settlement intention. Additive interaction term of PM2.5 and PGDP level was used to investigate their interactive effects. Overall, each one grade increment in annual average PM2.5 was associated with decreased probability of settlement intention (OR = 0.847, 95%CI: 0.811-0.885). The interaction effect between PM2.5 and PGDP on settlement intention was significant (OR = 1.168, 95%CI: 1.142-1.194). The stratified analysis showed PM2.5 exhibits lower settlement intention in the aged 55 years or above, engaged in low-skilled works, and living in the western China. This study indicates that PM2.5-exposed will decrease the settlement intention of floating population. High economic development level can weaken the relationship between PM2.5 and settlement intention. Policymakers should balance the socio-economic development and environmental health and focus on vulnerable population.

Learning Deep Blind Quality Assessment for Cartoon Images.

Although the cartoon industry has developed rapidly in recent years, few studies pay special attention to cartoon image quality assessment (IQA). Unfortunately, applying blind natural IQA algorithms directly to cartoons often leads to inconsistent results with subjective visual perception. Hence, this brief proposes a blind cartoon IQA method based on convolutional neural networks (CNNs). Note that training a robust CNN depends on manually labeled training sets. However, for a large number of cartoon images, it is very time-consuming and costly to manually generate enough mean opinion scores (MOSs). Therefore, this brief first proposes a full reference (FR) cartoon IQA metric based on cartoon-texture decomposition and then uses the estimated FR index to guide the no-reference IQA network. Moreover, in order to improve the robustness of the proposed network, a large-scale dataset is established in the training stage, and a stochastic degradation strategy is presented, which randomly implements different degradations with random parameters. Experimental results on both synthetic and real-world cartoon image datasets demonstrate the effectiveness and robustness of the proposed method.

MetaMP: Metalearning-Based Multipatch Image Aesthetics Assessment.

Image aesthetics assessment (IAA) is a subjective and complex task. The aesthetics of different themes vary greatly in content and aesthetic results, whether they are in the same aesthetic community or not. In aesthetic evaluation tasks, the pretrained network with direct fine-tune may not be able to quickly adapt to tasks on various themes. This article introduces a metalearning-based multipatch (MetaMP) IAA method to adapt to various thematic tasks quickly. The network is trained based on metalearning to obtain content-oriented aesthetic expression. In addition, we design a complete-information patch selection scheme and a multipatch (MP) network to make the fine details fit the overall impression. Experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art models based on aesthetic visual analysis (AVA) benchmark datasets. In addition, the evaluation of the dataset shows the effectiveness of our metalearning training model, which not only improves MetaMP assessment accuracy but also provides valuable guidance for network initialization of IAA.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC.

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (ßinteraction  = 0.018, P = 1.87 × 10-12 ), which mapped to RELA × HLA-G (ßinteraction  = 0.218, P = 8.82 × 10-11 ) and cg08872738 × cg27077312 (ßinteraction  = -0.010, P = 1.16 × 10-11 ), which mapped to TUBA1B × TOMM40 (ßinteraction =-0.250, P = 3.83 × 10-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

The Impact of Green Innovation on Enterprise Green Economic Efficiency.

In the process of China's transformation from high-speed to high-quality development, the role of green innovation has gradually begun to receive attention. Using 2163 observations of 687 listed companies from 2016 to 2020, this paper examined whether green innovation can improve green economic efficiency. The study found that green innovation significantly reduces the green economic efficiency of enterprises. In the case of insufficient protection of innovation achievements, limited knowledge and technology accumulation, and the absence of enterprise engagement in heavily polluting production and operation activities, the negative impact of green innovation on their green economic efficiency is more significant. Moreover, upgraded production processes, a high degree of freedom of technology selection and high market competition can help alleviate the negative impact of green innovation on enterprises' green economic efficiency, and when the above three conditions are simultaneously met, green innovation significantly promotes the green economic efficiency of enterprises. The above findings are contradictory to the assumption of most literature intuitively. However, after a series of tests, this paper found that green innovation can still stimulate overall environmental and economic performance in some conditions. Starting from the microenterprise level and based on actual emissions data, this paper examines whether and how green innovation affects high-quality development. The findings are of great significance to academic research, policy formulation, and enterprise production and operation.

