The synergistic evolution of supply-demand composite system for airport green development: A case study in Guangzhou Baiyun International Airport, China.

Given the pressing requirements for sustainable development in civil aviation, conducting a synergistic evolution analysis of the supply and demand aspects in the airport green development holds great significance. This analysis helps achieve sustainable airport development and facilitates the green transformation of civil aviation development. Taking a collaborative learning approach and utilizing historical data from Guangzhou Baiyun International Airport spanning 2008 to 2019, the supply-demand composite system for airport green development was deconstructed into two subsystems-demand and supply-and relevant evaluation index systems were established in this paper. A screening and optimization model of supply and demand synergy indicators for airport green development was constructed, and it was solved using a simulated annealing genetic algorithm. The Haken model was constructed to analyze the synergistic evolutionary relationship of the composite system of supply and demand for green airport development in two stages. The results indicate a shift in the order parameter of the co-evolution of the supply-demand composite system at Guangzhou Baiyun International Airport, moving from the demand subsystem in the first stage (2008-2015) to the supply subsystem in the second stage (2016-2019). The co-evolution of the airport supply-demand composite system has entered a new stage, but has not reached a high level of synergy. The study not only contributes theoretically by explaining the interaction mechanism between supply and demand for airport green development, but also offers targeted suggestions for achieving high-quality synergistic evolution of supply and demand for airport green development.

Planned expansion of transportation infrastructure in Brazil has implications for the pattern of agricultural production and carbon emissions.

High transportation costs have been a barrier to the expansion of agriculture in the interior of Brazil. To reduce transportation costs, Brazil launched the National Logistics Plan, aiming to expand its railway network by up to 91 % by 2035. Such a large-scale infrastructure investment raises concerns about its economic and environmental consequences. By combining geospatial estimation of transportation cost with a grid-resolving, multi-scale economic model that bridges fine-scale crop production with its trade and demand from national and global perspectives, we explore impacts of transportation infrastructure expansion on agricultural production, land use changes, and carbon emissions both locally and nationally in Brazil. We find that globally, the impacts on output and land use changes are small. However, within Brazil, the plan's primary impacts are impressive. PNL2035 results in the reduction of transportation costs by 8-23 % across states (depending on expansion's extent) in the interior Cerrado biome. This results in cropland expansion and increases in terrestrial carbon emissions in the Cerrado region. However, the increase in terrestrial carbon emissions in the Cerrado is offset by spillover effects elsewhere in Brazil, as crop production shifts away from the Southeast-South regions and accompanying change in the mix of transportation mode for farm products from roadway to more emission-efficient railway. Furthermore, we argue that the transportation infrastructure's impact on the enhanced mobility of labor and other agricultural inputs would further accentuate the regional shift in agricultural production and contribute to carbon emission mitigation. Upon its completion, PNL2035 is expected to result in the reduction of net national emissions by 1.8-30.7 million metric ton of CO2-equivalent, depending on the impacts on labor and purchased input mobility. We conclude that the omission of spillover effects due to infrastructure expansion can lead to misleading assessments of transport policies.

[Energy-saving and Emission Reduction Path for Road Traffic in Key Coastal Cities of Guangdong, Fujian and Zhejiang].

