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  1. Health system efficiency and equity in ASEAN: an empirical investigation
    Liu Y, Gong L, Niu H, Jiang F, Du S, Jiang Y
    Cost Eff Resour Alloc, 2024 Nov 26;22(1):86.
    PMID: 39587581 DOI: 10.1186/s12962-024-00588-3
    BACKGROUND: Equity and efficiency are two fundamental principles for the sound development of health systems, as advocated by the World Health Organization (WHO). Despite the notable progress made by the Association of Southeast Asian Nations (ASEAN) in advancing their health systems, gaps persist in achieving global health goals. This paper examines the efficiency of health system stages and the fairness of health resource distribution in ASEAN countries, analyzes the underlying causes of the existing gaps, and suggests potential solutions to bridge them.

    METHODS: Data spanning 2011 to 2019, sourced from the WHO Global Health Observatory and the World Bank Database, form the foundation of this study. This study employs an enhanced two-stage data envelopment analysis (DEA) to assess the efficiency of health system stages in ASEAN countries. Equity in health resource distribution is evaluated using health resource agglomeration degree and concentration curves across demographic, geographic, and economic aspects. Furthermore, the Entropy-Weighted TOPSIS method is utilized to integrate equity across these dimensions, measuring the overall fairness in health resource allocation across different countries. Finally, rankings of health system fairness and efficiency are compared to assess the overall development level of health systems.

    RESULTS: The overall efficiency of the ASEAN health systems from 2011 to 2019 averaged 0.231, with an upward trend in the first stage efficiency at 0.559 and a downward trend in the second stage at 0.502. The health resource agglomeration degree indicated that Singapore, Brunei, and Malaysia had HRAD and HRPD values significantly greater than 1, and Cambodia, Myanmar, and Laos predominantly had indices significantly less than 1. The concentration curve for hospital beds was the closest to the line of absolute equity. During the study period, the health resource concentration curve increasingly approached absolute equity, shifting from above to below the concentration curve. Singapore, Brunei, and Malaysia consistently remained in the first quadrant of the quadrant plot, and Myanmar and Cambodia were consistently in the third quadrant.

    CONCLUSION: ASEAN countries face two key challenges in their healthcare systems: first, while many nations such as Indonesia, Thailand, and Vietnam have improved resource allocation efficiency, this hasn't yet translated into better health services. To address this, establishing national health sector steering committees, focusing on workforce training and retention, and implementing centralized monitoring systems are crucial. Second, there is a growing disparity in healthcare development across ASEAN. Promoting balanced resource distribution and leveraging ASEAN's economic integration for regional collaboration will help bridge these gaps and foster more equitable healthcare systems.

  2. Fulltext Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization
    Niu H, Li Y, Zhang C, Chen T, Sun L, Abdullah MI
    Sensors (Basel), 2024 Oct 23;24(21).
    PMID: 39517691 DOI: 10.3390/s24216794
    Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.
  3. Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)
    Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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