The influence of the national drug price negotiation policy reform on the medical expenses of patients in Xuzhou City: an interrupted time series analysis.

Background: To reduce the burden of patients' medical care, the Xuzhou Municipal Government has initiated an exploratory study on the supply model and categorized management of nationally negotiated drugs. This study aims to understand the extent to which Xuzhou's 2021 reform of the National Drug Price Negotiation (NDPN) policy has had a positive impact on the healthcare costs of individuals with different types of health insurance. Methods: The Interrupted Time Series Analysis method was adopted, and the changes in average medical expenses per patient, average medical insurance payment cost per patient and actual reimbursement ratio were investigated by using the data of single-drug payments in Xuzhou from October 2020 to October 2022. Results: Following the implementation of the policy, there was a significant decrease in the average medical expenses per patient of national drug negotiation in Xuzhou, with a reduction of 62.42 yuan per month (p < 0.001). Additionally, the average medical insurance payment cost per patient decreased by 44.13 yuan per month (p = 0.01). Furthermore, the average medical expenses per patient of urban and rural medical insurance participants decreased by 63.45 yuan (p < 0.001), and the average monthly medical insurance payment cost per patient decreased by 57.56 yuan (p < 0.04). However, the mean total medical expenditures for individuals enrolled in employee medical insurance decreased by 63.41 yuan per month (p < 0.001), whereas the monthly decrease was 22.11 yuan per month (p = 0.21). On the other hand, there was no discernible change in the actual reimbursement ratio. Conclusion: After the adoption of the NDPN policy, a noticeable decline has been observed in the average medical expenses per patient and the mean cost of the average medical insurance payment per patient, although to a limited extent. Notably, the reduction in employee medical insurance surpasses that of urban and rural medical insurance.

Digital inclusive finance and the development of rural logistics in China.

The precarious production environment in rural areas limits the services of traditional finance and rural logistics. Digital inclusive finance is expected to alleviate some major drawbacks enabling financial services to contribute to rural logistics development. Using panel data from 31 provinces in China from 2013 to 2020, this paper constructed an indicator system to measure the development level of rural logistics. Furthermore, this paper investigates the mechanism enabling digital inclusive finance influences to enhance rural logistics development. We found that financial inclusion and digital finance have a positive and significant impact on the development level of rural logistics. Moreover, we found a nonlinear relationship with a diminishing marginal effect between digital inclusive finance and the development level of rural logistics. Furthermore, it was highlighted that the promotion efficiency of digital inclusive finance on the development level of rural logistics varies according to the region and economic development. This paper provides a theoretical basis for digital inclusive finance to promote rural logistics development. It also contributes to enhancing the role of financial services enabling good development of rural logistics.

Impacts of unifying urban and rural residents' medical insurance on the hospitalisation expenses of rural patients in eastern China: an interrupted time series analysis.

OBJECTIVES: This study evaluated the impact of the Urban and Rural Residents' Basic Medical Insurance scheme on hospitalisation expenses of rural patients in eastern China, which unified separate healthcare systems for urban and rural residents. DESIGN: Monthly hospitalisation data from municipal and county hospitals were collected from the local Medicare Fund Database, covering the period from January 2018 to December 2021. The unification of insurance between urban and rural patients was implemented at different times for county and municipal hospitals. An interrupted time series analysis was used to assess the immediate and gradual effects of the integrated policy on the total medical expenses, out-of-pocket (OOP) expenses and effective reimbursement rate (ERR) among rural patients. SETTING AND PARTICIPANTS: This study included 636 155 rural inpatients over 4 years in Xuzhou City, Jiangsu Province, China. RESULTS: In January 2020, the policy of urban and rural medical insurance was initially integrated in county hospitals, after which the ERR decreased at a monthly rate of 0.23% (p=0.002, 95% CI -0.37% to -0.09%) compared with the preintervention period. After the insurance systems were unified in municipal hospitals in January 2021, OOP expenses decreased by ¥63.54 (p=0.002, 95% CI -102.48 to -24.61) and the ERR increased at a monthly rate of 0.24% (p=0.029, 95% CI 0.03% to 0.045%). CONCLUSIONS: Our results suggest that the unification of urban and rural medical insurance systems was an effective intervention to reduce the financial burden of illness for rural inpatients, especially OOP expenses for hospitalisation in municipal hospitals.

Computationally Tractable Multivariate HMM in Genome-Wide Mapping Studies.

Hidden Markov model (HMM) is widely used for modeling spatially correlated genomic data (series data). In genomics, datasets of this kind are generated from genome-wide mapping studies through high-throughput methods such as chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq). When multiple regulatory protein binding sites or related epigenetic modifications are mapped simultaneously, the correlation between data series can be incorporated into the latent variable inference in a multivariate form of HMM, potentially increasing the statistical power of signal detection. In this chapter, we review the challenges of multivariate HMMs and propose a computationally tractable method called sparsely correlated HMMs (scHMM). We illustrate the method and the scHMM package using an example mouse ChIP-seq dataset.

A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data.