The rapid development of society and economy has resulted in a substantial increase in energy consumption, consequently exacerbating pollution issues. Current research predominantly focuses on energy-saving and emission reduction in road transportation within individual cities or the three major economic regions of China:the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei Region. However, there is a dearth of studies addressing the southeastern coastal economic region. Located at the heart of China's southeastern coastal economic development, the provinces of Guangdong, Fujian, and Zhejiang unavoidably face challenges associated with energy consumption and emissions while pursuing economic growth. To address these challenges, this study employed a LEAP model to construct various scenarios for road transportation in the key coastal cities of Guangdong, Fujian, and Zhejiang from 2015 to 2035. These scenarios included a baseline scenario (BAU), an existing policy scenario (EPS), and an improved policy scenario (MPS). The MPS and EPS encompassed vehicle structure optimization (VSO), improved fuel economy (IFE), and reduced annual average mileage (RDM). By simulating and evaluating these scenarios, the energy-saving and emission reduction potentials of road transportation in the key coastal cities were assessed. The results indicated that, in the primary scenario, the MPS exhibited the most significant improvements in energy-saving, carbon reduction, and pollutant reduction effects. By 2035, the MPS achieved a remarkable 75% energy-saving rate compared to that in the baseline scenario, accompanied by reductions of 68%, 59%, 66%, 70%, and 64% in CO2, CO, NOx, PM2.5, and SO2 emissions, respectively. In the secondary scenario, the improved scenario of enhancing fuel economy achieved a notable 30% reduction in energy consumption. Additionally, the scenarios involving vehicle structure adjustment (yielding reductions of 36%, 30%, 36%, 26%, and 40%) and annual average mileage reduction (resulting in reductions of 37%, 37%, 36%, 37%, and 36%) demonstrated significant reductions in CO2, CO, NOx, PM2.5, and SO2 emissions.

The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting structural brain abnormalities in cognitive impairments-- case controlled study.

BACKGROUND: Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, we propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). METHODS: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or magnetic resonance imaging (MRI) scans within 24 hours of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), we evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. RESULTS: A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3 to 5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and 3 control subjects developed incident CI. The CT inter-observer reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI 2.312-7.237, P=0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. CONCLUSION: Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Carbon tax and low-carbon credit: Which policy is more beneficial to the capital-constrained manufacturer's remanufacturing activities?

Remanufacturing has attracted much attention for its enormous potential in resource recycling and low-carbon emission reduction. To investigate the effects of different government intervention policies on remanufacturing and carbon emissions, two profit maximization models of the capital-constrained manufacturer under carbon tax and low-carbon credit policies are constructed respectively. Then, through theoretical and numerical analyses, some significant findings are drawn: (1) Both carbon tax and low-carbon credit policies can encourage capital-constrained manufacturers to produce more remanufactured products, but which intervention policy is more advantageous also depends on the carbon emission cost of new products or financing cost of the remanufactured products. (2) Although carbon tax policy can effectively control carbon emissions, it is always at the expense of both capital-constrained manufacturers and consumers; while low-carbon credit policy can help capital-constrained manufacturers achieve the goal of win-win economic and environmental benefits when the remanufacturing carbon savings advantages are more apparent. (3) From the perspective of consumer benefits, carbon tax is more advantageous when the consumer willingness to pay for remanufactured products is higher; otherwise, low-carbon credit policy should be implemented. (4) The higher the environmental damage coefficient is, the more it can highlight the advantages of the two intervention policies in social welfare enhancement, especially the carbon tax policy; and when the environmental damage coefficient is given, the stronger the consumers' willingness to pay for remanufactured products is, the more it is conducive to reducing the negative effects caused by the carbon tax or low-carbon credit policy in social welfare enhancement, or increasing the corresponding positive effects. Based on above findings, some managerial insights and policy implications are provided to capital-constrained manufacturers and policy-makers.

How the implementation of internal carbon pricing impacts on carbon reduction: facilitating or hindering.

A growing number of companies are incorporating internal carbon pricing into their climate management strategies, making it one of the ways in which the private sector can combat carbon emissions and respond to the climate crisis. In this study, we used the synthetic control method in an attempt to discover whether the implementation of internal carbon pricing has an impact on the carbon reduction of typical firms at an individual level. As some of the firms cannot be properly fitted, statistical approaches were then applied to the S&P 500 constituent firms to test if there is any correlation between the emission cuts and internal carbon pricing. We also conducted a channel analysis. Through examining three possible channels, internal carbon pricing is found to affect firms' carbon reduction mainly through two channels, namely the level of energy intensity and R&D investments. Among them, the energy intensity plays a positive mediating role between emission reduction and internal carbon pricing. As to another channel, contrary to expectations, internal carbon pricing, to some extent, suppresses the R&D investments which will promote carbon emission reduction. That is, the size of R&D investments has masking effects in the relationship between emission cuts and internal carbon pricing. Our findings contribute to researches on the carbon pricing, emission reduction, and climate management efforts from the private sector and have implications for practices.

