Distribution Of Medical Resources Research Articles

Advancing digital health in China: Aligning challenges, opportunities, and solutions with the Global Initiative on Digital Health (GIDH).

We summarized the unique challenges that China faced in digital health due to its large population, regional disparities, and uneven distribution of medical resources. Under the guidance of the Global Initiative on Digital Health (GIDH) released by WHO, we proposed corresponding solutions that address infrastructure, data, terminology, technology and security.

Application of machine learning algorithms in predicting carotid artery plaques using routine health assessments.

Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP. In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value. Among the seven ML models, the light gradient boosting machine (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation. This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization.

Measuring and optimizing the spatial accessibility of primary health care in remote and rural areas: a case study of Liannan Yao Autonomous County in China

The need for equitable access to primary healthcare services in the current global context has attracted widespread attention, prompting nations to continuously enhance their grassroots medical service levels. In response, China launched the "Healthy China" initiative, which prioritizes the enhancement of national health as a core goal of the healthcare system and uses this opportunity to deepen reforms aimed at strengthening primary care. However, in remote and rural areas, the optimization of medical resource allocation and the achievement of balanced service development remain critical challenges owing to limited resources. This study selected Liannan Yao Autonomous County, which is situated in the northwestern corner of Guangdong Province, as a case study due to its remote mountainous location, underdeveloped economy, and minority region characteristics. Through field research and interviews, this study thoroughly explored the needs of both supply and demand, factoring in elements such as the service capability of healthcare facilities and residents' travel thresholds to enhance the two-step floating catchment area model, thus making it more applicable to remote villages. By integrating electric bikes and cars, which are the primary means of transportation in rural areas, this study conducted a thorough analysis and comparison of the accessibility of medical services in Liannan Yao Autonomous County (Liannan County) . The results reveal significant disparities in healthcare accessibility, an uneven distribution of medical resources, and varying impacts of transportation conditions and facility service capabilities on accessibility. Notably, the study revealed that improving transportation conditions alone has limited effects in rural areas; the key lies in balancing medical service capabilities and the rationality of overall layouts. From the perspectives of equity and efficiency, this study employs the equitable coverage model and the efficiency-driven model to construct two scenarios, comparing accessibility changes in Liannan County under both conditions and proposing strategies to improve the spatial layout of local healthcare facilities. This research not only deepens the understanding of healthcare service accessibility in rural areas but also provides a scientific basis for optimizing resource allocation and enhancing primary medical services, offering valuable guidance and reference for Liannan County and other similar rural regions.

A Telemedicine System Using the Remote Diagnostic Imaging App "Join" for Emergency Medicine Throughout Wakayama Prefecture in Japan.

Background Telemedicine is expected to play an increasingly important role in building universal emergency medical systems without geographical disparities in the future. To eliminate healthcare disparities between regions, Wakayama Prefecture in Japan has been promoting telemedicine for closer inter-hospital coordination with secondary and tertiary emergency medical care hospitals. The "Join" diagnostic image-sharing app has been used since November 2018 for operating a remote emergency support system that shares medical images of emergency patients between 13 secondary and tertiary emergency medical care hospitals in Wakayama Prefecture. In this study, we investigated the effects of remote emergency medicine between hospitals after the introduction of Join. Methodology Four medical specialties use the remote emergency support system, namely, cardiovascular medicine, cardiovascular surgery, neurosurgery, and emergency care medicine. We investigated the results from 497 cases for which the system was used between November 2018 and February 2023. Results Of the 497 cases, treatment for 148 (29.8%) patients was continued in the same hospital, without a transfer. Emergency medicine was the group with the largest number of uses, in 232 cases. Of the 211 patients in the neurosurgery group, 88 (41.7%) were not transferred, the statistically significantly largest number of patients among the usage group (p < 0.01). An estimated transportation cost of 6,973,900 yen was saved due to 148 patients not being transferred. Tertiary emergency medical care hospitals were most frequently consulted, but secondary emergency medical care hospitals located long distances from tertiary emergency medical care hospitals also received large numbers of consultations from neighboring rural hospitals. Conclusions The Wakayama Prefecture remote emergency support system using Join has made it possible to communicate accurate information quickly between hospitals. This has contributed to fewer non-essential transfers to tertiary medical centers, reduced medical transport costs, and more effective distribution of medical resources to secondary medical institutions because ambulance transport was not necessary.

