社区卫生服务体系中慢性病综合防制的思考

结合平江区慢性病防制的现状,分析了社区卫生服务建设与慢性病综合防制的关系,探讨了发展社区卫生服务与慢性病综合防制之间的积极意义.认为社区卫生服务机构是开展慢性病综合防制的理想场所,通过社区卫生服务提供相应的健康教育、行为干预和疾病监测是慢性病综合防制的理想模式.     

定量论证城市疾病防制功能以以切实落实状况

运用我国大、中、小城市疾病防制机构总体数据模拟论证“我国防制功能难以切实落实的作用机制定量模型”,发现80年代以来,我国城市疾病防制机构财政投入相对其所开展的无偿服务呈现财政投入萎缩,疾病防制机构有偿服务支出占疾病防制机构业务支出的比重呈现增加趋势;扣除物价因素影响后,1997年与1986年相比,有偿服务膨胀的财政责任明显大于防疫机构疾病防制机构自身责任。与全国总体相比,城市防疫机构疾病防制机构有偿服务膨胀归因于自身责任比重更大。     

利用各类农村疾病防制机构数据定量验证疾病防制功能难以切实落实模型

利用各类农村疾病防制机构的数据定量验证疾病防制功能难以切实落实模型,发现模型具有科学性。并分析其开展有偿服务的动机和后果,发现农村地区有偿服务开展的状况与经济分类出现相关趋势,四类农村地区目前即使通过开展有偿服务,也难以落实疾病防制功能。     

农村疾病防制机构面临的问题及对策

目的 了解农村疾病防制机构面临的困难和问题，提出相应的对策。方法 采用问卷法对样本地区政府部门及卫生部门的领导、卫生技术人员进行意向调查分析。结果 政府对疾病防制事业投入不足，疾病防制机构普遍存在“重有偿轻无偿、重医轻防”现象，农村卫生服务网络运作不协调，基层农村卫生机构基础设施落后、卫生人才匮乏。结论 政府增加投入，提高农村卫生服务网络整体功能。     

新时期军队传染病预防和控制面临的形势与策略

为了解和掌握军队传染病面临的形势，提出预防策略，在广泛收集有关资料的基础上，着重阐述了军队现阶段传染病防制的外围形势、传染病对军队人员健康的危害和对军事行动的影响，以及未来战争中的传染病问题。根据我军实际，提出了以下几点相应的防制策略：①坚持预防为主方针，运用新医学模式指导军队传染病防制工作；②搞好国防前哨和战略要地的流行病学调查，进一步加强疾病监测工作；③全面提高突发事件的应急处理能力；④加强传染病防制领域的科学研究，以适应新时期军队传染病预防与控制的需要     

社会互动:疾病防制功能难以落实作用机制模型的逻辑推论

该文依据卫生系统宏观模型思路,应用层次分析法,逻辑推论了疾病防制系统焦点问题(疾病防制功能难以切实落实到位)的直接相关因素和间接促发因素,认为问题的起始根源是政府对公共产品提供机构的财政投入不足,致使疾病防制功能中无偿服务部分缺乏激励监督,同时为了弥补财政投入的不足政府不得不允许疾病防制机构提供部分“有偿服务“但缺乏有效的规范和制约,导致功能偏废(重有偿轻无偿),部分缺乏收益的疾病防制功能难以切实落实.并总结和建立了焦点问题的作用机制模型-“社会互动:我国疾病防制功能难以切实落实到位作用机制模型“.在此基础上,推论了农村三级卫生网及其组织的变化对农村疾病防制功能落实的影响.     

促使我国疾病防制工作进入良性循环的政策研究结果概述

介绍了“促使我国疾病防制工作进入良性循环的政策研究”项目的研究结果,包括我国疾病防制功能落实中呈现的关键问题,关键问题的根源和作用机制,根源作用下社区卫生服务功能落实中受到的观念和难点制约,促使疾病防制工作进入良性循环的政策思路和定量论证结果。     

实行四个结合,认真贯彻执行《传染病防治法》

湖北省谷城县在贯彻执行《传染病防治法》过程中，实行加强疫情报告管理与抓好疾病监测相结合；坚持冷链运转与重点抓好免疫针对疾病防制相结合；强化消毒管理与防制疾病传播，提高医疗质量相结合；经常性卫生监督与突击检查相结合。加大了执法力度，有效地预防、控制了传染病的发生和流行。     

