China has substantially increased financial investment and introduced favourable policies for strengthening its primary health care system with core responsibilities in preventing and managing chronic diseases such as hypertension and emerging infectious diseases such as coronavirus disease 2019 (COVID-19). However, widespread gaps in the quality of primary health care still exist. In this Review, we aim to identify the causes for this poor quality, and provide policy recommendations. System challenges include: the suboptimal education and training of primary health-care practitioners, a fee-for-service payment system that incentivises testing and treatments over prevention, fragmentation of clinical care and public health service, and insufficient continuity of care throughout the entire health-care system. The following recommendations merit consideration: (1) enhancement of the quality of training for primary healthcare physicians, (2) establishment of performance accountability to incentivise high-quality and high-value care;(3) integration of clinical care with the basic public health services, and (4) strengthening of the coordination between primary health-care institutions and hospitals. Additionally, China should consider modernising its primary health-care system through the establishment of a learning health system built on digital data and innovative technologies.

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Background and Objective Teriflunomide is a once-daily oral immunomodulatory agent approved in 80 countries for the treatment of patients with relapsing multiple sclerosis (RMS). The study objective was to estimate the cost effectiveness of teriflunomide (14 mg tablet, daily) versus interferon beta-1b (250 mcg subcutaneous injection, every other day) among RMS patients from the Chinese healthcare system perspective. Methods A Markov model with annual cycles and a lifetime horizon was utilized to assess cost-effectiveness of teriflunomide in comparison with interferon beta-1b in RMS patients. Treatment effects, including 3-month confirmed disability worsening and annualized relapse rate, were derived from a network meta-analysis. Cost inputs included costs related to treatment acquisition, administration, monitoring, natural disease management through Expanded Disability Status Scale states, relapse treatment, and adverse event management. These costs were calculated as the product between unit costs from published sources and healthcare resource utilization patterns identified in a survey conducted among 11 neurologists across different areas in China. Health effects were expressed as quality-adjusted life years (QALYs) with costs in local currency (¥) and US dollars (US$), 2018. Results Teriflunomide dominated interferon beta-1b and was associated with lower total costs (teriflunomide ¥1,887,144 vs interferon beta-1b ¥2,061,393) and higher QALYs (teriflunomide 9.60 QALYs vs interferon beta-1b 8.88 QALYs). In probabilistic sensitivity analysis, teriflunomide was dominant in 62.2% of model runs. Conclusion Teriflunomide is a cost-effective therapy over a lifetime time horizon compared to interferon beta-1b in the treatment of RMS patients in China. Results should be interpreted with caution as head-to-head comparisons are not available. Electronic supplementary material The online version of this article (10.1007/s40261-019-00750-3) contains supplementary material, which is available to authorized users.

Accurate short-term wind power forecasting (WPF) plays a crucial role in grid scheduling and wind power accommodation. Numerical weather prediction (NWP) wind speed is the fundamental data for short-term WPF. At present, reducing NWP wind speed forecast errors contributes to improving the accuracy of WPF from the perspective of data quality. In this article, a variational mode decomposition combined with bidirectional gated recurrent unit (VMD-BGRU) method for NWP wind speed correction and XGBoost forecasting model are proposed. First, several NWP wind speed sub-series are divided by VMD to obtain more abundant multidimensional timing features. BGRU is applied to establish the potential relation between decomposed NWP wind speed sub-series and measured wind speed and get the proposed wind speed correction model. Then, a more clear regression forecasting model is trained based on XGBoost using historical measured wind speed and power. The corrected NWP wind speed is used to forecast wind power by XGBoost. Finally, the superiority of the proposed method is validated on a wind farm located in China. The results show that the proposed correction model and forecasting model outperform other compared models.

Under the background of clean and low-carbon energy transformation, renewable distributed generation is connected to the distribution system on a large scale. This study proposes a probabilistic assessment method of hosting capacity considering wind–photovoltaic–load temporal characteristics in distribution networks. First, based on time series of wind, photovoltaic, and load demands, a discretization–aggregation technique is introduced to generate and filter extreme combinations. The method can effectively reduce the scenarios that need to be evaluated. Then a holomorphic embedding method considering generation and load scaling directions is proposed. The holomorphic function of voltage about an embedding variable is established, and it is analytically expanded in the form of series. The hosting capacity restrained by the voltage violation problem is calculated quickly and accurately. Finally, the proposed stochastic framework is implemented to evaluate hosting capacity involving renewable energy types, penetration levels, and locations. The hosting capacity of single energy and hybrid wind–solar renewable energy systems is evaluated from the perspective of probability analysis. The results verify the outstanding performance of the hybrid wind–solar energy system in improving the hosting capacity.