Comparative stress response assessment of PFOS and its alternatives, F-53B and OBS, in wheat: An insight of toxic mechanisms and relative magnitudes.

Emerging alternatives to perfluorooctane sulfonate (PFOS), including 6:2 chlorinated polyfluorinated ether sulfonate (F-53B) and p-perfluorous nonenoxybenzene sulfonate (OBS), have been widely detected in the real environment as PFOS restriction. However, the toxicity in plants and the underlying mechanism of F-53B and OBS remain scarce, especially compared to PFOS. PFOS and their emerging alternatives pose significant potential risks to food, especially for crops, safety and human health with the great convenience of high chemical stability. Germination toxicity, oxidative stress biomarkers, and metabolomics were used to compare the relative magnitudes of toxicity of PFOS and its alternatives in wheat (Triticum aestivum L.). PFOS, F-53B, and OBS inhibited wheat germination compared to the control group, with germination inhibition rates of 45.6%, 53.5%, and 64.3% at 400 µM PFOS, F-53B, and OBS exposure, respectively. Moreover, oxidative stress biomarker changes were observed in PFOS, F-53B, and OBS, with OBS being more pronounced. The chlorophyll concentrations in wheat shoots increased, and the anthocyanin concentration decreased along with the increased exposure concentration. Superoxide dismutase (SOD) activity increased in wheat root but decreased in the shoot. Peroxidase (POD) activity and malondialdehyde (MDA) concentration increased, whereas catalase (CAT) activity decreased. Regarding metabolomics, PFOS, F-53B, and OBS exposure (10 µM) significantly altered 85, 133, and 134 metabolites, respectively. According to KEGG enrichment analysis, F-53B specifically affects lipid metabolism, whereas OBS causes an imbalance in amino acid and carbohydrate metabolism. These findings suggested that PFOS, F-53B, and OBS have distinct toxic mechanisms. Thus, our results indicated that the relative size of the toxicity in wheat is as follows: OBS > F-53B > PFOS, and this finding provides a new reference basis for the phytotoxicity assessment of F-53B and OBS.

Assessment of surgical management for locked fracture-dislocations of the proximal humerus in patients of different ages.

Background: Locked fracture-dislocation of the proximal humerus (LFDPH) is a very severe complex injury; neither arthroplasty nor internal plating are fully satisfactory. This study aimed to evaluate different surgical treatments for LFDPH to determine the optimal option for patients of different ages. Methods: From October 2012 to August 2020, patients who underwent open reduction and internal fixation (ORIF) or shoulder hemiarthroplasty (HSA) for LFDPH were retrospectively reviewed. At follow-up, radiologic evaluation was performed to evaluate bony union, joint congruence, screw cut-out, avascular necrosis of the humeral head, implant failure, impingement, heterotopic ossification, and tubercular displacement or resorption. Clinical evaluation comprised the Disability of the Arm, Shoulder, and Hand (DASH) questionnaire and Constant-Murley and visual analog scale (VAS) scores. Additionally, intraoperative and postoperative complications were assessed. Results: Seventy patients (47 women and 23 men) with final evaluation results qualified for inclusion. Patients were divided into three groups: group A: patients aged under 60 years who underwent ORIF; group B: patients aged ≥60 years who underwent ORIF; and group C: patients who underwent HSA. At a mean follow-up of 42.6 ± 26.2 months, function indicators, namely shoulder flexion, and Constant-Murley and DASH scores, in group A were significantly better than those in groups B and C. Function indicators in group B were slightly but not significantly better compared with group C. Regarding operative time and VAS scores, there were no significant differences between the three groups. Complications occurred in 25%, 30.6%, and 10% of the patients in groups A, B, and C, respectively. Conclusions: ORIF and HSA for LFDPH provided acceptable but not excellent results. For patients aged <60 years, ORIF might be optimal, whereas, for patients aged ≥60 years, both ORIF and HSA provided similar results. However, ORIF was associated with a higher rate of complications.

Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.

Background and objectives: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. Methods: The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. Results: For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Conclusion: Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.

