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Background: Treatment burden is a patient-centred, dynamic concept. However, longitudinal data on the changing pattern of treatment burden among patients with one or more long-term conditions (LTCs) are relatively scanty. We aimed to explore the longitudinal trajectories of treatment burden and associated risk factors in a large, patient population in primary care settings. Methods: We analysed data from 5573 primary care patients with long-term conditions (LTCs) recruited using a multistage sampling method in Shenzhen, southern China. The treatment burden was assessed by the Mandarin Chinese version of the Treatment Burden Questionnaire (TBQ). We used latent class growth mixture modelling (LCGMM) to determine trajectories of treatment burden across four time points, ie, at baseline, and at 6, 12, and 18 months. Predictors of trajectory classes were explored using multivariable logistic regression analysis. Results: The mean TBQ scores of patients with a single LTC (n = 2756), 2 LTCs (n = 1871), 3 LTCs (n = 699), and ≥ 4 LTCs (n = 247) were 18.17, 20.28, 21.32, and 26.10, respectively, at baseline. LCGMM identified three discrete classes of treatment burden trajectories over time, ie, a high-increasing class, a low-stable class, and a high-decreasing class. When controlling for individual-level factors including age, education, monthly household income per head, smoking, alcohol consumption, and attendance in health education, patients who had a clinical diagnosis of 3 LTCs (adjusted odds ratio [aOR] = 1.49, 95% CI = 1.21– 1.86, P < 0.001) or ≥ 4 LTCs (aOR = 1.97, 95% CI = 1.44– 2.72, P < 0.001) were more likely to belong to the high-increasing class. Sensitivity analysis using propensity score methods obtained similar results. Conclusion: Our study revealed the presence of discrete patterns of treatment burden over time in Chinese primary care patients with LTCs, providing directions for tailored interventions to optimise disease management. Patients with 3 or more LTCs should receive close attention in healthcare delivery as they tend to experience a greater treatment burden. [ABSTRACT FROM AUTHOR]

China has launched a general practice (GP)-orientated primary care reform in 2009 to develop a more productive, coordinated, and cost-effective system to maintain and improve the health and wellbeing of one-fifth of the world population. The restructure of the health care system with a focus on primary care requires practitioners working on GP as gatekeepers for service delivery that is responsive to the needs of people. It is particularly prioritised to establish a sound education and training system to ensure that the competencies of practitioners are aligned with local health care needs. This article aims to provide a brief review of the development of GP, including exemplary model of education and training currently implemented in southern China, as well as the challenges to be addressed in the next step. There is a shortage of well-trained and qualified general practitioners in China where more than half of the licensed clinicians in primary care are educated below the undergraduate level. Although there is a stepwise increase in recognition that the capacity of GP is pivotal to the success of primary care development in China, challenges coming from resource restriction, rural and urban disparity, social attitude, and community involvement are highlighted as major bottlenecks that currently hinder the rapid development of GP in China. Supportive policy and guidelines are necessary to build up strong GP recognition and ensure adequate resources to underpin a robust primary care system to deliver affordable and effective health care services for the world’s largest population. It might share some similar experiences with other countries that are struggling to develop a GP-based primary care system.

Background: Patients with multimorbidity often experience treatment burden as a result of fragmented, specialist‐driven healthcare. The 'family doctor team' is an emerging service model in China to address the increasing need for high‐quality routine primary care. Objective: This study aimed to explore the extent to which treatment burden was associated with healthcare needs and patients' experiences. Methods: Multisite surveys were conducted in primary care facilities in Guangdong province, southern China. Interviewer‐administered questionnaires were used to collect data from patients (N = 2160) who had ≥2 clinically diagnosed long‐term conditions (multimorbidity) and had ≥1 clinical encounter in the past 12 months since enrolment registration with the family doctor team. Patients' experiences and treatment burden were measured using a previously validated Chinese version of the Primary Care Assessment Tool (PCAT) and the Treatment Burden Questionnaire, respectively. Results: The mean age of the patients was 61.4 years, and slightly over half were females. Patients who had a family doctor team as the primary source of care reported significantly higher PCAT scores (mean difference 7.2 points, p <.001) and lower treatment burden scores (mean difference −6.4 points, p <.001) when compared to those who often bypassed primary care. Greater healthcare needs were significantly correlated with increased treatment burden (β‐coefficient 1.965, p <.001), whilst better patients' experiences were associated with lower treatment burden (β‐coefficient −0.252, p <.001) after adjusting for confounders. Conclusion: The inverse association between patients' experiences and treatment burden supports the importance of primary care in managing patients with multimorbidity. Patient Contribution: Primary care service users were involved in the instrument development and data collection. [ABSTRACT FROM AUTHOR]

Summary: A large crack detection dataset of 2446 manually labeled images is established to cover a wide range of noise and to evaluate the performance of end‐to‐end deep convolutional networks in detecting cracking. Five state‐of‐the‐art end‐to‐end deep computer vision architectures for semantic segmentation are trained and evaluated, including Fully Convolutional Network (FCN), Global Convolutional Network (GCN), Pyramid Scene Parsing Network (PSPNet), UPerNet, and DeepLabv3+. For the backbones, the VGG, ResNet, and DenseNet are adopted. Based on the comparison of test set metrics, DeepLabv3+ with the ResNet101 backbone achieved the highest IoU of 0.6298, the highest recall of 0.6834, and the highest F1 score of 0.7732. The influence of database choice and image noise on crack detection performance is reported. Based on the comparison of predicted images, UperNet with ResNet101 backbone shows the highest performance for images with shadings, while DeepLabv3+ with ResNet101 backbone shows the best performance for images with blemishes. The research outcome can provide reference for the application of fast and accurate detection of cracks in civil engineering. [ABSTRACT FROM AUTHOR]