Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Does Monetary Support Increase the Number of Scientific Papers？ An Interrupted Time Series Analysis

Purpose： One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council （TUBITAK） to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so.Design/methodology/approach： We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science （WoS） database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series （ITS） analysis technique （also known as ＂quasi-experimental time series analysis＂ or ＂intervention analysis＂） to test if TOBITAK＇s support program helped increase the number of publications. We defined ARIMA （1,1,0） model for ITS data and observed the impact of TOBiTAK＇s support program in 1994, 1997, and 2003 （after one, four and 10 years of its start, respectively）. The majority of publications （93%） were full papers （articles）, which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis.Findings： TUBITAK＇s support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TUBITAK＇s support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations. Research limitations： Interrupted time series analysis shows if the ＂intervention＂ has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the＂intervention＂, other ＂event（s）＂ that might affect the time series data （e.g., increase in the number of research personnel over the years） should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher. Practical implications： TUBITAK＇s ＂cash-for-publication＂ program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as ＂micropayments.＂ Originality/value： Based on 25 years＇ worth of payments data, this is perhaps one of the first large-scale studies showing that ＂cash-for-publication＂ policies or ＂piece rates＂ paid to researchers tend to have little or no effect on the increase of researchers＇ productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs （based usually on bibliometric measures such as journal impact factors） and revising their reward policies.

Time-series analysis of the characteristic pressure fluctuations in a conical fluidized bed with negative pressure

The negative pressure conical fluidized bed is widely used in the pharmaceutical industry.In this study,experiments based on the negative pressure conical fluidized bed are carried out by changing the material mass and particle size.The pressure fluctuation signals are analyzed by the time and the frequency domain methods.A method for absolutely characterizing the degree of the energy concentration at the main frequency is proposed,where the calculation is to divide the original power spectrum by the average signal power.A phenomenon where the gas velocity curve temporarily stops growing is observed when the material mass is light,and the particle size is small.The standard deviation and kurtosis both rapidly change at the minimum fluidization velocity and thus can be used to determine the flow regime,and the variation rule of the kurtosis is independent of both the material mass and particle size.In the initial fluidization stage,the dominant pressure signal comes from the material movement;with the increase in the gas velocity,the power of a 2.5 Hz signal continues to increase.A method of dividing the main frequency by the average cycle frequency can conveniently determine the fluidized state,and a novel concept called stable fluidized zone proposed in this paper can be obtained.Controlling the gas velocity within the stable fluidized zone ensures that the fluidized bed consistently remains in a stable fluidized state.

Using Interrupted Time Series Design to Analyze Changes in Hand, Foot, and Mouth Disease Incidence during the Declining Incidence Periods of 2008-2010 in China

Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.

Rockfall seismic features analysis based on in situ tests:frequency, amplitude, and duration

In the seismic event classification,determining the seismic features of rockfall is significantly important for the automatic classification of seismic events because of the huge amount of raw data recorded by seismic stations in continuous monitoring. At the same time, the rockfall seismic features are still not completely understood.This study concentrates on the rockfall frequency content, amplitude(ground velocity), seismic waveform and duration analysis, of an artificial rockfall test at Torgiovannetto(a former quarry in Central Italy). A total of 90 blocks were released in the test, and their seismic signals and moving trajectories were recorded by four tri-axial seismic stations and four cameras, respectively. In the analysis processing,all the artificial rockfall signal traces were cut separately and the seismic features were extracted individually and automatically. In this study, the relationships between a) frequency content and impacted materials, b) frequency content and the distance between block releasing position and seismic station(source-receiver distance) were discussed. As a result, we found that the frequency content of rockfall focuses on 10-60 Hz and 80-90 Hz within a source-receiver distance of 200 m, and it is well correlated with impacted material and source-receiver distance. To evaluate the difference between earthquake and rockfall, 23 clear earthquake signals recorded in a seven month-long continuous seismic monitoring, carried out with the four seismic stations, were picked out, according to the Italian national earthquakes database(INGV). On these traces we performed the same analysis as in the artificial rockfall traces, and two parameters were defined to separate rockfall events from earthquake noise. The first one, the amplitude ratio, is related to the amplitude variation of rockfall between two stations and is greater than that of earthquakes, because of the higher attenuation occurring for rockfall events, which consists in high frequencies whereas for earthquakes it consists in low frequencies. The other parameter, the shape of waveform of signal trace, showed a significant difference between rockfall and earthquake and that could be a complementary feature to discriminate between both. This analysis of artificial rockfall is a first step helpful to understand the seismic characteristics of rockfall, and useful for rockfall seismic events classification in seismic monitoring of slope.

