Analyzing maternal mortality rate in rural China by Grey-Markov model

Bai

et al. 2020

IJERPH

Background: Although China began implementing medical reforms in 2009 aimed at fair allocation of the regional distribution of doctors, little is known of their impact. This study analyzed the geographic distribution of doctors from 2002 to 2017. Methods: This study calculated the Gini coefficient and Theil index among doctors in the eastern, central, and western regions (Category 1) of China, and in urban and rural areas (Category 2). The statistical significance of fairness changes was analyzed using the Mann–Whitney U test. Results: The annual growth rates of the number of doctors for the periods from 2002 to 2009 and 2010 to 2017 were 2.38% and 4.44%. The Gini coefficients among Category 1 were lower than those in Category 2, and statistically decreased after the medical reforms (P < 0.01) but continued to increase in Category 2 (P = 0.463). In 2017, the Theil decomposition result of Category 1 was 74.33% for the between-group, and in Category 2, it was 95.22% for the within-group. Conclusions: The fairness among the regional distribution of doctors in Category 1 is now at a high level and is better than that before the reforms. While the fairness in Category 2 is worse than that before the reforms, it causes moderate inequality and is continually decreasing. Overall unfairness was found to be derived from the between-group.

Section: Data Resources and Regional Divisionsmentioning

confidence: 99%

Comparing Regional Distribution Equity among Doctors in China before and after the 2009 Medical Reform Policy: A Data Analysis from 2002 to 2017

Bai

et al. 2020

IJERPH

Wireless Communications and Mobile Computing

“…According to the above description, the quantitative evaluation of the social credibility of public power is obtained [17]. The result of the directed marker graph structure analysis of the statistical feature sequence data is obtained, and the social credibility index of the public power is calculated as the semantic ontology characteristic state of the information flow of the public power system [18,19]:…”

Section: Data Analysis and Preprocessing Socialmentioning

confidence: 99%

Research on Composition of Social Credibility Index Based on Artificial Intelligence Model

Liu

2020

Honesty refers to the fairness, justice, and responsibility of public power in social life. In order to enhance the trust of public power in society, it is necessary to analyze the social credibility indicator system. In order to achieve an accurate evaluation of social credibility, this article uses big data mining and artificial intelligence analysis methods to construct a social credibility evaluation index system for public power. First of all, this paper analyzes the distribution structure model of the public power social credibility index and analyzes the empirical mode decomposition and factor analysis of the public power social credibility evaluation data sequence in the social credibility feature distribution model. Secondly, this article constructs the public power social credit evaluation and artificial intelligence data sequence model. Using high-order cumulant features as a constraint operator, the accurate evaluation and consistent estimation of the social credibility index system of public power can be realized. The grey model is used to predict the social credibility of public power and realizes the optimized design of the evaluation model of public power social credibility. The simulation results show that the model has higher accuracy, better error convergence, and objectivity when designing the social reputation index system.

“…Existing research primarily focused on national or regional levels 10 , 12 , with less analysis conducted simultaneously across the national and urban‒rural dimensions. In terms of research methods, most of these studies use general descriptive analysis 13 or classical time series models such as Grey-Markov model and autoregressive integrated moving average (ARIMA) model 14 , 15 . Although these methods have certain advantages in data prediction, they cannot fully capture the localized variation patterns and inflection point information of the data.…”

Section: Introductionmentioning

confidence: 99%

Trends in maternal and child health in China and its urban and rural areas from 1991 to 2020: a joinpoint regression model

Wang,

Zhang,

Cao

et al. 2024

Sci Rep

The long-term trends in maternal and child health (MCH) in China and the national-level factors that may be associated with these changes have been poorly explored. This study aimed to assess trends in MCH indicators nationally and separately in urban and rural areas and the impact of public policies over a 30‒year period. An ecological study was conducted using data on neonatal mortality rate (NMR), infant mortality rate (IMR), under-five mortality rate (U5MR), and maternal mortality ratio (MMR) nationally and separately in urban and rural areas in China from 1991 to 2020. Joinpoint regression models were used to estimate the annual percentage changes (APC), average annual percentage changes (AAPC) with 95% confidence intervals (CIs), and mortality differences between urban and rural areas. From 1991 to 2020, maternal and child mortalities in China gradually declined (national AAPC [95% CI]: NMRs − 7.7% [− 8.6%, − 6.8%], IMRs − 7.5% [− 8.4%, − 6.6%], U5MRs − 7.5% [− 8.5%, − 6.5%], MMRs − 5.0% [− 5.7%, − 4.4%]). However, the rate of decline nationally in child mortality slowed after 2005, and in maternal mortality after 2013. For all indicators, the decline in mortality was greater in rural areas than in urban areas. The AAPCs in rate differences between rural and urban areas were − 8.5% for NMRs, − 8.6% for IMRs, − 7.7% for U5MRs, and − 9.6% for MMRs. The AAPCs in rate ratios (rural vs. urban) were − 1.2 for NMRs, − 2.1 for IMRs, − 1.7 for U5MRs, and − 1.9 for MMRs. After 2010, urban‒rural disparity in MMR did not diminish and in NMR, IMR, and U5MR, it gradually narrowed but persisted. MCH indicators have declined at the national level as well as separately in urban and rural areas but may have reached a plateau. Urban‒rural disparities in MCH indicators have narrowed but still exist. Regular analyses of temporal trends in MCH are necessary to assess the effectiveness of measures for timely adjustments.