The associations of SNPs rs11614913, rs2292832, and rs2910164 in miRNAs have been exploded in several independent studies and meta-analyses, but the small sample sizes and incomplete data precluded well-defined roles of the miRNA SNPs in the development of CRC. The aim of this study was to combine all available data to comprehensively assess the unclear association. A meta-analysis of nine studies included 2,209 cancers and 2,803 controls, 2,349 cases and 2,663 controls, and 1,409 cases and 1,115 controls for SNP rs11614913, SNP rs2910164, and SNP rs2292832, respectively. The true effect size was estimated by an odds ratio (OR) and 95 % confidence intervals (CI) with the fixed effects model. For SNP rs11614913, the risk of CRC was more pronounced in the C allele carriers as compared with the T allele carriers among the subjects of Asian decent (CC vs. TT: OR = 1.18, 95% CI 1.01-1.38, P = 0.734; CC vs. TC + TT: OR = 1.18, 95% CI 1.02-1.36, P = 0.573; C vs. T: OR = 1.08, 95% CI 1.00-1.17, P = 0.775). SNP rs2910164 and SNP rs2292832 were not found to be significantly associated with CRC risk. This meta-analysis reveals that SNP rs11614913, but not SNP rs2910164 and SNP rs2292832, may contribute to susceptibility to CRC in an Asian-specific manner.

Objective Today, the development mode of public hospitals in China is turning from expansion to efficiency, and the management mode is turning from extensive to refined. This study aims to evaluate the efficiency of clinical departments in a Chinese class A tertiary public hospital (Hospital M) to analyze the allocation of hospital resources among these departments providing a reference for the hospital management. Methods The hospitalization data of inpatients from 32 clinical departments of Hospital M in 2021 are extracted from the hospital information system (HIS), and a dataset containing 38,147 inpatients is got using stratified sampling. Considering the non-homogeneity of clinical departments, the 38,147 patients are clustered using the K-means algorithm based on workload-related data labels including inpatient days, intensive care workload index, nursing workload index, and operation workload index, so that the medical resource consumption of inpatients from non-homogeneous clinical departments can be transformed into the homogeneous workload of medical staff. Taking the numbers of doctors, nurses, and beds as input indicators, and the numbers of inpatients assigned to certain clusters as output indicators, an input-oriented BCC model is built named the workload-based DEA model. Meanwhile, a control DEA model with the number of inpatients and medical revenue as output indicators is built, and the outputs of the two models are compared and analyzed. Results Clustering of 38,147 patients into 3 categories is of better interpretability. 14 departments reach DEA efficient in the workload-based DEA model, 10 reach DEA efficient in the control DEA model, and 8 reach DEA efficient in both models. The workload-based DEA model gives a relatively rational judge on the increase of income brought by scale expansion, and evaluates some special departments like Critical Care Medicine Dept., Geriatrics Dept. and Rehabilitation Medicine Dept. more properly, which better adapts to the functional orientation of public hospitals in China. Conclusion The design of evaluating the efficiency of non-homogeneous clinical departments with the workload as output proposed in this study is feasible, and provides a new idea to quantify professional medical human resources, which is of practical significance for public hospitals to optimize the layout of resources, to provide real-time guidance on manpower grouping strategies, and to estimate the expected output reasonably.

Objective Today, the development mode of public hospitals in China is turning from expansion to efficiency, and the management mode is turning from extensive to refined. This study aims to evaluate the efficiency of clinical departments in a Chinese class A tertiary public hospital (Hospital M) to analyze the allocation of hospital resources among these departments providing a reference for the hospital management. Methods The hospitalization data of inpatients from 32 clinical departments of Hospital M in 2021 are extracted from HIS, and a dataset containing 38,147 inpatients is got using stratified sampling. Considering the non-homogeneity of clinical departments, the 38,147 patients are clustered using the K-means algorithm based on workload-related indicators including inpatient days, intensive care workload index, nursing workload index, and operation workload index, so that the medical resource consumption of inpatients from non-homogeneous clinical departments can be transformed into the homogeneous workload of medical staff. Taking the numbers of doctors, nurses and beds as input indicators, and the numbers of inpatients assigned to certain clusters as output indicators, an input-oriented BCC model is built named the workload-based DEA model. Meanwhile, a control DEA model with the number of inpatients and medical revenue as output indicators is built, and the outputs of the two models are compared and analyzed. Results The clustering of 38,147 patients into 3 categories is of better interpretability. 14 departments reach DEA efficient in the workload-based DEA model, 10 reach DEA efficient in the control DEA model, and 8 reach DEA efficient in both models. The workload-based DEA model gives a relatively rational judge on the increase of income brought by scale expansion, and evaluates some special departments like Critical Care Medicine Dept., Geriatrics Dept. and Rehabilitation Medicine Dept. more properly, which better adapts to the functional orientation of public hospitals in China. Conclusion The design of evaluating the efficiency of non-homogeneous clinical departments with the workload as output proposed in this study is feasible, and provides a new idea to quantify professional medical human resources, which is of practical significance for public hospitals to optimize the layout of resources, to provide real-time guidance on manpower grouping strategies, and to estimate the expected output reasonably.