Uncertainty Analysis on Electric Power Consumption

The analysis of large time-series datasets has profoundly enhanced our ability to make accurate predictions in many fields.However,unpredictable phenomena,such as extreme weather events or the novel coronavirus 2019(COVID-19)outbreak,can greatly limit the ability of time-series analyses to establish reliable patterns.The present work addresses this issue by applying uncertainty analysis using a probability distribution function,and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul,South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic process automation.We first apply Facebook Prophet for conducting time-series analysis.The results demonstrate that the COVID19 outbreak seriously compromised the reliability of the time-series analysis.Then,machine learning models are developed in the TensorFlow framework for conducting uncertainty analysis based on modeled relationships between electric power consumption and outdoor temperature.The benefits of the proposed uncertainty analysis for predicting the electricity consumption of the hotel building are demonstrated by comparing the results obtained when considering no uncertainty,aleatory uncertainty,epistemic uncertainty,and mixed aleatory and epistemic uncertainty.The minimum and maximum ranges of predicted electricity consumption are obtained when using mixed uncertainty.Accordingly,the application of uncertainty analysis using a probability distribution function greatly improved the predictive power of the analysis compared to time-series analysis.

A Semi Automated Method for Laminated Sediments Analysis

We developed a software performing laminae counting, thickness measurements, spectral and wavelet analysis of laminated sediments embedded signal. We validated the software on varved sediments. Varved laminae are automatically counted using an image analysis classification method based on K-Nearest Neighbors (KNN) algorithm. In a next step, the signal corresponding to varved black laminae thickness variation is retrieved. The obtained signal is a good proxy to study the paleoclimatic constraints controlling sedimentation. Finally, the use of spectral and wavelet analysis methods on the variation of black laminae thickness revealed the existence of frequencies and periods which can be linked to known paleoclimatic events.

Experimental Study of Heat Transfer to Flowing Air inside a Circular Tube with Longitudinal Continuous and Interrupted Fins

Experimental investigations have been performed to determine the detailed module-by-module pressure drop and heat transfer coefficient of turbulent flow inside a circular finned tube. The tubes are provided with longitudinal fins continuous or interrupted in the stream wise direction by arranging them both in a staggered and in-line manner. Experiments are carried out for two different fin geometries, with two numbers of fins (N = 6 and 12). All tested finned tubes have 16 modules each with length equal to the tube diameter (L = D = 30 mm). The thermal boundary condition considered here, is a uniform heat flux. The module-by-module heat transfer coefficient is found to vary only in the first modules, and then attained a constant thermally periodic fully developed value after eight to twelve modules. The results also showed that in the periodic hydrodynamic fully developed region, the value of the pressure drop along the tube with continuous fins is greater than that of the in-line arrangement, and lower than that of the staggered arrangement. Furthermore, the results showed that in the periodic fully developed region, the tube with continuous fins produces a greater value of the heat transfer coefficients than that the tube with interrupted fins, especially through a high range of Reynolds number (5 × 104 > Re > 2 × 104). The tube with Staggered arrangement of fins produces a greater value of the heat transfer coefficient than the tube with continuous fins and the in-line arrangement finned tube at low Reynolds number (Re < 1.2 × 104).). It was found that the fins efficiency is greater than 90 percent;in the worst case (maximum Reynolds number with continuous fins tube).

Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

The downward shortwave radiation(DSR)is a key input parameter for land surface models and climate models.Based on the daily averaged Global Land Surface Satellite downward shortwave radiation(GLASS-DSR)dataset over the Yunnan-Kweichow Plateau(YKP)from 1984 to 2018,this paper analyzes variation trend and breakpoints of DSR.The results show that:annual averaged DSR decreases at a decreasing rate of-1.84 W·m^(-2)·decade^(-1) over the YKP from 1984 to 2018;the overall distribution of interannual averaged DSR shows higher in the mid-west,and gradually decreasing from west to northeast over the YKP;the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon;monthly averaged DSR reaches its maximum in May and its minimum in December;breakpoints are found in the seasonal and trend components of daily averaged DSR.Eleven driving factors are examined for their effects on DSR variation,including annual average temperature,precipitation,10 m wind speed,aerosol optical thickness(AOT),total cloud cover,elevation,slope,aspect,longitude,latitude,and climate zones.According to thefindings,AOT predominates in the spatio-temporal distribution of DSR over the YKP.This study will contribute to studies related to climate change and highland radiation.

