School of Economics and Management, Chongqing Normal University, Chongqing 401331, China.
Big Data Marketing Research and Applications Center, Chongqing Normal University, Chongqing 401331, China.
Int J Environ Res Public Health. 2023 Jan 12;20(2):1381. doi: 10.3390/ijerph20021381.
Regional eco-efficiency affects local public health through intermediaries such as economic and environmental impacts. Considering multiple factors, the implicit and uncertain relationship with regional characteristics, and the limited data availability, this paper investigated the forecasting of changes in local public health-including the number of visits to hospitals (VTH), outpatients with emergency treatment (OWET), number of inpatients (NI), number of health examinations (NOHE), and patients discharged (PD)-using calculated regional eco-efficiency with the Least Square-Support Vector Machine-Forecasting Model and acquired empirical evidence, utilizing the province-level data in China. Results: (1) regional eco-efficiency is a good predictor in both a single and multi-factor situation; (2) the prediction accuracy for five dimensions of the changes in local public health was relatively high, and the volatility was lower and more stable throughout the whole forecasting period; and (3) regional heterogeneity, denoted by three economic and demographic factors and three medical supply and technical level factors, improved the forecasting performance. The findings are meaningful for provincial-level decision-makers in China in order for them to know the current status or trends of medical needs, optimize the allocation of medical resources in advance, and enable ample time to tackle urgent emergencies, and, finally, the findings can serve to evaluate the social effects of improving regional eco-efficiency via local enterprises or individuals and adopting sustainable development strategies.
区域生态效率通过经济和环境影响等中介因素影响当地公共卫生。考虑到多种因素、与区域特征的隐含和不确定关系以及有限的数据可用性，本文使用计算出的区域生态效率，利用最小二乘支持向量机预测模型，调查了包括医院就诊次数 (VTH)、急诊门诊人次 (OWET)、住院人数 (NI)、健康检查次数 (NOHE)和出院人数 (PD) 在内的当地公共卫生变化的预测，利用中国省级数据获得了经验证据。结果表明：(1) 区域生态效率在单因素和多因素情况下都是一个很好的预测指标；(2) 五种维度的当地公共卫生变化的预测精度较高，整个预测期内波动性较低且更稳定；(3) 区域异质性，由三个经济和人口因素以及三个医疗供应和技术水平因素表示，提高了预测性能。研究结果对中国省级决策者具有重要意义，以便他们了解医疗需求的现状或趋势，提前优化医疗资源配置，并为应对紧急情况提供充足的时间，最终，研究结果可以评估通过当地企业或个人提高区域生态效率和采用可持续发展战略的社会影响。
