Efficiency evaluation and promoter identification of primary health care system in China: an enhanced DEA-Tobit approach.

BACKGROUND: With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement. METHODS: Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China's healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation. RESULTS: The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion. CONCLUSIONS: Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.

Telemedicine-Enhanced Lung Cancer Screening Using Mobile Computed Tomography Unit with Remote Artificial Intelligence Assistance in Underserved Communities: Initial Results of a Population Cohort Study in Western China.

Introduction: Lung cancer is a leading cause of cancer deaths globally. Despite favorable recommendations, low-dose computed tomography (LDCT) lung screening adoption remains low in China. Barriers such as limited infrastructure, costs, distance, and personnel shortages restrict screening access in disadvantaged regions. We initiated a telemedicine-enabled lung cancer screening (LCS) program in a medical consortium to serve people at risk in underserved communities. The objective of this study was to describe the implementation and initial results of the program. Methods: From 2020 to 2021, individuals aged 40-80 years were invited to take LCS by mobile computed tomography (CT) units in three underserved areas in Western China. Numerous CT scans were remotely reported by radiologists aided by artificial intelligence (AI) diagnostic systems. Abnormal cases were tracked through an integrated hospital network for follow-up. A retrospective cohort study documented participant demographics, health history, LDCT results, and outcomes. Descriptive analysis was conducted to report baseline characteristics and first-year follow-up results. Results: Of the 28,728 individuals registered in the program, 19,517 (67.94%) participated in the screening. The study identified 2.68% of participants with high-risk pulmonary nodules and diagnosed 0.55% with lung cancer after a 1-year follow-up. The majority of high-risk participants received timely treatment in hospitals. Conclusions: This study demonstrated mobile CT units with remote AI assistance improved access to LCS in underserved areas, with high participation and early detection rates. Our implementation supports the feasibility of deploying telemedicine-enabled LCS to increase access to a large scale of basic radiology and diagnostic services in resource-limited settings. Clinical Trial Registration Number: ChiCTR1900024623.

Adaption of Ulva pertusa to multiple-contamination of heavy metals and nutrients: Biological mechanism of outbreak of Ulva sp. green tide.

The multiple-contamination of heavy metals and nutrients worsens increasingly and Ulva sp. green tide occurs almost simultaneously. To reveal the biological mechanism for outbreak of the green tide, Ulva pertusa was exposed to seven-day-multiple-contamination. The relation between pH variation (VpH), Chl a content, ratio of (Chl a content)/(Chl b content) (Rchla/chlb), SOD activity of U. pertusa (ASOD) and contamination concentration is [Formula: see text] (p<0.05), Cchla=0.88±0.09-0.01±0.00×CCd (p<0.05), [Formula: see text] (p<0.05), and [Formula: see text] (p<0.05), respectively. Cammonia, CCd and CZn is concentration of ammonia, Cd2+ and Zn2+, respectively. Comparing the contamination concentrations of seawaters where Ulva sp. green tide occurred and the contamination concentrations set in the present work, U. pertusa can adapt to multiple-contaminations in these waters. Thus, the adaption to multiple-contamination may be one biological mechanism for the outbreak of Ulva sp. green tide.