China has the highest number of hepatitis B and C cases globally. Despite remarkable achievements, China faces daunting challenges in achieving international targets for hepatitis elimination. As part of a large-scale project assessing China’s progress in achieving health-related Sustainable Development Goals using quantitative, qualitative data and mathematical modelling, this paper summarises the achievements, gaps and challenges, and proposes options for actions for hepatitis B and C control. China has made substantial progress in controlling chronic viral hepatitis. The four most successful strategies have been: (1) hepatitis B virus childhood immunisation; (2) prevention of mother-to-child transmission; (3) full coverage of nucleic acid amplification testing in blood stations and (4) effective financing strategies to support treatment. However, the total number of deaths due to hepatitis B and C is estimated to increase from 434 724 in 2017 to 527 829 in 2030 if there is no implementation of tailored interventions. Many health system barriers, including a fragmented governance system, insufficient funding, inadequate service coverage, unstandardised treatment and flawed information systems, have compromised the effective control of hepatitis B and C in China. We suggest five strategic priority actions to help eliminate hepatitis B and C in China: (1) restructure the viral hepatitis control governance system; (2) optimise health resource allocation and improve funding efficiency; (3) improve access to and the quality of the health benefits package, especially for high-risk groups; (4) strengthen information systems to obtain high-quality hepatitis epidemiological data; (5) increase investment in viral hepatitis research and development.

This article is an overview of the National Assessment of Education Quality (NAEQ) of China in reading, mathematics, sciences, arts, physical education, and moral education at Grades 4 and 8. After a review of the background and history of NAEQ, we present the assessment framework with students’ holistic development at the core and the design for each subject used in the 2015–2017 assessment cycle. Technical details including item response modeling and the standard setting procedure are presented. We conclude with a discussion of the social impact, current issues, and future directions of national educational assessment.

This study addresses measurement issues around a standards-based content analysis of mathematics textbooks' coverage of standards for use in large-scale monitoring of standards implementation as proposed in a 2013 report by the National Research Council. An earlier study produced an exhaustive content analysis of textbooks using the 2012 Common Core State Standards for Mathematics. This yielded an accurate and reliable portrait of a textbook's coverage of standards. However, such an in-depth analysis is not feasible for large-scale standardsimplementation monitoring in which a large number of textbooks may need to be analyzed. To provide such a portrait with sufficient accuracy while also substantially reducing the associated resources needed to produce such a portrait, a simulation study was conducted with the exhaustively coded database to compare different sampling schemes. Results indicated that sampling 1 day from each week and coding the corresponding lessons led to sufficiently accurate representations of the overall content of the textbook. The results provide empirical evidence for large-scale standards-based content analyses of mathematics textbooks for monitoring standards implementation which could be adapted for other subject areas.

Multidimensional computerized adaptive testing (MCAT) based on the bifactor model is suitable for tests with multidimensional bifactor measurement structures. Several item selection methods that proved to be more advantageous than the maximum Fisher information method are not practical for bifactor MCAT due to time-consuming computations resulting from high dimensionality. To make them applicable in bifactor MCAT, dimension reduction is applied to four item selection methods, which are the posterior-weighted Fisher D-optimality (PDO) and three non-Fisher information-based methods—posterior expected Kullback–Leibler information (PKL), continuous entropy (CE), and mutual information (MI). They were compared with the Bayesian D-optimality (BDO) method in terms of estimation precision. When both the general and group factors are the measurement objectives, BDO, PDO, CE, and MI perform equally well and better than PKL. When the group factors represent nuisance dimensions, MI and CE perform the best in estimating the general factor, followed by the BDO, PDO, and PKL. How the bifactor pattern and test length affect estimation accuracy was also discussed.

Two widely used scales of Internet addiction (IA), the Internet Addiction Test (IAT) and the Chen Internet Addiction Scale (CIAS), were compared and a new scale of IA was assembled from their items with improved reliability in terms of classification consistency. A total of 467 Chinese college students participated in the study. Items were calibrated using the Muraki's Generalized Partial Credit Model. Most items had higher item information on medium levels of addiction, but much lower item information on the two ends of the latent trait continuum. The average item information of the CIAS was significantly larger compared with IAT on most of the latent trait levels. A new scale assembled using the cutoff points of IAT had a larger classification consistency than the original IAT. It was shown that the classification consistency of the IA measurement could be improved by selecting items to optimize test information around cutoff points. Implications for test and item development of IA were discussed.