Tailoring Rule-Based Data Quality Assessment to the Patient-Centered Outcomes Research Network (PCORnet) Common Data Model (CDM).

Individual researchers and research networks have developed and applied different approaches to assess the data quality of electronic health record (EHR) data. A previously published rules-based method to evaluate the data quality of EHR data provides deeper levels of data quality analysis. To examine the effectiveness and generalizability of the rule-based framework, we reprogrammed and translated published rule templates to operate against the PCORnet Common Data Model and executed them against a database for a single center of the Greater Plains Collaborative (GPC) PCORnet Clinical Research Network. The framework detected additional data errors and logical inconsistencies not revealed by current data quality procedures. Laboratory and medication data were more vulnerable to errors. Hemolyzed samples in the emergency department and metformin prescribing in ambulatory clinics are further described to illustrate application of specific rule-based findings by researchers to engage their health systems in evaluating healthcare delivery and clinical quality concerns.

COVID-19 and financial market response in China: Micro evidence and possible mechanisms.

This paper uses event study based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to study the impact of the COVID-19 outbreak on China's financial market. It finds that the pandemic had an overall significant and negative impact on the stock prices of firms listed on SSE, SZSE and ChiNext. However, such impact appeared to be heterogeneous across industries, affecting listed firms in industries such as pharmaceutical and telecommunications positively, but those in services industries such as accommodation, catering, and commercial services negatively. Apparently, a crisis for some had been an opportunity for others. In addition, this paper seeks to understand the micro mechanism behind the heterogeneity of pandemic shock from the perspective of firms' financial position. It finds that listed firms with higher debt level were hit harder, whereas those with more net cash flow had displayed higher resilience against the blow of the pandemic. However, the opposite pattern is found among those listed on ChiNext and in industries severely devastated by the pandemic. These findings have policy implications in terms of preventing systemic financial risks and facilitating recovery during pandemic-induced economic downturns. It also helps investor adjust investment strategies, hedge against risks, and secure gains when the market conditions in general are unfavorable.

Visible Light-Driven GO/TiO2-CA Nano-Photocatalytic Membranes: Assessment of Photocatalytic Response, Antifouling Character and Self-Cleaning Ability.

Photocatalysis and membrane technology in a single unit is an ideal strategy for the development of wastewater treatment systems. In this work, novel GO (x wt%)/TiO2-CA hybrid membranes have been synthesized via a facile non-solvent induced phase inversion technique. The strategy aimed to address the following dilemmas: (1) Effective utilization of visible light and minimize e-/h+ recombination; (2) Enhanced separation capability and superior anti-fouling and self-cleaning ability. The experimental results reveal that the integration of nano-composite (GO/TiO2) boosts the membrane properties when compared to pristine CA and single photocatalyst employed membrane (GO-CA and TiO2-CA). The effect of GO content on the properties of the photocatalytic membrane has been determined by utilizing three different ratios of GO, viz. 0.5 wt%, 1 wt%, and 2 wt% designated as NC(1)-CA, NC(2)-CA, and NC(3)-CA, respectively. Amongst them, NC(3)-CA membrane showed state-of-the-art performance with an elevated photocatalytic response (four times higher than pristine CA membrane) toward methyl orange. Moreover, the water flux of NC(3)-CA membrane is 613 L/m2h, approximately three times higher than bare CA membrane (297 L/m2h), while keeping the MO rejection high (96.6%). Besides, fouling experiments presented the lowest total and fouling resistance ratios and a higher flux recovery ratio (91.78%) for the NC(3)-CA membrane, which endows the membrane with higher anti-fouling and self-cleaning properties. Thus, NC(3)-CA membrane outperforms the other as synthesized membranes in terms of separation efficiency, visible light photo-degradation of pollutant, anti-fouling and self-cleaning ability. Therefore, NC(3)-CA membrane is considered as the next generation membrane for exhibiting great potential for the wastewater treatment applications.

The atmospheric lead emission, deposition, and environmental inequality driven by interprovincial trade in China.