Investigating the Impact of Public Services on Rental Prices in Chinese Super Cities Based on Interpretable Machine Learning

In China, approximately 20% of the permanent population are renters, with 91% of leased land concentrated in first-tier and new first-tier cities. Education and healthcare are primary concerns for residents, significantly influencing rental decisions due to the household registration (hukou) system, competitive educational environment, and uneven distribution of medical resources. This study explores the distinct factors affecting rental decisions in China’s super cities, differing from other countries where renters prioritize proximity to work or urban amenities. Using advanced interpretable machine learning techniques, the study analyses rental markets in Beijing, Shanghai, and Shenzhen. The random forest model demonstrates superior performance in rent prediction across all three cities. The results indicate that the impact of public service resources on rent is notably higher in Beijing and Shanghai, while in Shenzhen, balanced urban planning results in property characteristics being more prominent in tenant preferences. These findings enhance the understanding of global rental market dynamics and provide recommendations for promoting sustainable rental housing development. The scientific novelty of this study lies in its application of advanced machine learning models to identify and quantify the unique influences of public service resources on rental markets in different urban contexts.

Cross-provincial inpatient mobility patterns and their determinants in China

BackgroundThe incongruity between the regional supply and demand of healthcare services is a persistent challenge both globally and in China. Patient mobility plays a pivotal role in addressing this issue. This study aims to delineate the cross-provincial inpatient mobility network (CIMN) in China and identify the underlying factors influencing this CIMN.MethodsWe established China’s CIMN by applying a spatial transfer matrix, utilizing the flow information from 5,994,624 cross-provincial inpatients in 2019, and identified the primary demand and supply provinces for healthcare services. Subsequently, we employed GeoDetector to analyze the impact of 10 influencing factors—including medical resources, medical quality, and medical expenses—on the spatial patterns of CIMN.FindingsBeijing, Shanghai, Zhejiang, and Jiangsu provinces are the preferred medical destinations for cross-provincial inpatients, while Anhui, Henan, Hebei, and Jiangsu provinces are the main sources for cross-provincial inpatients. Patient flow between provinces decreases with distance. The spatial distribution of medical resources, medical quality, and medical expenses account for 87%, 73%, and 56% of the formation of CIMN, respectively. Additionally, interactions between these factors enhance explanatory power, suggesting that considering their interactions can more effectively optimize medical resources and services.ConclusionsThe analysis of CIMN reveals the supply and demand patterns of healthcare services, providing insights into the inequality characteristics of healthcare access. Furthermore, understanding the driving factors and their interactions offers essential evidence for optimizing healthcare services.

Orthopedic Image Segmentation and Recognition Based on Deep Neural Networks

In the field of medicine, especially orthopedics, accurate diagnosis is crucial. However, due to the uneven distribution of medical resources and the continuous growth of patient numbers, the diagnostic efficiency of hospitals has been significantly affected, and the problems of misdiagnosis and missed diagnosis are becoming increasingly prominent. To address these issues, this study explores a deep neural network-based orthopedic image segmentation and recognition model, aiming to improve the accuracy and efficiency of diagnosis. Firstly, we use U-Net neural network for segmentation training of orthopedic images in order to extract detailed features from the images.

Vulnerability factors of snake bite patients in China

ObjectiveTo analyze the vulnerability factors of snakebite patients in China.MethodsMulti-stage random sampling was used as the main sampling method and snowball sampling as the auxiliary sampling method. The knowledge, attitude and behavior of snakebite among Chinese residents were investigated. Non-parametric test was used to compare the percentage differences in residents’ knowledge, attitude and behavior of snakebite, and generalized linear regression analysis was used to analyze the influencing factors, and the vulnerability factors of snakebite patients were comprehensively analyzed.ResultsA total of 6338 subjects were included in this study, of which 68.4% were males, and 58.6% were farmers, workers and service personnel. The median total score of knowledge, attitude, and behavior was 26 (22,36). The patients who were improperly treated after injury were ligation proximal to the affected area (23.43%), squeezing (21.82%), and oral and suction wounds (8.74%). Did not go to hospital due to poverty (1351 cases) and did not receive antivenom (2068 cases). There were 21.32% and 32.63%, respectively. Among 4270 patients injected with antivenom 30.7% were vaccinated within 2 h. Among the patients who went to the hospital for treatment (4987), 75.0% arrived at the hospital within 6 h; Among the 4,761 patients who made emergency calls, 37.4% were treated within 0.5 h.ConclusionsSnakebite patients in China have weak knowledge about snakebite, low awareness of medical treatment, lack of correct prevention and emergency treatment measures, dependence on folk remedies, poor housing and so on. In addition, there are low availability of antivenoms and unreasonable distribution of medical resources in some areas of China. Multisectoral and multidisciplinary cooperation should be developed to prevent and control snakebites in order to reduce the burden caused by snakebites.