DRG支付对急性胰腺炎患者住院费用影响分析——基于中断时间序列模型研究

目的:本研究选取浙江省某三甲医院2019年至2021年的急性胰腺炎患者结算数据,评估DRG改革前后患者住院费用的变化,旨在为政策的持续优化提供实证依据。方法:采用中断时间序列模型分析政策执行前后住院费用变化趋势,并通过Spearman相关分析探讨住院总费用与各构成部分费用的关联度。结果:分析显示,改革前住院总费用每月上升1639.3元(P<0.001),改革后每月下降1924.1元(P<0.001),药品费和医疗服务费每月分别下降910.0元和480.3元(P<0.001)。改革后,医疗服务费与住院总费用的相关性最强(r=0.941,P<0.001)。结论:研究发现,随着DRG改革实施,样本医院急性胰腺炎患者的住院费用逐年下降,表明改革效果显著。医疗机构通过加强药品和耗材管控及规范医疗行为,有效体现了政策要求。为进一步提升DRG政策的调控作用,建议调整医疗服务价格以激励医务人员,同时建立动态调整机制,优化DRG分组方案,确保政策更好地适应临床需求和医疗实践。

基于中断时间序列分析的江苏省某三甲综合医院冠脉支架集中带量采购政策实施效果研究

目的:通过分析江苏省某三甲综合医院冠脉支架集中带量采购政策实施前后的冠脉支架使用情况,评估冠脉支架集中带量采购政策实施效果,为冠脉支架集中带量采购政策协议期满后接续采购的持续优化及完善提供建议。方法:提取2018年10月~2022年10月江苏省某三甲综合医院冠脉支架使用量数据,采用中断时间序列分析方法分析江苏省冠脉支架集中带量采购政策和国家组织冠脉支架集中带量采购政策对江苏省某三甲综合医院冠脉支架使用数量的影响,同时分析该院实施冠脉支架集中带量采购政策前后的进口和国产冠脉支架使用情况。结果:江苏省冠脉支架集中带量采购政策执行后,冠脉支架使用量呈显著上升趋势(β_(3)=4.62,P=0.017);国家组织冠脉支架集中带量采购政策执行后,冠脉支架使用量的水平变化和趋势变化均不显著。江苏省冠脉支架集中带量采购政策实施后,国产冠脉支架和进口冠脉支架使用量都呈不显著上升趋势(β_(3)=2.46,P=0.232;β_(3)=2.16,P=0.173);国家组织冠脉支架集中带量采购政策实施后,国产冠脉支架使用量无显著变化(β_(5)=-1.09,P=0.589),进口冠脉支架使用量瞬间下降(β_(4)=-46.6,P=0.018),随后呈上升趋势,但变化不显著(β_(5)=0.16,P=0.919)。结论:该院总体上冠脉支架集中带量采购政策实施效果良好,江苏省冠脉支架集中带量采购政策实施后增加了该院冠脉支架使用量;国家组织冠脉支架集中带量采购政策促使该院冠脉支架使用量稳中有涨,其中国产冠脉支架整体呈上升趋势,进口冠脉支架使用量整体下降。后续国家冠脉支架集中带量采购政策建议探索多元化的分配方式,合理分配国产冠脉支架和进口冠脉支架中选品种,规范冠脉支架合理使用。

脉内频率 时延捷变雷达抗间歇采样转发干扰方法

间歇采样转发干扰凭借快速准确的采样和转发手段,利用脉压雷达的匹配滤波特性形成假目标群,严重影响目标检测和跟踪性能。针对这一问题,结合波形设计和滤波思想,设计一种脉内频率时延捷变波形,并在此基础上提出一种基于时频分析的抗间歇采样转发干扰方法。首先,将线性调频信号划分为多个子脉冲,并在子脉冲之间增加随机时延。然后,提取未被干扰的子脉冲进行目标位置的准确判断,结合子脉冲的频率和随机时延等先验信息,利用迭代阈值分割算法去除强干扰信号。最后,通过构造时频域和时域带通滤波器分别进行子脉冲滤波和时域滤波处理。仿真实验结果表明,所提方法在不同干信比、信噪比和非同步采样情况下具有较好的抗间歇采样转发干扰性能。

