THE POLITICAL ECONOMY OF HEALTH SECTOR WAGE POLICY: A CASE OF URBAN CHINA FROM 1949 TO PRESENT

Up until today, China’s health professionals and workers are mainly salaried employees in public sectors. In the socialist state, government-set salaries represent more than economic rewards for work; they reflect the redistributive priorities under the state socialist system and reveal information about the state’s political and ideological orientation. While health provider payment models for industrialized economies provide useful starting points for analysis, they are inadequate for understanding the wage policies for the Chinese health workforce and the politics behind these policies. This study examines the empirical impacts of the state’s politics and development strategies on the incentive arrangements for the urban health workforce. This study uses a combination of qualitative and quantitative research methods. The qualitative analysis addresses the political logic of redistribution and pay structures of the health workforce. It revisits and analyzes the major laws, administrative regulations, and government documents. The quantitative analysis aims to link state policies and individual health workers by examining how the policy-related factors affect health workers’ compensation. The regression models expand the Mincer's human capital earnings function and add the political capital variable to examine the impact of political loyalty on health workers’ wages. The data is synthesized from the Chinese Household Income Projects and the Chinese Household Finance Survey.The qualitative findings emphasize the balancing act of the state between competing political objectives including economic productivity, social equity, meritocracy, and political loyalty. In the Mao era, the state centralized wage-setting authority, structured national wage standards based on hierarchical positions, and paid health workers low wages but compensated them with in-kind subsidies and social welfare. In the post-Mao era, the state introduced market incentives to the health workers’ wage formula and delinked political factor from one’s compensation. The central authority decentralized the authority of setting wage standards to the ministry level in the 1980s and delegated the authority of bonus-setting to each health facility in the 1990s. But in recent years, the central government took an increasing role in building wage incentives that emphasize both position responsibility and performance of the health workforce. The regression results are in keeping with the qualitative findings, which suggest that wage distribution is a powerful policy tool that directly connects the state’s redistributive priorities to health workers' everyday lives. In particular, political loyalty (measured by Communist Party membership) and human capital (measured by education and work experience) yielded significant economic returns to health workers in 1988, before the onset of market reforms in the urban areas. Since the state’s deregulation and marketization in the 1990s, political loyalty no longer has substantial impacts on health workers’ regular wages and bonuses, while human capital continues to be an important wage determinant in the health labor markets. The intertwining of politics and economics plays a key role in the determination of health-sector wage policies, which sheds light on effective ways to improve health provider incentives and behaviors to yield more inclusive, outcome-oriented, and patient-centered health systems.</p

DataSheet_4_Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.csv

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.</p

DataSheet_8_Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.csv

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.</p

Table_6_New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions.xlsx

MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.</p

DataSheet_5_Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.csv

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.</p

DataSheet_6_Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.csv

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.</p

Table_5_New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions.xlsx

MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.</p

Table_2_New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions.xlsx

MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.</p

Image_4_New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions.tif

MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.</p

Table_1_New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions.xlsx

MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.</p