This study examined the effects of referral requirements for insurance patients which have been enforced since July 1, 1989 when medical insurance coverage was extended to the whole population except beneficiaries of medical assistance program. The requirements are mainly aimed at discourag - ing the use of to Vii; ry care hospitals by imposing restrictions on the patient's choice of a medical service facility. The expectation is that such change in the pattern of medical care utilization would produce several desirable effects including increased efficiency in patient care and balanced development of various types of medical service facilities. In this study, these effects were assessed by the change in the number of out-patient visits and bed-days per illness episode and the share of each type of facility in the volume of services and the amount of expenditures after the implementation of the new referral system. The data for analysis were obtained from the claims to the insurance for government and school employees. The sample was drawn from the claims for the patients treated during the first six months of 1989, prior to the enforcement of referral requirements, and those of the patients treated during the first six months of 1990, after the enforcement. The 1989 sample included 299,824 claims (3.6% of total) and the 1990 sample included 332,131(3.7% of total). The data were processed to make the unit of analysis an illness episode instead of an insurance claim. The facilities and types of care utilized for a given illness episode are defined to make up the pathway of medical care uti lization. This pathway was conceived of as a Markov Chain process for further analysis. The conclusion emerged from the analysis is that the enforcement of referral requirements resulted in less use of tertiary care hospitals, and thereby decreased the volume of services and the amount of insurance expenses per illness episode. However, there are a few points that have to be taken into account in relation to the conclusion. The new referral system is likely to increase the use of medical services not covered by insurance, so that its impact on national health expenditures would be different from. that on insurance expenditures. The extension of insurance coverage must have inereased patient load for all types of medical service organizations, and this increase may be partly responsible for producing the effects attributed to the new referral system. For example, excessive patient load for tertiary care hospitals may lead to the transfer of their patients to other types of facilities. Another point is that the data for this study correspond to very early phase of the new system. But both patients and medical care providers would adapt themselves to the new system to avoid or overcome its disadvantages for them, so as that its effects could change over time. Therefore, it is still necessary to closely monitor the impact of the referral requirements.
Health Expenditures ; Humans ; Insurance Coverage ; Insurance* ; Markov Chains ; Medical Assistance ; Outpatients ; Patient Care ; Referral and Consultation* ; Tertiary Healthcare

Aberrant DNA methylation, as an epigenetic marker of cancer, influences tumor development and progression. We downloaded publicly available DNA methylation and gene expression datasets of matched cancer and normal pairs from the Cancer Genome Atlas Data Portal and performed a systematic computational analysis. This study has three aims to screen genes that show hypermethylation and downregulated patterns in colorectal cancers, to identify differentially methylated regions in one of these genes, SFRP1, and to test whether the SFRP genes affect survival or not. Our results show that 31 hypermethylated genes had a negative correlation with gene expression. Among them, SFRP1 had a differentially methylated pattern at each methylation site. We also show that SFRP1 may be a potential biomarker for colorectal cancer survival.
Colorectal Neoplasms* ; Computer Simulation* ; Dataset ; DNA Methylation ; Epigenomics ; Gene Expression ; Genome ; Methylation ; Survival Analysis

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type–specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type–specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

Genome-wide association (GWA) studies may benefit from the inclusion of imputed SNPs into their dataset. Due to its predictive nature, the imputation process is typically not perfect. Thus, it would be desirable to develop a scheme for filtering out the imputed SNPs by maximizing the concordance with the observed genotypes. We report such a scheme, which is based on the combination of several parameters that are calculated by PLINK, a popular GWA analysis software program. We imputed the genotypes of 8,842 Korean individuals, based on approximately 2 million SNP genotypes of the CHB+JPT panel in the International HapMap Project Phase II data, complementing the 352k SNPs in the original Affymetrix 5.0 dataset. A total of 333,418 SNPs were found in both datasets, with a median concordance rate of 98.7%. The concordance rates were calculated at different ranges of parameters, such as the number of proxy SNPs (NPRX), the fraction of successfully imputed individuals (IMPUTED), and the information content (INFO). The poor concordance that was observed at the lower values of the parameters allowed us to develop an optimal combination of the cutoffs (IMPUTED> or =0.9 and INFO> or =0.9). A total of 1,026,596 SNPs passed the cutoff, of which 94,364 were found in both datasets and had 99.4% median concordance. This study illustrates a conservative scheme for filtering imputed SNPs that would be useful in GWA studies
Complement System Proteins ; Genome-Wide Association Study ; Genotype ; HapMap Project ; Humans ; Polymorphism, Single Nucleotide ; Proxy