Multimorbidity and out-of-pocket expenditure for medicines in China and India.

INTRODUCTION: Using nationally representative survey data from China and India, this study examined (1) the distribution and patterns of multimorbidity in relation to socioeconomic status and (2) association between multimorbidity and out-of-pocket expenditure (OOPE) for medicines by socioeconomic groups. METHODS: Secondary data analysis of adult population aged 45 years and older from WHO Study on Global Ageing and Adult Health (SAGE) India 2015 (n=7397) and China Health and Retirement Longitudinal Study (CHARLS) 2015 (n=11 570). Log-linear, two-parts, zero-inflated and quantile regression models were performed to assess the association between multimorbidity and OOPE for medicines in both countries. Quantile regression was adopted to assess the observed relationship across OOPE distributions. RESULTS: Based on 14 (11 self-reported) and 9 (8 self-reported) long-term conditions in the CHARLS and SAGE datasets, respectively, the prevalence of multimorbidity in the adult population aged 45 and older was found to be 63.4% in China and 42.2% in India. Of those with any long-term health condition, 38.6% in China and 20.9% in India had complex multimorbidity. Multimorbidity was significantly associated with higher OOPE for medicines in both countries (p<0.05); an additional physical long-term condition was associated with a 18.8% increase in OOPE for medicine in China (p<0.05) and a 20.9% increase in India (p<0.05). Liver disease was associated with highest increase in OOPE for medicines in China (61.6%) and stroke in India (131.6%). Diabetes had the second largest increase (China: 58.4%, India: 91.6%) in OOPE for medicines in both countries. CONCLUSION: Multimorbidity was associated with substantially higher OOPE for medicines in China and India compared with those without multimorbidity. Our findings provide supporting evidence of the need to improve financial protection for populations with an increased burden of chronic diseases in low-income and middle-income countries.

Provincial heterogeneity in the management of care cascade for hypertension, diabetes, and dyslipidaemia in China: Analysis of nationally representative population-based survey.

Background: This study aims to examine (1) province-level variations in the levels of cardiovascular disease (CVD) risk and behavioral risk for CVDs, (2) province-level variations in the management of cascade of care for hypertension, diabetes, and dyslipidaemia, and (3) the association of province-level economic development and individual factors with the quality of care for hypertension, diabetes, and dyslipidaemia. Methods: We used nationally representative data from the China Health and Retirement Longitudinal Study in 2015, which included 12,597 participants aged 45 years. Using a care cascade framework, we examined the quality of care provided to patients with three prevalent NCDs: hypertension, diabetes, and dyslipidaemia. The proportion of WHO CVD risk based on the World Health Organization CVD risk prediction charts, Cardiovascular Risk Score (CRS) and Behavior Risk Score (BRS) were calculated. We performed multivariable logistic regression models to determine the individual-level drivers of NCD risk variables and outcomes. To examine socio-demographic relationships with CVD risk, linear regression models were applied. Results: In total, the average CRS was 4.98 (95% CI: 4.92, 5.05), while the average BRS was 3.10 (95% confidence interval: 3.04, 3.15). The weighted mean CRS (BRS) in Fujian province ranged from 4.36 to 5.72 (P < 0.05). Most of the provinces had a greater rate of hypertension than diabetes and dyslipidaemia awareness and treatment. Northern provinces had a higher rate of awareness and treatment of all three diseases. Similar patterns of regional disparity were seen in diabetes and dyslipidaemia care cascades. There was no evidence of a better care cascade for CVDs in patients who reside in more economically advanced provinces. Conclusion: Our research found significant provincial heterogeneity in the CVD risk scores and the management of the cascade of care for hypertension, diabetes, and dyslipidaemia for persons aged 45 years or more. To improve the management of cascade of care and to eliminate regional and disparities in CVD care and risk factors in China, local and population-based focused interventions are necessary.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.