Health Shocks and Unbalanced Growth of Medical Resources: Evidence From the SARS Epidemic in China

Trends in maternal and child health in China and its urban and rural areas from 1991 to 2020: a joinpoint regression model

The long-term trends in maternal and child health (MCH) in China and the national-level factors that may be associated with these changes have been poorly explored. This study aimed to assess trends in MCH indicators nationally and separately in urban and rural areas and the impact of public policies over a 30‒year period. An ecological study was conducted using data on neonatal mortality rate (NMR), infant mortality rate (IMR), under-five mortality rate (U5MR), and maternal mortality ratio (MMR) nationally and separately in urban and rural areas in China from 1991 to 2020. Joinpoint regression models were used to estimate the annual percentage changes (APC), average annual percentage changes (AAPC) with 95% confidence intervals (CIs), and mortality differences between urban and rural areas. From 1991 to 2020, maternal and child mortalities in China gradually declined (national AAPC [95% CI]: NMRs - 7.7% [- 8.6%, - 6.8%], IMRs - 7.5% [- 8.4%, - 6.6%], U5MRs - 7.5% [- 8.5%, - 6.5%], MMRs - 5.0% [- 5.7%, - 4.4%]). However, the rate of decline nationally in child mortality slowed after 2005, and in maternal mortality after 2013. For all indicators, the decline in mortality was greater in rural areas than in urban areas. The AAPCs in rate differences between rural and urban areas were - 8.5% for NMRs, - 8.6% for IMRs, - 7.7% for U5MRs, and - 9.6% for MMRs. The AAPCs in rate ratios (rural vs. urban) were - 1.2 for NMRs, - 2.1 for IMRs, - 1.7 for U5MRs, and - 1.9 for MMRs. After 2010, urban‒rural disparity in MMR did not diminish and in NMR, IMR, and U5MR, it gradually narrowed but persisted. MCH indicators have declined at the national level as well as separately in urban and rural areas but may have reached a plateau. Urban‒rural disparities in MCH indicators have narrowed but still exist. Regular analyses of temporal trends in MCH are necessary to assess the effectiveness of measures for timely adjustments.

Sustainable Implementation of Physician-Pharmacist Collaborative Clinics for Diabetes Management in Primary Healthcare Centers: A Qualitative Study

BACKGROUND

Although physician-pharmacist collaborative clinics for diabetes management have been shown to be effective and cost-effective worldwide, there is limited understanding of the factors that influence their sustainable implementation. This study aims to identify the associated factors and provide sustainability strategy to better implement physician-pharmacist collaborative clinics for diabetes management in primary healthcare centers in China.

METHODS

A sample of 43 participants were participated in face-to-face, in-depth, semi-structured interviews. Consolidated Framework for Implementation Research was used to identify facilitators and barriers to implementing physician-pharmacist collaborative clinics for diabetes management in primary healthcare centers, and to explore discriminating factors between low and high implementation units. A sustainable strategy repository based on dynamic sustainability framework was established to inform further implementation.

RESULTS

This study demonstrated that clear recognition of intervention benefits, urgent needs of patients, adaptive and tailored plan, highly collaborative teamwork and leadership support were the major facilitators, while the major barriers included process complexity, large number and poor health literacy of patients in primary areas, inappropriate staffing arrangements, weak financial incentives and inadequate staff competencies. Six constructs were identified to distinguish between high and low implementation units. Sixteen strategies were developed to foster the implementation of physician-pharmacist collaborative clinics, targeting Intervention, Practice setting, and Ecological system.

CONCLUSION

This qualitative study demonstrated facilitators and barriers to implementing physician-pharmacist collaborative clinics for diabetes management in primary healthcare centers and developed theory-based strategies for further promotion, which has the potential to improve the management of diabetes and other chronic diseases in under-resourced areas.