MOTIVATION: Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, detecting non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfillment of this task has many important implications including identifying long-range interactions that assist interpreting a sizable fraction of the results from genome-wide association studies. The task - distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions - poses great challenges both statistically and computationally. Model-based methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration. RESULTS: In this paper, we propose a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabilities in the two-dimensional space based on the contact frequency matrix. By borrowing information from neighboring loci pairs, our method demonstrates superior reproducibility and statistical power in both simulation studies and real data analysis. AVAILABILITY AND IMPLEMENTATION: The Source codes can be downloaded at: http://www.unc.edu/∼yunmli/HMRFBayesHiC CONTACT: ming.hu@nyumc.org or yunli@med.unc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sparsely correlated hidden Markov models with application to genome-wide location studies.

MOTIVATION: Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. RESULTS: We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward-backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis. AVAILABILITY: The scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is recommended for use in a linux environment.

Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer.

Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in select prostate tissues and cell lines using MethylPlex-next-generation sequencing (M-NGS). Hidden Markov model-based next-generation sequence analysis identified ∼68,000 methylated regions per sample. While global CpG island (CGI) methylation was not differential between benign adjacent and cancer samples, overall promoter CGI methylation significantly increased from ~12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues, respectively (P-value < 2 × 10(-16)). We found distinct patterns of promoter methylation around transcription start sites, where methylation occurred not only on the CGIs, but also on flanking regions and CGI sparse promoters. Among the 6691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer-specific, including numerous novel DMRs. A novel cancer-specific DMR in the WFDC2 promoter showed frequent methylation in cancer (17/22 tissues, 6/6 cell lines), but not in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested an epigenetic mechanism for alternate transcription start site utilization, and these modifications segregated into distinct regions when present on the same promoter. Finally, we observed differences in repeat element methylation, particularly LINE-1, between ERG gene fusion-positive and -negative cancers, and we confirmed this observation using pyrosequencing on a tissue panel. This comprehensive methylome map will further our understanding of epigenetic regulation in prostate cancer progression.

Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data.

MOTIVATION: Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genome-wide coverage and resolution up to a single base pair. For many well-studied TFs, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available. Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS. RESULTS: In this work, hierarchical hidden Markov model (HHMM) is proposed for combining data from ChIP-seq and ChIP-chip. In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied TFs, NRSF and CCCTC-binding factor (CTCF), also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two. AVAILABILITY: Source code for the software ChIPmeta is freely available for download at http://www.umich.edu/~hwchoi/HHMMsoftware.zip, implemented in C and supported on linux.

A new probabilistic rule for drug-dug interaction prediction.

An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (-infinity, 1.25), (1.25, 2), and (2, infinity). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K(i) and dose combinations.

Drug-drug interaction prediction assessment.

Model-based drug-drug interaction (DDI) is an important in-silico tool to assess the in vivo consequences of in vitro DDI. Before its general application to new drug compounds, the DDI model is always established from known interaction data. For the first time, tests for difference and equivalent tests are implemented to compare reported and model-base simulated DDI (log AUCR) in the sample mean and variance. The biases and predictive confidence interval coverage probabilities are introduced to assess the DDI prediction performance. Sample size and power guidelines are developed for DDI model simulations. These issues have never been discussed in trial simulation studies to investigate DDI prediction. A ketoconazole (KETO)/midazolam (MDZ) example is employed to demonstrate these statistical methods. Based on published KETO and MDZ pharmacokinetics data and their in vitro inhibition rate constant data, current model-based DDI prediction underpredicts the area under concentration curve ratio (AUCR) and its between-subject variance compared to the reported study.

Operon prediction for sequenced bacterial genomes without experimental information.

Various computational approaches have been proposed for operon prediction, but most algorithms rely on experimental or functional data that are only available for a small subset of sequenced genomes. In this study, we explored the possibility of using phylogenetic information to aid in operon prediction, and we constructed a Bayesian hidden Markov model that incorporates comparative genomic data with traditional predictors, such as intergenic distances. The prediction algorithm performs as well as the best previously reported method, with several significant advantages. It uses fewer data sources and so it is easier to implement, and the method is more broadly applicable than previous methods--it can be applied to essentially every gene in any sequenced bacterial genome. Furthermore, we show that near-optimal performance is easily reached with a generic set of comparative genomes and does not depend on a specific relationship between the subject genome and the comparative set. We applied the algorithm to the Bacillus anthracis genome and found that it successfully predicted all previously verified B. anthracis operons. To further test its performance, we chose a predicted operon (BA1489-92) containing several genes with little apparent functional relatedness and tested their cotranscriptional nature. Experimental evidence shows that these genes are cotranscribed, and the data have interesting implications for B. anthracis biology. Overall, our findings show that this algorithm is capable of highly sensitive and accurate operon prediction in a wide range of bacterial genomes and that these predictions can lead to the rapid discovery of new functional relationships among genes.

Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms.

Haplotypes have gained increasing attention in the mapping of complex-disease genes, because of the abundance of single-nucleotide polymorphisms (SNPs) and the limited power of conventional single-locus analyses. It has been shown that haplotype-inference methods such as Clark's algorithm, the expectation-maximization algorithm, and a coalescence-based iterative-sampling algorithm are fairly effective and economical alternatives to molecular-haplotyping methods. To contend with some weaknesses of the existing algorithms, we propose a new Monte Carlo approach. In particular, we first partition the whole haplotype into smaller segments. Then, we use the Gibbs sampler both to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can accurately and rapidly infer haplotypes for a large number of linked SNPs. By using a wide variety of real and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots.