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Objective We aimed to investigate the feasibility and method of constructing a traditional Chinese medicine (TCM) curative effect prediction model based on the data of Kechuanting acupoint plastering therapy in the treatment of bronchial asthma (asthma). Methods Data from the Chronic Disease Management Research Platform of 303 patients with asthma who were treated with Kechuanting acupoint plastering therapy for 6 weeks in the Department of Acupuncture and Rehabilitation of Jiangsu Hospital of Traditional Chinese Medicine from June to August 2018 to 2021 were selected. We used Phyton 3.10 for statistical analysis. After data preprocessing, the influencing factors were used to build models by Logistic regression, support vector machine, K-means clustering algorithm, Bayes algorithm, random forest method and Light gradient boosting machine (LightGBM) respectively, with the improvement of asthma control test score (ACT), forced expiratory volume in one second (FEV1) and exhaled nitric oxide (FeNO) as the outcome indicators. Then, the models were compared and analyzed. Subsequently, the superior model was used to establish the efficacy prediction model and verify its stability to obtain the accuracy rate and eliminate the relatively important factors. Results The accuracy rate of the Kechuanting acupoint plastering therapy curative effect prediction model established by the LightGBM model was more than 70%. Five important factors were selected, including allergic history, tabacco and alcohol abuse, plastering duration, ACT before treatment, and FeNO before treatment. According to the classification analysis and the relationship between the important factors and the outcome indicators, Kechuanting acupoint plastering therapy significantly improved the ACT of patients with no history of allergy, no tabacco and alcohol abuse, and poor ACT:5-15 points (P < 0.05). Furthermore, Kechuanting acupoint plastering therapy improved FeNO more significantly in patients with more than 3 years of treatment than those with no more than 3 years (P < 0.05). However, Kechuanting acupoint plastering therapy only improved FeNO in a few patients with poor asthma control levels (P < 0.05) and severe airway inflammation (FeNO>50x10-9) (P>0.05). Conclusion Acupoint plastering application has a significant effect on improving the control level of asthma, but its effect on improving airway inflammation is limited. It is feasible to use data from the chronic disease management research platform to construct the prediction model. After optimization and testing, the predictive model established based on the data of this study may provide an effective evaluation tool for targeted clinical treatment. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is the second most deadly infectious disease after COVID-19 and the 13th leading cause of death worldwide. Among the 30 countries with a high burden of TB, China ranks third in the estimated number of TB cases. China is in the top four of 75 countries with a deficit in funding for TB strategic plans. To reduce costs and improve the effectiveness of TB treatment in China, the NHSA developed an innovative BP method. This study aimed to simulate the effects of this payment approach on different stakeholders, reduce the economic burden on TB patients, improve the quality of medical services, facilitate policy optimization, and offer a model for health care payment reforms that can be referenced by other regions throughout the world. Methods: We developed a simulation model based on a decision tree analysis to project the expected effects of the payment method on the potential financial impacts on different stakeholders. Our analysis mainly focused on comparing changes in health care costs before and after receiving BPs for TB patients with Medicare in the pilot areas. The data that were used for the analysis included the TB service claim records for 2019–2021 from the health insurance agency, TB prevalence data from the local Centre for Disease Control, and health care facilities' revenue and expenditure data from the Statistic Yearbook. A Monte Carlo randomized simulation model was used to estimate the results. Results: After adopting the innovative BP method, for each TB patient per year, the total annual expenditure was estimated to decrease from $2,523.28 to $2,088.89, which is a reduction of $434.39 (17.22%). The TB patient out-of-pocket expenditure was expected to decrease from $1,249.02 to $1,034.00, which is a reduction of $215.02 (17.22%). The health care provider's revenue decreased from $2,523.28 to $2,308.26, but the health care provider/institution's revenue-expenditure ratio increased from -6.09% to 9.50%. Conclusions: This study highlights the potential of BPs to improve medical outcomes and control the costs associated with TB treatment. It demonstrates its feasibility and advantages in enhancing the coordination and sustainability of medical services, thus offering valuable insights for global health care payment reform. [ABSTRACT FROM AUTHOR]

Packaging has become an important concern of fresh food supply chains and will exert a growing influence under environmental policies. In this paper, we have discussed three specific issues to investigate fresh food supply chains' packaging strategy, that is, whether to use disposable packaging containers (DPCs) or reusable packaging containers (RPCs), whether to purchase or rent RPCs, and the influences of environmental policies. Our research has generated some key findings. First, we find that penalty (such as taxes) and reward (such as subsidies) are simultaneously needed to motivate supply chains to choose RPCs. In terms of the "purchasing or renting" choice, it highly depends on the relative magnitude of the purchase and rental subsidies, along with the number of uses of RPCs and the perishability of fresh food. We also conclude that reusing RPCs for more times has two‐sided effects, that is, the effect of cost saving and the effect of accelerated deterioration. Thus, it is not recommended to excessively reuse RPCs. In addition, we make two extensions. First, we consider endogenous tax rate and subsidy rates and discuss from the perspective of social welfare. Second, we consider endogenous sales price and rental price of RPCs and study the optimal decisions of the RPC operator. Results show that the government should establish a classified tax and subsidy system for packaging containers, and meanwhile benchmarking prices for RPC services should be set to curb the RPC operator's free‐riding behavior. [ABSTRACT FROM AUTHOR]

Background: Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes., Objective: This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities., Methods: We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis., Results: Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective., Conclusions: High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions., Trial Registration: PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9., (©Jian Wang, Jinli Mahe, Yujia Huo, Weiyuan Huang, Xinru Liu, Yang Zhao, Junjie Huang, Feng Shi, Zhihui Li, Dou Jiang, Yilong Li, Garon Perceval, Lindu Zhao, Lin Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.08.2024.)