Ten years of China’s new healthcare reform: a longitudinal study on changes in health resources

Jiang, Chen; Lin, Zhongqiu; Lian, Li; Li, Jing; Wang, Yuyao; Pan, Yu; Yang, Jie; Xu, Chuncong; Zeng, Xiaojing; Xie, Xiaoxu; Xiao, Liangcheng

doi:10.1186/s12889-021-12248-9

Cited by 59 publications

(51 citation statements)

References 46 publications

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“…Inequity in the distribution and allocation of medical resources among different regions remains a worldwide problem [ 81 , 82 ], and China is no exception [ 83 , 84 ]. Some of the analytical results of this study reconfirm the conclusions of some scholars, such as the spatial heterogeneity [ 85 ], agglomeration [ 86 ], autocorrelation and correlation [ 87 , 88 ], non-equilibrium [ 89 ], and inequality [ 90 ] in the geographic distribution of medical resources and their spatio-temporal evolution in China; additionally, the geographic inequality of hospitals is greater than that of doctors and beds [ 91 ]. Some of the analytical results in this paper are not exactly the same or even contrary to the current knowledge and conclusions, and they are of enlightening value.…”

Section: Discussionsupporting

confidence: 83%

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

et al. 2022

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(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) Methods: Based on the competitive state model and spatial mismatch index, this paper uses GIS and Geodetector spatial analysis methods and three typical indicators of hospitals, doctors, and beds to conduct an empirical study on the evolutionary characteristics and degree of mismatch in the geographic pattern of health resources in China from 2010 to 2020 (the data are from official publications issued by the National Bureau of statistics in China), in two dimensions of resource supply (economic carrying capacity) and demand (potential demand or need of residents). (3) Results: The spatial pattern of health resources at the provincial level in China has been firmly established for a long time, and the children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30–40% of the factor pairs are in a nonlinear enhancement relationship. Hospital, doctor, and bed evolution trends and the magnitude and speed of their changes vary widely in spatial differentiation, but all are characterized by a high level of geographic agglomeration, heterogeneity, and gradient. Dynamic matching is the mainstream of development, while the geographical distribution of negative and positive mismatch shows strong spatial agglomeration and weak spatial autocorrelation. The cold and hot spots with evolution trend and space mismatch are highly clustered, shaping a center-periphery or gradient-varying spatial structure. (4) Conclusions: Despite the variability in the results of the analyses by different dimensions and indicators, the mismatch of health resources in China should not be ignored. According to the mismatch types and change trend, and following the geographic differentiation and spatial agglomeration patterns, this paper constructs a policy design framework of “regionalized governance-classified management”, in line with the concept of spatial adaptation and spatial justice, in order to provide a decision making basis for the government to optimize the allocation of health resources and carry out health spatial planning.

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Section: Discussionsupporting

confidence: 83%

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

et al. 2022

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“…Although Shanghai has over 30 million residents and the sample size was representative enough, it was notable that the GDP per capita in Shanghai was significantly higher than in other provinces. 49 Another limitation was the participant’s understanding of attribute and DCE tasks. Although the qualitative consultations confirmed the validity of our study design, the majority of our respondents were senior residents and they may have lower cognitive ability to consider all the information together.…”

Section: Limitationsmentioning

confidence: 99%

Preference to Family Doctor Contracted Service of Patients with Chronic Disease in Urban China: A Discrete Choice Experiment

Wang¹,

Sun²,

Jin³

et al. 2022

PPA

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Objective Shanghai is one of the pioneers proposing family doctor contract service (FDCS). However, there is no quantitative research focusing on the Shanghai experience from a demand-side perspective. This study investigated Shanghai chronic patients’ relative preferences for FDCS using a discrete choice experiment method. Methods A face-to-face discrete choice experiment (DCE) was performed to elicit the preference with 300 samples. Attributes and levels were extracted from the literature review and focus group consultation with patients. Seven attributes, follow-up frequency, medicine accessibility, family doctor competency, health management, referral convenience, appointment flexibility, and shared decision-making, were decided. Three levels were attached to each attribute. A mixed logit model was used to evaluate the multiple-choice data. Results A total of 248 patients completed the survey. Patient valued FDCS medicine accessibility (β=0.57, P < 0.05), and high family doctor competency (β= 0.43, P < 0.05), regular health management activities (β=0.36, P < 0.05), high follow-up frequency (β=0.31, P < 0.05) the most. The good doctor-patient shared decision-making atmosphere (β=0.12, P < 0.05), high referral convenience (β=0.06, P < 0.05) and high appointment flexibility (β=0.04, P < 0.05) are valued as less important. No significant preference heterogeneity was identified for patients with different sociodemographic characteristics. Respondents reported other FDCS needs, including online health consultation, specialist services in local institutes, higher reimbursement rates, free rehabilitation guidance for the disabled and personal health management. Conclusion This research is the first discrete choice experiment FDCS preference research targeting on Chinese urban population. The results suggested that to increase the quality of FDCS, policy-makers should prioritize follow-up frequency, medicine accessibility, family doctor competency and health management. The service package should consider a higher reimbursement rate and rehabilitation guidance for the disabled if extra health-care resources available. Future FDCS policy should consider stated societal preference and be congruent with it.

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“…However, the use and scoring of WISC-IV need to be authorized by relevant parties, with high acquisition costs, many standardized test items, and specific training for evaluators, which hinder the primary medical workers from evaluating the intelligence level of children with ASD. Furthermore, medical and health resources distribution is uneven in developing countries like China ( 22 ), with millions of children with ASD ( 23 ). Therefore, it is unrealistic to carry out comprehensive intelligence assessments like WISC-IV.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Song

Jiang

et al. 2022

Front. Psychiatry

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BackgroundEarly detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.MethodFrom January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children’s Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model’s performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).ResultAmong 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747–0.944]; RF, 0.829 [95% CI: 0.738–0.920]; XGBoost, 0.845 [95% CI: 0.734–0.937]) is not different from traditional LR (0.858 [95% CI: 0.770–0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.ConclusionCompared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.

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Ten years of China’s new healthcare reform: a longitudinal study on changes in health resources

Cited by 59 publications

References 46 publications

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

Preference to Family Doctor Contracted Service of Patients with Chronic Disease in Urban China: A Discrete Choice Experiment

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

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