RESULTS Higher visit proportion to primary care was associated with reductions in the infant mortality rate and the under-five mortality rate in both the entire population and the interprovincial migrants (p  0.05). CONCLUSIONS Our ecological study based in urban districts of Guangdong province found consistent associations between higher visit proportion to primary care and improvements in child health among the entire population and the interprovincial migrants, suggesting that China should continue to strengthen and develop the primary care system. The findings from China adds to the previously reported evidence on the association between primary care and improved health, especially that of the disadvantaged.BACKGROUND Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. METHODS In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. RESULTS We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. CONCLUSION To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.BACKGROUND Community-based care services refers to the professional services provided at home to the elderly with formally assessed demands. The growth of the elderly population has increased the demand for these services, and this issue is even worse in the affordable housing community (AHC) of China. Understanding of elderly's demands for different types of community-based care services and its determinations would enable the implementation of appropriate incentive schemes to promote utilization of community-based care services in the AHCs of China.  https://www.selleckchem.com/products/Eloxatin.html  METHODS Guided by previous studies, a conceptual framework was developed. Then, a questionnaire was designed and a community based survey was conducted from May 10-20, 2018 in Daishan AHC of Nanjing City, China. Four hundred eight participants from 25,650 elderly people were selected by systematic random sampling technique. Binary logistic regression was applied to the data about the elderly' primary demands for community-based care services in the AHC, to quantify the elderly's demands and explore related individual-level factors. RESULTS The finding indicates that more than 50% of respondents had the demand for an elderly care hotline, building health archives, on-call nursing and doctor visits, medical lectures, regular medical examinations and sporting fitness. The binary logistic regression models revealed that the primary demands of the elderly for community-based care services were influenced by distinct factors. CONCLUSIONS Our findings help clarify different types of community-based care services and provide fresh information about the demand for community-based care among the elderly in AHCs. Several policy implications are discussed to enhance the efficiency of community-based care service provision.BACKGROUND Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them. METHODS We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score,to achieve automated prescription and dispensing.BACKGROUND A reasonable allocation of health resources is often characterized by equity and high efficiency. This study aims to evaluate the equity and efficiency of maternal and child health (MCH) resources allocation in Hunan Province, China. METHODS Data related to MCH resources and services was obtained from the Hunan maternal and child health information reporting and management system. The Gini coefficient and data envelopment analysis (DEA) were employed to evaluate the equity and efficiency of MCH resources allocation, respectively. RESULTS The MCH resources allocation in terms of demographic dimension were in a preferred equity status with the Gini values all less than 0.3, and the Gini values for each MCH resources' allocation in terms of the geographical dimension ranged from 0.1298 to 0.4256, with the highest values in the number of midwives and medical equipment (≥ CNY 10,000), which exceeds 0.4, indicating an alert of inequity. More than 40% regions in Hunan were found to be relatively inefficient with decreased return to scale in the allocation of MCH resources, indicating those inefficient regions were using more inputs than needed to obtain the current output levels.