基于间断时间序列分析的三明市公立医院DRG改革效果的综合评估

[目的]分析三明市C-DRG和CN-DRG医保支付方式改革对二级、三级公立医院医疗收入、服务效率和患者负担的影响。[方法]采用间断时间序列、灰色关联度和结构变动度分析方法对2015-2019年22家公立医院的医药收入等指标进行回顾性分析。[结果]三明市实施DRG医保支付方式改革后二级医院的医疗收入降低5.2%,对三级医院的医疗收入没有显著影响;二级、三级医院的平均住院天数和病床使用率无显著改变;三级医院的门急诊次均费用增长2%,出院例均费用未受显著影响;二级医院的出院例均费用降低10.8%后增长8.4%。[结论]在三明市的DRG改革下,公立医院情况稳中向好;DRG可以完善临床路径,促进医疗机构的成本管控,未来的医改进程中,应考虑不同等级医院特点,加强改革的监督管理。

公立医院科室绩效方案改革对DIP患者住院费用影响——基于中断时间序列分析

目的 探讨医保按病种分值付费(Diagnosis-Intervention Packet,DIP)背景下公立医院科室绩效方案改革对DIP患者住院费用的影响,为优化公立医院科室绩效管理制度、降低DIP患者住院费用提供参考。方法 收集广东省某县级人民医院2020年7月—2023年7月DIP患者的住院费用数据,采用中断时间序列分析法描述例均住院费用变化趋势,分析该院科室绩效方案改革对DIP患者住院费用的影响。结果 医院科室绩效方案改革后,DIP患者总费用下降1 071.93元,并且每月继续下降116.39元。同时,科室绩效方案改革主要对居民医保DIP患者发挥作用,对职工医保DIP患者没有明显影响。结论 公立医院科室绩效方案改革明显降低了DIP患者的总费用及其分项费用,但是对不同险种DIP患者的影响不同。应进一步完善公立医院科室绩效管理机制,建立与医疗效能、医疗质量等相关的多维度绩效考核指标体系,因地制宜推动公立医院科室绩效方案改革,促进公立医院高质量发展。

317例肺结核患者中断治疗影响因素分析

目的:分析317例肺结核患者中断治疗情况及影响因素,并提出相应对策,为提升肺结核患者管理质量提供依据。方法:以北京市疾病预防控制中心结核病门诊部2012—2021年登记管理的2715例肺结核患者为研究对象,收集“中国疾病预防控制信息系统”的子系统“结核病管理信息系统”和门诊病案记录中的人口学信息及临床诊疗信息,分析肺结核患者中断治疗情况,采用单因素和多因素非条件logistic回归方法分析中断治疗的影响因素。结果:2715例肺结核患者中,中断治疗患者317例,中断治疗率为11.68%。单因素分析结果显示,女性患者的中断治疗率(10.11%,109/1078)低于男性患者(12.71%,208/1637),差异有统计学意义(χ^(2)=4.244,P=0.039);25~44岁组肺结核患者的中断治疗率(8.21%,96/1170)低于<25岁组(11.38%,57/501)、45~64岁组(14.75%,86/583)和≥65岁组(16.92%,78/461),差异有统计学意义(χ^(2)=31.349,P<0.001);职业分类中,学生/教师/医务人员/干部职员肺结核患者的中断治疗率(7.29%,72/988)低于农民/民工/工人(13.37%,25/187)、离退休/家务及待业/其他(14.65%,194/1324)和商业、餐饮服务/公共场所服务员(12.04%,26/206)的患者,差异有统计学意义(χ^(2)=30.373,P<0.001);初治肺结核患者中断治疗率(11.05%,274/2480)低于复治肺结核患者(18.30%,43/235),差异有统计学意义(χ^(2)=10.939,P<0.001)。多因素分析结果显示,与25~44岁组患者相比较,<25岁组(OR=1.536,95%CI:1.082~2.180)、45~64岁组(OR=1.538,95%CI:1.101~2.149)和≥65岁组(OR=1.596,95%CI:1.095~2.326)更容易发生中断治疗;与学生/教师/医务人员/干部职员相比较,商业、餐饮服务/公共场所服务员(OR=1.693,95%CI:1.050~2.731)、农民/民工/工人(OR=1.681,95%CI:1.014~2.787)、离退休/家务及待业/其他职业(OR=1.776,95%CI:1.278~2.469)的患者更容易发生中断治疗;复治患者比初治患者更容易发生中断治疗(OR=1.487,95%CI:1.031~2.145)。在疗程的前2个月中断治疗的肺结核患者占全部中断治疗患者的54.57%(173/317)。结论:应对男性、离退休/家务及待业/其他、≥65岁的老年人、复治肺结核患者群体采取更有针对性的督导及健康教育,并加强治疗前2个月的管理,降低中断治疗率。