运用全国疾病防制机构数据模拟论证疾病防制功能难以切实落实模型

运用全国疾病防制机构总体数据模拟论证“我国疾病防制功能难以切实 落实作用的机制定量模型”,发现1997年全国疾病防制机构财政投入相对其开展的无偿服务缺口为37．6万元－47．4万元,从80年代以来,疾病防制机构有偿服务支出占其业务支出比重呈现增加趋势,1997年达0．48％－0．53％。扣除物价因素影响,1997年与1986年相比,有偿服务收入膨胀的财政责任为77．00％－86．10％,疾病防制机构自身责任为13．90％－23．00％。     

决策树模型在制定疾病防制策略中的应用(一)

研究疾病的防制策略和保健对策是预防医学的主要任务。由于医学模式的演变以及疾病尤其是非传染病的病因往往是多因素综合作用,要求我们在制定疾病防制策略和保健对策时,必须考虑到各种影响因素,综合地采取措施。同时,由于病因的多因素,就决定了防制     

Sensitivity analysis for validating expert opinion as to ideal data set criteria for transport modeling

Environmental fate modeling results are often used in risk assessment without adequately considering uncertainty in exposure predictions. Sensitivity analysis is fundamental to model validation and error prediction since sensitive model input parameters account for the largest variance in model prediction. Once identified, sensitive model input parameters can be used to propagate parametric uncertainty in numerical predictions. Output sensitivity to variation in input code sequences was investigated for the pesticide root zone model (PRZM 3) using Plackett-Burman analysis for six runoff and leaching data sets. The analysis utilized an incomplete block factorial design with even parameter weighting and uniform proportional input perturbation. Timing and duration of key period rainfall were assumed a priori to be dominant sensitive inputs. Thus, meteorological data were fixed, allowing identification of additional input components contributing to model sensitivity. Results validated expert modeler assumptions concerning parameters most critical for model validation. For leaching data sets, the application rate, soil bulk density (an indicator of available water-holding capacity), chemical partition coefficient, and pesticide degradation rates were commonly the most sensitive inputs. For runoff data sets, the in-crop runoff curve number was the most significant input governing pesticide loss in runoff and erosion flux. The chemical partition coefficient, soil and foliar decay rates, and soil bulk density were also common sensitive components for runoff predictions. These commonly observed sensitive components for runoff and leaching prediction need to be carefully considered in the design and conduct of relevant field studies, modeling assessment of such studies, and future improvements in algorithms for environmental transport modeling.     

Measuring decision sensitivity: a combined Monte Carlo-logistic regression approach.

Modeling of the uncertainty of multiple input variables for a complex decision problem complicates sensitivity analysis. A method of analysis comprising stochastic simulation of the model and logistic regression of the simulated dichotomous decision variable against all of the input variables yields a direct measure of the importance of input variables to the decision. This method is demonstrated on a previously analyzed clinical decision either to continue observation or to immediately treat with anticoagulants a woman presenting with deep vein thrombosis in the first trimester of pregnancy. A relative measure of the importance of each input variable in causing a change of decision is estimated by calculating the change in the log odds attributable to variation of each input variable over its range of uncertain values compared with the total change of log odds from variation of all input variables. This method is compared with alternative measures of input variable importance, and is found to be a simple yet powerful tool for gaining quantitative insight into the nuances of a decision model.     

Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccumulation models

First-order analytical sensitivity and uncertainty analysis for environmental chemical fate models is described and applied to a regional contaminant fate model and a food web bioaccumulation model. By assuming linear relationships between inputs and outputs, independence, and log-normal distributions of input variables, a relationship between uncertainty in input parameters and uncertainty in output parameters can be derived, yielding results that are consistent with a Monte Carlo analysis with similar input assumptions. A graphical technique is devised for interpreting and communicating uncertainty propagation as a function of variance in input parameters and model sensitivity. The suggested approach is less calculationally intensive than Monte Carlo analysis and is appropriate for preliminary assessment of uncertainty when models are applied to generic environments or to large geographic areas or when detailed parameterization of input uncertainties is unwarranted or impossible. This approach is particularly useful as a starting point for identification of sensitive model inputs at the early stages of applying a generic contaminant fate model to a specific environmental scenario, as a tool to support refinements of the model and the uncertainty analysis for site-specific scenarios, or for examining defined end points. The analysis identifies those input parameters that contribute significantly to uncertainty in outputs, enabling attention to be focused on defining median values and more appropriate distributions to describe these variables.     