Lead (Pb) as a hazardous air pollutant has raised widespread concerns due to its adverse and toxic effects on the ecological environment and human health. Here we integrated the multi-regional input-output (MRIO) model and an atmospheric transport model to examine regional environmental inequality (REI) index induced by Pb emission transfers, and to evaluate the impacts of interprovincial trade on regional atmospheric Pb concentrations and dry deposition fluxes in China in 2012. In 2012, approximately 57.4% ~ 72.6% of Pb emissions in well-developed eastern regions (Beijing-Tianjin, Yangtze River Delta (YRD)) and the southern seaboard of China were embodied in other regions in China subject to the demands from these well-developed regions to industrial products and services. Our results, based on the net virtual flows of Pb emission and value-added, indicate that most provinces in the eastern seaboard of China outsource Pb emission and benefit from the interprovincial trade by reducing their Pb emissions. REI indexes show that the well-developed Guangdong province outsources its Pb emission but has low economic gains. Many less-developed provinces in central China enhance virtual Pb emission inflow but have high economic gains. Whereas, inland provinces in western China not only experience Pb emission increase, but also suffer from indirect economic loss due to trade with well-developed provinces to meet their increasing demands to Pb emission abundant industrial products from these provinces in eastern China which are mostly provided by less-developed but energy and mineral product abundant provinces in western China. For example, the province pair with highest REI index was Jiangsu-Inner Mongolia (REI = 2.47), which revealed that Jiangsu was the largest beneficiary which exported 37.2 t of net Pb emission and gained value-added of 521.4 billion RMB through trade with Inner Mongolia which suffered from both virtual Pb inflow and economic loss in 2012. As a result of interprovincial trade, Pb dry deposition in central and eastern China was decreased but increased in western China. Overall, interprovincial trade reduced 17.6% of atmospheric Pb dry deposition in China.

Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study.

BACKGROUND: Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD. OBJECTIVE: We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD. METHODS: Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system. RESULTS: High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F1,103=14.1, P<.001) and stride time variability (F1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: ß=.39, P<.001; dual task: ß=.37, P=.01), HAM-A (single-task: ß=.49, P=.007; dual-task: ß=.48, P=.009), and HAM-D (single task: ß=.44, P=.01; dual task: ß=.49, P=.009). Moreover, those with greater dual-task stride time variability (ß=.48, P=.001) or dual-task cost of stride time variability (ß=.44, P=.004) exhibited lower MoCA scores. CONCLUSIONS: A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.

A Rule-Based Data Quality Assessment System for Electronic Health Record Data.

OBJECTIVE: Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners. METHODS: We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring. RESULTS: We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information. DISCUSSION: The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set. CONCLUSION: This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.

Rules Based Data Quality Assessment on Claims Database.

Data quality problems in coded clinical and administrative data have persisted ever since diagnoses and procedures were first coded and used for healthcare billing. These data are used in clinical decision-making introducing a route for iatrogenesis. As we share data on regional Health Information Exchanges (HIEs) and include them in electronic health records the potential for harm may be increased. To study this problem we applied rules-based data quality checks that have been previously tested on Electronic Health Records (EHR) data on a limited set of aggregated claims data. Medicaid claims data was used exclusively. CMS has clear guidelines for claims submitted for Medicaid patients and penalties are incurred for erroneous claims, which should ensure a high quality data source, however reports of low and varying sensitivity, specificity, positive and negative predictive value of coded diagnoses are common. To identify data quality defects in claims data in a state All Payer Claims Dataset (APCD) we applied and evaluated a recently developed rules-based data quality assessment and monitoring system for Electronic Health Record (EHR) data to test effectiveness in claims data. These rules, that are feasible for "All Payer Claims data" and Medicaid data are identified, applied and the Data Quality issue results are produced.

Effect of small vessel disease burden and lacunes on gait/posture impairment in Parkinson's disease.