Machine learning for screening and predicting the availability of medications for children: a cross-sectional survey study.

The aim of the study was to explore the factors influencing the availability of medications for children, and establish a machine learning model to provide an empirical basis for the subsequent formulation and improvement of relevant policies. Design: Cross-sectional survey. Setting: 12 provinces, China. Medical doctors from 25 public hospitals were enrolled. All data were randomly divided into a training set and a validation set at a ratio of 7:3. Three prediction models, namely random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost), were developed and compared. The receiver operating characteristic curve (ROC) and the associated area under the curve (AUC) were used to evaluate the three models. A nomogram and clinical impact curve (CIC) for availability of medication were developed. Fifteen of 29 factors in the database that were most likely to be selected were considered to establish the prediction model. The XGBoost model (AUC = 0.915) demonstrated better performance than the RF model (AUC = 0.902) and the LR model (AUC = 0.890). According to the Shapley additive explanation values, the five factors that most significantly affected the availability of medications for children in the XGboost model were as follows: the relatively small number of specialized dosage forms for children; unaffordable medications for children; public education on the accessibility and safety of medication for children; uneven distribution of medical resources, leading to insufficient access to medication for children; and years of service as a doctor. The CIC was used to assess the practical applicability of the factor prediction nomogram. The XGBoost model can be used to establish a prediction model to screen the factors associated with the availability of medications for children. The most important contributing factors to the models were the following: the relatively small number of specialized dosage forms for children; unaffordable medications for children; public education on the accessibility and safety of medication for children; uneven distribution of medical resources, leading to insufficient access to medication for children; and years of service as a doctor.

Clinical Advances and Current Status in the Treatment of Ureteral Calculi.

Ureteral calculi are a common urological disease with a consistently high incidence and an increasing trend each year. Ureteral calculi treatment is an essential and hot topic in the urology field and holds a vital status in the urological work system. Recently, with rapid advances in urology, there have been continuous updates and developments in treatment modalities, and many new methods and techniques have emerged and are being applied in clinical settings; This has effectively improved the clinical treatment outcomes of individuals with ureteral calculi. However, each treatment modality has its specific indications, and owing to the uneven distribution of medical resources and the effect of the patients' conditions and nature of the stones, standardization and randomness in selecting the treatment regimens for ureteral calculi are lacking. Therefore, selecting the diagnostic and therapeutic plan is vital for improving treatment efficacy. In this review, we summarize the findings of recent domestic and international studies to provide an outline of the progress and current status of ureteral calculi treatment from aspects such as pharmacotherapy, surgery, and minimally invasive treatment to provide a basis for treating this disease in clinical settings.

The usage and costs of national drug price-negotiated anticancer medicines in a first-tier city in Northeast China: a study based on health insurance data

