BACKGROUND: Urban-rural disparities in medical care, including in home healthcare, persist globally. With aging populations and medical advancements, demand for home health services rises, warranting investigation into home healthcare disparities. Our study aimed to 1) investigate the relationship between rurality on home healthcare quality, and 2) assess the temporal disparities and the changes in disparities in home healthcare quality between urban and rural home health agencies (HHAs), incorporating an analysis of geospatial distribution to visualize the underlying patterns.
METHODS: This study analyzed data from HHAs listed on the Centers for Medicare and Medicaid Services (CMS) website, covering the period from 2010 to 2022. Data were classified into urban and rural categories for each HHA. We employed panel data analysis to examine the impact of rurality on home healthcare quality, specifically focusing on hospital admission and emergency room (ER) visit rates. Disparities between urban and rural HHAs were assessed using the Wilcoxon test, with results visualized through line and dot plots and heat maps to illustrate trends and differences comprehensively.
RESULTS: The rurality is demonstrated as the most significant variable in hospital admission and ER visit rates in the panel data analysis. Urban HHAs consistently exhibit significantly lower hospital admission rates and ER visit rates compared to rural HHAs from 2010 to 2022. Longitudinally, the gap in hospital admission rates between urban and rural HHAs is shrinking, while there is an increasing gap in ER visit rates. In 2022, HHAs in Mountain areas which are characterized by a higher proportion of rural regions exhibited higher hospital admission and ER visit rates than other areas.
CONCLUSIONS: This study underscores the persistent urban-rural disparities in home healthcare quality. The analysis emphasizes the ongoing need for targeted interventions to address disparities in home healthcare delivery and ensure equitable access to quality care across urban and rural regions. Our findings have the potential to inform policy and practice, promoting equity and efficiency in the long-term care system, for better health outcomes throughout the United States.

OBJECTIVE: The ileum is the most commonly affected segment of the gastrointestinal tract in Crohn\'s disease (CD). We aimed to determine whether disease location affects response to filgotinib, a Janus kinase (JAK) inhibitor, in patients with moderate-to-severely active Crohn\'s disease (CD) and applying appropriate methods to account for differences in measuring disease activity in the ileum compared to the colon.
METHODS: This post-hoc analysis of data from the FITZROY phase 2 trial (NCT02048618) compared changes in the Crohn\'s Disease Activity Index (CDAI) and Simple Endoscopic Score for Crohn\'s Disease (SES-CD) amongst patients with ileal-dominant and isolated colonic CD treated with 10 weeks of filgotinib 200 mg daily or placebo. A mixed effects model for repeated measures was used to test whether ileal disease responded differently than colonic disease, by evaluating for effect modification using the interaction term of treatment assignment-by-disease location.
RESULTS: Numerically greater proportions of patients with isolated colonic disease compared to ileal-dominant CD achieved clinical remission (CDAI <150, 75.9% vs. 41.6%) and endoscopic response (SES-CD reduction by 50%, 52.5% vs. 15.5%) at Week 10. However, after adjusting for baseline disease activity by disease location and within-patient clustering effects, there was no significant difference in treatment response by disease location (mean difference in ΔCDAI between ileal-dominant vs. isolated colonic disease +9.24 [95% CI: -87.19, +105.67], p=0.85; mean difference in ΔSES-CD -1.93 [95% CI: -7.03, +3.44], p=0.48).
CONCLUSIONS: Filgotinib demonstrated similar efficacy in ileal-dominant and isolated colonic CD when controlling for baseline disease activity and clustering effects.

Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.

暂无摘要。

Braking systems are extremely important in any vehicle. They convert the kinetic energy of motion into thermal energy that is dissipated into the atmosphere. Different vehicle groups have different nominal and maximum speeds and masses, so the amount of thermal energy that needs to be absorbed by the friction pads and then dissipated can vary significantly. Conventional friction materials are composite materials capable of withstanding high temperatures (in the order of 500-600 °C) and high mechanical loads resulting from braking intensity and vehicle weight. In small vehicles traveling at low speeds, where both the amount of thermal energy and its density are limited, the use of slightly weaker friction materials with better ecological properties can be considered. This work proposes a prototype composite friction material using flax fibers as reinforcement instead of the commonly used aramid. A number of samples were prepared and subjected to laboratory tests. The samples were prepared using components of plant origin, specifically flax fibers. This component acted as reinforcement in the composite friction material, replacing aramid commonly used for this purpose. The main tribological characteristics were determined, such as the values of the coefficients of friction and the coefficients of abrasive wear rate. For this purpose, an authorial method using ball-cratering contact was used. The results were analyzed using statistical methods. It was found that the composite material using flax fibers does not differ significantly in its tribological properties from conventional solutions; so, it can be assumed that it can be used in the vehicle\'s braking system.

UNASSIGNED: Maternal nutrient intake may moderate associations between environmental exposures and children\'s neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores.
UNASSIGNED: We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009-2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 μm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children\'s autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months).
UNASSIGNED: Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores.
UNASSIGNED: In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data (SISAQOL) initiative was established in 2016 to assess the quality and standardization of patient-reported outcomes (PRO) data analysis in randomized controlled trials (RCTs) on advanced breast cancer. The initiative identified deficiencies in PRO data reporting, including nonstandardized methods for handling missing data. This study evaluated the reporting of health-related quality of life (HRQOL) in Japanese cancer RCTs to provide insights into the state of PRO reporting in Japan. The study reviewed PubMed articles published from 2010 to 2018. Eligible studies included Japanese cancer RCTs with ≥50 adult patients (≥50% were Japanese) with solid tumors receiving anticancer treatments. The evaluation criteria included clarity of the HRQOL hypotheses, multiplicity testing, primary analysis methods, and reporting of clinically meaningful differences. Twenty-seven HRQOL trials were identified. Only 15% provided a clear HRQOL hypothesis, and 63% examined multiple HRQOL domains without adjusting for multiplicity. Model-based methods were the most common statistical methods for the primary HRQOL analysis. Only 22% of the trials explicitly reported clinically meaningful differences in HRQOL. Baseline assessments were reported in most trials, but only 26% reported comparisons between the treatment groups. HRQOL analysis was based on the intention-to-treat population in 19% of the trials, and 74% reported compliance at follow-up; however, 41% did not specify how missing values were handled. Although the rates of reporting clinical hypotheses and clinically meaningful differences were relatively low, the current state of HRQOL evaluation in the Japanese cancer RCT appears comparable to that of previous studies.

OBJECTIVE: Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based \"Automated Visual Evaluation\" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance.
METHODS: Prospective study.
METHODS: Eight public health facilities in Zambia.
METHODS: A total of 8204 women aged 25-55.
METHODS: Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive.
METHODS: Area under the receiver operating curve (AUC); sensitivity; specificity.
RESULTS: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden\'s index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91).
CONCLUSIONS: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).

BACKGROUND: Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate.
OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR).
METHODS: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design.
RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption.
CONCLUSIONS: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.