集中带量采购政策对注射用喹诺酮类药物处方行为和细菌耐药性影响分析

目的 研究集中带量采购政策对注射用喹诺酮类药物使用情况的影响和细菌耐药情况的变化。方法 提取南京鼓楼医院2017年1月—2019年10月和2020年1月—2022年10月注射用喹诺酮类药物的使用数据,采用中断时间序列模型分析集采政策对其产生的影响;基于集采政策相关的调查问卷分析本院医务人员对政策的看法及产生相应行为的原因;利用相应时间段细菌耐药率、检出量进行相关性分析。结果 第三批集采政策实施后,莫西沙星使用量呈下降趋势;第五批集采政策实施后,莫西沙星和环丙沙星的使用量呈上升趋势,且相较于对照组变化存在显著性差异;左氧氟沙星使用量呈持续下降趋势。问卷调查显示,喹诺酮类药物作为替代药优先选择,与第五批集采后喹诺酮类药物使用量的上升一致。喹诺酮类药物耐药率受政策影响不明显,与喹诺酮类药物用量无高度相关性。结论 喹诺酮类药物在第三批集采政策实施后使用量下降,在第五批实施后也未出现异常增长现象。相关病原菌耐药率与药物使用量无高度相关性,政策对耐药率变化的影响还需长期监测。

应用中断时间序列分析评价医院等级评审对医务人员手卫生依从性的影响

目的应用中断时间序列分析模型,评价综合干预措施对医务人员手卫生依从性的影响。方法选取某三级医院全部临床和医技科室的工作人员为研究对象,在三级医院等级评审迎检筹备期间,实施培训、调查、竞赛等多种形式的手卫生综合干预措施。收集干预前后手卫生依从性监测数据,应用中断时间序列方法进行模型构建,评价干预的即刻效果和远期效果。结果医务人员手卫生依从率由干预前的67.58%提升至干预后的81.43%。中断时间序列分析结果显示,干预实施后总体医务人员的手卫生依从率即刻提升19.717%(t=5.473,P<0.001);但其斜率改变量为-1.995(t=-2.249,P=0.048),提示干预实施后手卫生依从率的下降趋势扩大;干预对不同岗位工作人员均有即刻提升效果,以医生群体提升幅度最大;对不同岗位工作人员的斜率改变量均为负值,以护士群体的改变量绝对值最小。结论综合干预措施可以有效提升医务人员手卫生依从性,但其远期效果不甚理想。中断时间序列模型可有效用于手卫生依从性监测数据的分析。

中断时间序列分析在基本医疗保险支付方式改革效果评估中的应用研究

目的评估基本医疗保险支付方式改革在控费、规范医疗行为和缓解患者负担方面的效果,为完善支付方式改革提供参考。方法采用中断时间序列分析支付方式改革前后当天再入院率、次均住院费用和自付比例的变化,采用利益相关者定性访谈深入了解变化可能的原因和改革建议。结果参保患者次均住院费用、自付比例改革初期有显著下降,后呈上升趋势,住院人次增幅减缓、当天再入院率降幅减缓。中断时间序列分析结果显示,支付方式改革后参保患者住院总人次增长速度由原来的600人次/月下降为20人次/月;当天再入院率在改革后短期内上升0.237%,但长期呈下降趋势,下降速度由改革前的0.014%/月变为0.003%/月。在改革后短期内,参保患者次均住院费用下降786.360元,自付比例下降2.089%。结论长沙市城镇职工基本医疗保险支付方式改革初期,控费、缓解患者就医负担的效果明显,但需要加强远期的监管,尤其是对于分解住院和患者负担的控制。