BackgroundThe National Drug Price Negotiation (NDPN) policy has entered a normalisation stage, aiming to alleviate, to some extent, the disease-related and economic burdens experienced by cancer patients. This study analysed the use and subsequent burden of anticancer medicines among cancer patients in a first-tier city in northeast China.MethodsWe assessed the usage of 64 negotiated anticancer medicines using the data on the actual drug deployment situation, the frequency of medical insurance claims and actual medication costs. The affordability of these medicines was measured using the catastrophic health expenditure (CHE) incidence and intensity of occurrence. Finally, we used the defined daily doses (DDDs) and defined daily doses cost (DDDc) as indicators to evaluate the actual use of these medicines in the region.ResultsDuring the study period, 63 of the 64 medicines were readily available. From the perspective of drug usage, the frequency of medical insurance claims for negotiated anticancer medicines and medication costs showed an increasing trend from 2018 to 2021. Cancer patients typically sought medical treatment at tertiary hospitals and purchased medicines at community pharmacies. The overall quantity and cost of medications for patients covered by the Urban Employee Basic Medical Insurance (UEBMI) were five times higher than those covered by the Urban and Rural Resident Medical Insurance (URRMI). The frequency of medical insurance claims and medication costs were highest for lung and breast cancer patients. Furthermore, from 2018 to 2021, CHE incidence showed a decreasing trend (2.85–1.60%) under urban patients’ payment capability level, but an increasing trend (11.94%–18.42) under rural patients’ payment capability level. The average occurrence intensities for urban (0.55–1.26 times) and rural (1.27–1.74 times) patients showed an increasing trend. From the perspective of drug utilisation, the overall DDD of negotiated anticancer medicines showed an increasing trend, while the DDDc exhibited a decreasing trend.ConclusionThis study demonstrates that access to drugs for urban cancer patients has improved. However, patients’ medical behaviours are affected by some factors such as hospital level and type of medical insurance. In the future, the Chinese Department of Health Insurance Management should further improve its work in promoting the fairness of medical resource distribution and strengthen its supervision of the nation’s health insurance funds.

Evaluating the integration of traditional gong methods in smart home environment for the recovery of health functions of stroke patients

Since the 21st century, network and mobile communication technology are gradually entering the medical and health services field. Combining body area networks, broad-generation mobile communications, and cloud platforms has made various medical applications for large-scale populations possible. The development of digital medical technology, especially digital telemedicine, is increasingly proving to be an important means of significantly reducing the cost of medical care and access, changing the distribution of medical resources, and improving the overall level of care. To observe the effects of traditional Chinese medicine gongfu combined with rehabilitation therapy on mild depression, anxiety, and functional recovery of activities of daily living (ADL) in patients recovering from stroke, and to provide new treatment methods to improve the function and daily living ability of the group who develop mild depression and anxiety after stroke. In this paper, the digital medical engineering application combining information technology and medical treatment integrates various high-end information technologies such as body domain network and cloud computing to solve the difficulties in the current application one by one, to provide the national people with the system provides “timely”, “local” and “bottomless” remote digital health services to the Chinese people.

A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis

Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it is difficult to discriminate the staged diagnosis of pneumoconiosis imaging, and due to the influence of uneven distribution of medical resources and other factors, it is easy to lead to misdiagnosis and omission of diagnosis in primary healthcare institutions. Computer-aided diagnosis system can realize rapid screening of pneumoconiosis in order to assist clinicians in identification and diagnosis, and improve diagnostic efficacy. As an important branch of deep learning, convolutional neural network (CNN) is good at dealing with various visual tasks such as image segmentation, image classification, target detection and so on because of its characteristics of local association and weight sharing, and has been widely used in the field of computer-aided diagnosis of pneumoconiosis in recent years. This paper was categorized into three parts according to the main applications of CNNs (VGG, U-Net, ResNet, DenseNet, CheXNet, Inception-V3, and ShuffleNet) in the imaging diagnosis of pneumoconiosis, including CNNs in pneumoconiosis screening diagnosis, CNNs in staging diagnosis of pneumoconiosis, and CNNs in segmentation of pneumoconiosis foci to conduct a literature review. It aims to summarize the methods, advantages and disadvantages, and optimization ideas of CNN applied to the images of pneumoconiosis, and to provide a reference for the research direction of further development of computer-aided diagnosis of pneumoconiosis.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

The Global Occurrences of Cleft Lip and Palate in Pediatric Patients and Their Association with Demographic Factors: A Narrative Review.