Unveiling spatial disparities in basic medical and health services: insights from China's provincial analysis

Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2012 to 2019, this paper constructs the evaluation index system of basic medical and health services in China from seven dimensions: medical and health facilities, health expenditure, medical services, traditional Chinese medicine hospital services, maternal and child health care, people's health and medical security, disease control and public health. The entropy method was used to measure the level of basic medical and health services in China, and its spatial differences and convergence characteristics were further investigated. In this study, we employ the entropy weight method, σ convergence, and β convergence as our primary methodologies. The entropy weight method is used to evaluate the variability of each indicator, determine the weights of indicators, and quantify the information content of the data. σ convergence illustrates the process by which the variance of a sample decreases over time. β convergence refers to the gradual approach of variables within an economic system towards their long-term equilibrium level over time. The results show that: (1) The scores of basic medical and health services in China's four major regions (including Northeast, East, Central and West) remain in a relatively stable state, with small fluctuations and great room for improvement; (2) There are significant regional differences in the level of basic medical and health services in China, and the intra-regional differences are much greater than the inter-regional differences; (3) There is no significant σ convergence observed in China and its four major regions; however, there is a notable presence of β convergence.

A study on the impact of health shocks on subjective wellbeing of middle-aged people and older adults-Evidence from China

Introduction

The health issues that afflict middle-aged people and older individuals are a significant factor that affects their quality of life. It is crucial to investigate the impact of health shocks on the subjective wellbeing of this demographic and the mechanisms that underlie this impact to promote healthy aging.

Methods

This study utilized data from the China Family Panel Study in 2018 and 2020 to analyze the effects of HSs and their categories on the subjective wellbeing of middle-aged people and older individuals using the propensity score matching difference-in-differences method. Additionally, the study explored the mediating role of social participation.

Results

The findings indicate that health shocks, both chronic and acute, diminish the subjective wellbeing of middle-aged people and older adults. Furthermore, these shocks have a more significant negative effect on the subjective wellbeing of individuals aged 60 and above, women in the middle-aged and older demographic, individuals in rural areas who belong to the middle-aged and older age groups, and individuals possessing activities of daily living. The mechanism analysis revealed that health shocks, both chronic and acute, reduce the subjective wellbeing of middle-aged people and older individuals by disrupting partnerships.

Discussion

Lowering the possibility of health shocks, the government should build a strong health management system and improve the health insurance system to enable timely treatment for persons suffering from health shocks. Individuals and families should live healthy lives and engage in social activities to avoid health shocks and improve subjective wellbeing.

Artificial intelligence in global health equity: an evaluation and discussion on the application of ChatGPT, in the Chinese National Medical Licensing Examination

Background

The demand for healthcare is increasing globally, with notable disparities in access to resources, especially in Asia, Africa, and Latin America. The rapid development of Artificial Intelligence (AI) technologies, such as OpenAI's ChatGPT, has shown promise in revolutionizing healthcare. However, potential challenges, including the need for specialized medical training, privacy concerns, and language bias, require attention.

Methods

To assess the applicability and limitations of ChatGPT in Chinese and English settings, we designed an experiment evaluating its performance in the 2022 National Medical Licensing Examination (NMLE) in China. For a standardized evaluation, we used the comprehensive written part of the NMLE, translated into English by a bilingual expert. All questions were input into ChatGPT, which provided answers and reasons for choosing them. Responses were evaluated for "information quality" using the Likert scale.

Results

ChatGPT demonstrated a correct response rate of 81.25% for Chinese and 86.25% for English questions. Logistic regression analysis showed that neither the difficulty nor the subject matter of the questions was a significant factor in AI errors. The Brier Scores, indicating predictive accuracy, were 0.19 for Chinese and 0.14 for English, indicating good predictive performance. The average quality score for English responses was excellent (4.43 point), slightly higher than for Chinese (4.34 point).

Conclusion

While AI language models like ChatGPT show promise for global healthcare, language bias is a key challenge. Ensuring that such technologies are robustly trained and sensitive to multiple languages and cultures is vital. Further research into AI's role in healthcare, particularly in areas with limited resources, is warranted.