Closely related species share large genomic segments called syntenic regions, where the genomic elements such as genes are arranged co-linearly among the species. While synteny is an important criteria in establishing orthologous regions between species, non-syntenic regions may display species-specific features. As the first step in cataloging human- or primate-specific genomic elements, we surveyed human genomic regions that are not syntenic with any other non-primate mammalian genomes sequenced so far. Based on the data compiled in Ensembl databases, we were able to identify 10 such regions located in eight different human chromosomes. Interestingly, most of these highly human- or primate-specific loci are concentrated in subtelomeric or pericentromeric regions. It has been reported that subtelomeric regions in human chromosomes are highly plastic and filled with recently shuffled genomic elements. Pericentromeric regions also show a great deal of segmental duplications. Such genomic rearrangements may have caused these large human- or primate-specific genome segments.
Cataloging ; Chromosomes, Human ; Genome ; Genome, Human ; Humans ; Plastics ; Resin Cements ; Segmental Duplications, Genomic ; Synteny

Genome-wide association (GWA) studies are the method of choice for discovering loci associated with common diseases. More than a thousand GWA studies have reported successful identification of statistically significant association signals in human genomes for a variety of complex diseases. In this review, I discuss some of the issues related to the future of GWA studies and their biomedical applications.
Genome, Human ; Genome-Wide Association Study ; Humans

No abstract available.

No abstract available.

Until now there are few available epidemiologic data of osteoporosis in Korea, and the severity of osteoporosis-related health problem has not been widely recognized yet. But the numbers of the old people are increasing in Korea, and in 2000, the proportion of people over 65 will be up to about 6.8% of total population. Therefore, osteoporosis, one of the most common metabolic bone disease among the old people, will be one of the most important public health problem. on this background this study was performed to find out risk factors of the development of osteoporosis in Korean women through case-control approach. The subject of this study were selected among the women one general hospital in seoul and were checked bone density from sep. 1988 to sep. 1993. Those who were diagnosed to have hypertension, diabetes mellitus, thyroid disease, breast disease, or liver disease, which are thought to influence bone density, were excluded. Also excluded those who are age-unknown. Finally the subjects were 2,139 women aged between 18 and 79. We operatively defined patient group as those whose bone density is below 1.03 g/cm2, 90% of average bone density of women of 4th decade who visited the same hospital. And we defined control group as whose bone density is above 1.15g/cm2. we randomly selected 201 women from the patient group and 202 from the control. As independent variables we chose age, menarche age, menopause age, menopause type, the number of siblings, the number of pregnancies, body mass index, taking oral pill or not, feeding type, and educational state. Multiple logistic regression analysis was done to see the influence of these variables on the risk of osteoporosis. Results are as follows; 1. menopausal status was statistically significant risk factor to all women irrespective of her age, while obesity and later menopause age were food to be statistically significant protective factors. 2. The more siblings and pregnancies, the greater the risk of osteoporosis, but these factors were not statistically significant. This result is not consistent with other studies. Further studies are strongly needed.
Body Mass Index ; Bone Density ; Bone Diseases, Metabolic ; Breast Diseases ; Case-Control Studies* ; Diabetes Mellitus ; Female ; Hospitals, General* ; Humans ; Hypertension ; Korea ; Liver Diseases ; Logistic Models ; Menarche ; Menopause ; Obesity ; Osteoporosis* ; Outpatients* ; Pregnancy ; Public Health ; Risk Factors* ; Seoul* ; Siblings ; Thyroid Diseases