Orofacial clefts are one of the most common abnormalities that occur in the orofacial area. Due to their high prevalence, special attention provided to risk factors and their possible involvement in the occurrence of orofacial clefts is of importance. The objective of this study was to review the current global occurrence of orofacial clefts and the possible linkage of previously investigated risk factors to the occurrence of orofacial clefts. The risk factors of orofacial clefts can be classified into two groups, modifiable risk factors and non-modifiable risk factors. Due to the extent of elaboration of each risk factor in each group, this current narrative review is limited to several mostly investigated risk factors, which included a review of parental age, sexual disparities, educational attainment, and income. Studies indicate that Asians are more likely than other races to have orofacial clefts, with a higher incidence rate in men than in women. There is evidence that the age of the parents is associated with the chance of the occurrence of orofacial cleft. The prevention of orofacial clefts and the distribution of medical resources depend heavily on a thorough understanding of epidemiology on a global scale. Nevertheless, the earlier studies concentrated on more developed nations or areas, and registry data from low-income nations had significant gaps. The findings of this narrative review can be used as the scientific basis for further research within this area. The occurrence rate of orofacial clefts remains high in several regions. Possible associations between parental age, sexual disparities, educational attainment, and family income to the occurrence of orofacial clefts remain contradictory, indicating the importance of further research to obtain more insights.

Does unequal economic development contribute to the inequitable distribution of healthcare resources? Evidence from China spanning 2001–2020

BackgroundThere is a dearth of research combining geographical big data on medical resource allocation and growth with various statistical data. Given the recent achievements of China in economic development and healthcare, this study takes China as an example to investigate the dynamic geographical distribution patterns of medical resources, utilizing data on healthcare resources from 290 cities in China, as well as economic and population-related data. The study aims to examine the correlation between economic growth and spatial distribution of medical resources, with the ultimate goal of providing evidence for promoting global health equity.MethodsThe data used in this study was sourced from the China City Statistical Yearbook from 2001 to 2020. Two indicators were employed to measure medical resources: the number of doctors per million population and the number of hospital and clinic beds per million population. We employed dynamic convergence model and fixed-effects model to examine the correlation between economic growth and the spatial distribution of medical resources. Ordinary least squares (OLS) were used to estimate the β values of the samples.ResultsThe average GDP for all city samples across all years was 36,019.31 ± 32,029.36, with an average of 2016.31 ± 1104.16 doctors per million people, and an average of 5986.2 ± 6801.67 hospital beds per million people. In the eastern cities, the average GDP for all city samples was 47,672.71 ± 37,850.77, with an average of 2264.58 ± 1288.89 doctors per million people, and an average of 3998.92 ± 1896.49 hospital beds per million people. Cities with initially low medical resources experienced faster growth (all β < 0, P < 0.001). The long-term convergence rate of the geographic distribution of medical resources in China was higher than the short-term convergence rate (|βi + 1| > |βi|, i = 1, 2, 3, …, 9, all β < 0, P < 0.001), and the convergence speed of doctor density exceeded that of bed density (bed: |βi| >doc: |βi|, i = 3, 4, 5, …, 10, P < 0.001). Economic growth significantly affected the convergence speed of medical resources, and this effect was nonlinear (doc: βi < 0, i = 1, 2, 3, …, 9, P < 0.05; bed: βi < 0, i = 1, 2, 3, …, 10, P < 0.01). The heterogeneity between provinces had a notable impact on the convergence of medical resources.ConclusionsThe experiences of China have provided significant insights for nations worldwide. Governments and institutions in all countries worldwide, should actively undertake measures to actively reduce health inequalities. This includes enhancing healthcare standards in impoverished regions, addressing issues of unequal distribution, and emphasizing the examination of social determinants of health within the domain of public health research.

Study on the trend of congenital heart disease inpatient costs and its influencing factors in economically underdeveloped areas of China, 2015-2020: a case study of Gansu Province.

Economic data on congenital heart disease are scarce in economically underdeveloped areas of China. Therefore, this study aimed to shed light on the level and changing trend of congenital heart disease inpatients' economic burden in underdeveloped areas. This study used a multi-stage stratified cluster sampling method to select 11,055 inpatients with congenital heart disease from 197 medical and health institutions in Gansu Province. Their medical records and expenses were obtained from the Hospital Information System. Univariate analysis was conducted using the rank sum test and Spearman rank correlation. Quantile regression and random forest were used to analyze the influencing factors. From 2015 to 2020, the average length of stay for congenital heart disease patients in Gansu Province was 10.09 days, with an average inpatient cost of USD 3,274.57. During this period, the average inpatient costs per time increased from USD 3,214.85 to USD 3,403.41, while the average daily inpatient costs increased from USD 330.05 to USD 376.56. The average out-of-pocket costs per time decreased from USD 2,305.96 to USD 754.77. The main factors that affected the inpatient costs included length of stay, cardiac procedure, proportion of medications, age, and hospital level. Congenital heart disease causes a significant economic burden on both families and society. Therefore, to further reduce the patient's financial burden, the length of stay should be reasonably reduced, and the rational distribution of medical resources should be continuously promoted to ensure equitable access to healthcare services.

The effect of urban-rural resident basic medical insurance on physical health of the rural older adult in China.

Urban-Rural Resident Basic Medical Insurance (URRBMI) is an important system for effectively transferring disease risks to the rural older adult. As China experiences rapid aging, maintaining the physical health of the rural older adult is key to achieving the goal of healthy aging. The study explores the impact of URRBMI on physical health of the rural older adult in China using the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data in 2018. Ordinary least square models were used to analyze the relationship between URRBMI and physical health of the rural older adult, and we used instrumental variable method to address the potential endogenous problem. We find that URRBMI greatly improves physical health of the rural older adult. The heterogeneity analysis indicates that URRBMI contributes more significantly to the rural older adult in eastern areas and the advanced rural older adult. The results also suggested that URRBMI improves physical health of the rural older adult through increasing life satisfaction and enhancing the timeliness of medical services. This study implies that we need to further improve the participation rate, increase the actual reimbursement ratio and increase financial subsidies for URRBMI in central and western areas, and further integrate the distribution of medical resources to promote physical health of the rural older adult.

Equity and spatial accessibility of healthcare resources in online health community network

Introduction: This study investigates the geographical distribution and fractal characteristics of the medical service network in China, using the “Good Doctor website” as a case study.Methods: Data for this study were extracted from the Good Doctor website Health Community. A two-tiered hierarchical network model was developed to analyze the geographical distribution and fractal characteristics of the medical service network in China.Results: Results unveil the hierarchical nature of hospital distribution and the interconnectivity among healthcare institutions. Shandong Province as a central node within the national hospital network, and networks of secondary hospitals show significant self-similarity and scale-free properties.Discussion: The small world and fractal characteristics shed light on the rapid dissemination of medical information and the robustness of the healthcare network. The results offer a novel perspective for understanding and optimizing the distribution of medical resources, and help improve the efficiency of healthcare services supply.

Challenges for Medical Students in Applying Ethical Principles to Allocate Life-Saving Medical Devices During the COVID-19 Pandemic: Content Analysis.

The emergence of the COVID-19 pandemic has posed a significant ethical dilemma in the allocation of scarce, life-saving medical equipment to critically ill patients. It remains uncertain whether medical students are equipped to navigate this complex ethical process. This study aimed to assess the ability and confidence of medical students to apply principles of medical ethics in allocating critical medical devices through the scenario of virtual patients. The study recruited third- and fourth-year medical students during clinical rotation. We facilitated interactions between medical students and virtual patients experiencing respiratory failure due to COVID-19 infection. We assessed the students' ability to ethically allocate life-saving resources. Subsequently, we analyzed their written reports using thematic analysis to identify the ethical principles guiding their decision-making. We enrolled a cohort of 67 out of 71 medical students with a mean age of 34 (SD 4.7) years, 60% (n=40) of whom were female students. The principle of justice was cited by 73% (n=49) of students while analyzing this scenario. A majority of them expressed hesitancy in determining which patient should receive life-saving resources, with 46% (n=31) citing the principle of nonmaleficence, 31% (n=21) advocating for a first-come-first-served approach, and 25% (n=17) emphasizing respect for patient autonomy as key influencers in their decisions. Notably, medical students exhibited a lack of confidence in making ethical decisions concerning the distribution of medical resources. A minority, comprising 12% (n=8), proposed the exploration of legal alternatives, while 4% (n=3) suggested medical guidelines and collective decision-making as potential substitutes for individual ethical choices to alleviate the stress associated with personal decision-making. This study highlights the importance of improving ethical reasoning under time constraints using virtual platforms. More than 70% of medical students identified justice as the predominant principle in allocating limited medical resources to critically ill patients. However, they exhibited a lack of confidence in making ethical determinations and leaned toward principles such as nonmaleficence, patient autonomy, adherence to legal and medical standards, and collective decision-making to mitigate the pressure associated with such decisions.