Influences of parameter uncertainties within the ICRP-66 respiratory tract model: a parameter sensitivity analysis

An important aspect in model uncertainty analysis is the evaluation of input parameter sensitivities with respect to model outcomes. In previous publications, parameter uncertainties were examined for the ICRP-66 respiratory tract model. The studies were aided by the development and use of a computer code LUDUC (Lung Dose Uncertainty Code) which allows probabilities density functions to be specified for all ICRP-66 model input parameters. These density functions are sampled using Latin hypercube techniques with values subsequently propagated through the ICRP-66 model. In the present study, LUDUC has been used to perform a detailed parameter sensitivity analysis of the ICRP-66 model using input parameter density functions specified in previously published articles. The results suggest that most of the variability in the dose to a given target region is explained by only a few input parameters. For example, for particle diameters between 0.1 and 50 microm, about 50% of the variability in the total lung dose (weighted sum of target tissue doses) for 239PuO2 is due to variability in the dose to the alveolar-interstitial (AI) region. In turn, almost 90% of the variability in the dose to the AI region is attributable to uncertainties in only four parameters in the model: the ventilation rate, the AI deposition fraction, the clearance rate constant for slow-phase absorption of deposited material to the blood, and the clearance rate constant for particle transport from the AI2 to bb1 compartment. A general conclusion is that many input parameters do not significantly influence variability in final doses. As a result, future research can focus on improving density functions for those input variables that contribute the most to variability in final dose values.     

公共卫生筹资来源分析：政府投入与私人投入

对我国卫生的政府投入和私人投入进行建模分析，结果表明，从总体上看，我国卫生领域内政府投入与私人投入之间存在替代关系。从长期来看，经济增长对政府投入和私人投入都具有正向的促进作用，现行平均税率对政府投入和私人投入具有负反馈作用。实证分析的结果所包含的政策含义在于：在增加政府投入、设置最优平均税率的同时，要着力吸引私人投入。     

Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge

Objectives

The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.

An example to illustrate the potential use of probabilistic modelling to estimate operator exposure to pesticides

To illustrate the potential for probabilistic simulation modelling of operator exposure a probabilistic model was constructed using the draft EUROPOEM II database. The model also incorporated actual pesticide use data. Although the EUROPOEM II database is not complete, and the resulting simulation has to be regarded as only provisional, the data were sufficient to illustrate some of the principles involved. The model was constructed using non-parametric empirical input distributions. Associations were observed in the input exposure data and were reflected in the model. The exposure and usage data were all variable and showed highly negatively skewed distributions and the technique avoided concerns about having to define single representative values for point estimates. Increasing the number of iterations improved the stability of the output, but as expected, resulted in higher exposures being predicted due to a combination of rare events. Analysis of the inputs for individual iterations giving high results indicated that, in this example, these were due to unrepresentatively high input values for inhalation exposure. Simulations that excluded these values were more stable and showed lower exposures, illustrating the need to ensure the validity of the input distributions.     

河北省某铁矿职业卫生投入产出分析

目的:探讨职业卫生经费投入和产出的关系,为相关部门决策提供依据。方法:于2018年9月,选取河北省某国有铁矿（开采历史10年以上,可代表一般类型的铁矿）为研究对象。通过调查收集企业一般情况、职业卫生投入、损失和产出相关指标,建立铁矿职业卫生经费投入产出表和模型,用matlab软件对经费投入与产出之间的数字关系进行求解。...     

正确处理为部队服务与医院建设的关系

本文论述了正确处理军队医院为部队服务与建设发展的关系。文章从正确处理为部队服务与为社会服务的关系,正确处理为部队服务投入与医院自身建设投入的关系,正确处理医院自身建设与支援部队卫生建设的关系三个方面结合本院实际进行了探讨。提出了在保障思想上坚持为部队服务不动摇,在保障方式上努力创新服务模式,在保障措施上把为兵服务落到实处;优先保证为部队服务的投入,合理把握医院自身建设投入,切实落实合理医疗投入;医院自身建设必须使部队官兵受益,帮助部队加强卫生建设等八项措施。     

外来医学媒介生物（蝇类）输入风险评估体系研究

目的采用多指标综合评估模式研究外来医学媒介生物（蝇类）的输入风险。方法采用专家调研法确定评估指标并建立评估指标体系；明确各风险指标之间的相互关系并建立一致化和无量纲化后的指标评判标准；选择并确定合适的风险分析数学模型，对外来医学媒介生物蝇类的输入风险进行评估。结果建立了外来医学媒介生物（蝇类）输入风险评估体系，即风险评估体系由6个一级指标及其下属的26个二级指标组成、结论明确了各风险指标之间的相互关系后通过多指标综合分析方法建立了风险评估模型，通过模型可计算出任意一种已鉴定出来的外来蝇类的输入风险值。