INTRODUCTION: The comorbidity of cerebral small vessel disease (CSVD) may worsen gait impairment of Parkinson's disease (PD). However, the evidence remains scarce and controversial, and the mechanism of their potential interaction remains largely unknown. The present study aimed to investigate the overall impact of quantity and location of CSVD on gait/posture function in PD. METHODS: This cross-sectional study included 315 consecutive eligible patients with PD from Beijing Tiantan Hospital from May 2016 to August 2018. Associations of gait/posture subscores with the burden score of CSVD and four CSVD imaging markers were assessed using multivariate linear regression models. RESULTS: Burden of CSVD was significantly associated with more severe gait/posture impairment in PD in the unadjusted model (ß = 0.521, P = 0.011, 95% CI 0.118-0.923) and in the model adjusted for age, hypertension, ischemic stroke, low-density lipoprotein level, cholesterol level, and cognitive statues (ß = 0.448, P = 0.047, 95% CI 0.006-0.891). The presence of lacunes, but not other CSVD markers, was significantly associated with higher gait/posture subscores after the adjustment (ß = 0.492, P = 0.041, 95% CI 0.021-0.964), and the number of lacunes in the basal ganglia significantly correlated with the gait/posture subscores in patients with PD (P = 0.012, Spearman r = 0.161). CONCLUSIONS: CSVD and lacunes in the basal ganglia may independently contribute to gait/posture dysfunction in PD. Promoting neurovascular health may preserve some gait/posture function of PD.

What to Do With Renal Cysts in Children?

OBJECTIVE: To explore the current practice patterns for the management of renal cysts internationally among pediatric urologists and nephrologists. MATERIALS AND METHODS: A survey composed of 21 questions and 4 clinical scenarios was distributed to pediatric urologists and nephrologists. Survey questions evaluated optimal imaging modality, management, and follow-up period. Interspecialty comparisons were made using chi-square analysis where appropriate. RESULTS: A total of 183 respondents completed the survey (128 pediatric urologists, 37 pediatric nephrologists, and 19 other specialists). Most (57%) respondents agreed or strongly agreed with using an ultrasound based Bosniak classification to categorize renal cysts in children. The most commonly used follow-up intervals were 6-12 months for pediatric urologists and 1-2 years for pediatric nephrologists. Symptomatic mass effect (80.9%), gross hematuria (79.2%), or family history were the most common reasons for escalating surveillance. Pediatric nephrologists were more likely to increase follow-up with development of bilateral simple renal cysts (P = .008) or chronic kidney disease (P = .027) when compared to pediatric urologists. Laparoscopic marsupialization (39.4%) was the most common treatment for a simple renal cyst in a symptomatic child. Modified Bosniak III cysts had more heterogeneity in treatment based on the physician responses. CONCLUSION: There is currently no consensus on the optimal protocol for the surveillance, imaging, or treatment of renal cysts in children. Most respondents agree that using an ultrasound-based Bosniak classification is reasonable. A call to action is therefore necessary for the development of registries and guidelines on the management of pediatric renal cysts and their associated malignancies.

Attributable risk and economic cost of hospital admissions for mental disorders due to PM2.5 in Beijing.

BACKGROUND: Few studies have estimated the attributable risk and economic cost of mental disorders (MDs) due to particulate matters with aerodynamic diameter of <2.5 µm (PM2.5) exposure in Beijing. OBJECTIVES: This study aims to identify the possible correlation between PM2.5 and risk of hospital admissions (HAs) for MDs in Beijing and calculate the attributable risk and economic cost. METHODS: A generalized additive model (GAM) with controlling for time trend, meteorological conditions, holidays and day of the week was used to estimate the associations. Stratified analyses were performed by age, gender and season. We further estimated the health and economic burden of HAs for MDs attributable to PM2.5. FINDINGS: A total of 17,252 HAs for MDs were collected. A 10 µg/m3 daily increase in PM2.5 was associated with a statistically significant risk increase of 3.55% for HAs for MDs. The effects of PM2.5 exposures on HAs for MDs were more pronounced in males, elderly (≥65 years old) individuals and in cold seasons. Using WHO's air quality guidelines as the reference, 15.12% of HAs and 16.19% of the related medical expenses for MDs were attributed to PM2.5 during the study period. NOVELTY: PM2.5 accounts for substantial morbidity and economic burden of MDs for both the society and households, which shows environmental protections are essential to improve mental health status of the population.