四川大山区血吸虫病传播动力学及控制的研究

目的 利用数学流行病学和计算机模拟方法研究山区血吸虫病的传播因素及其控制策略.方法 基于在四川西昌20个村的血吸虫病流行病学调查,建立基于本地传播因素的血吸虫病传播模型.利用蒙特卡罗模拟对模型进行模拟,采用二分法用3个村的数据来效验模型,并用经效验的模型来分析在3种控制策略下本地血吸虫病传播的趋势.结果 现场调查显示以村为单位的人群血吸虫病感染率和感染强度分别在3%～73%和平均克粪虫卵数(epg)在0.1至100.0之间.流行因素包括居民职业、疫水接触、微环境参数(气温、降雨),被整合入模型中.经二分法效验的模型预测显示连续化疗(50%～60%的覆盖率)在6年内能将感染强度降低30%～80%,但不能降低传播潜力;所以化疗一旦停止,传播即反弹.持续的局部的环境干预,如灭螺和虫卵控制(30%～50%覆盖率),可以将传播控制在半稳定水平;只有持续的包括化疗和环境干预在内的综合控制,在5～10年间才能将传播控制到很低的水平,甚至阻断传播.结论 利用传播模型结合现场数据能够帮助分析影响血吸虫病本地传播的因素,并帮助制定相应的控制策略;血吸虫病的控制应建立在对本地因素的认识和可持续的环境干预基础上.     

南京市浦口区血吸虫病传播阻断达标考核结果

目的评价浦口区血吸虫病防治效果。方法收集2016年汤泉街道九龙村、顶山街道大新村、桥林街道周营村人畜血吸虫病疫情、螺情和相关因素的调查数据进行分析。结果 2016年3个村血吸虫病血清学阳性率以及居民感染率分别为1.08%和0,家畜感染率为0;2016年3个村共调查面积231万平方米,查获钉螺1只,钉螺感染率为0。结论浦口区人畜血吸虫病感染率处于较低水平,无钉螺感染,达到血吸虫病传播阻断标准。     

三峡库区可能传播血吸虫病的危险因素及其防制对策

目的 掌握三峡库区可能传播血吸虫病的危险因素并提出相应的防制对策。方法 采用流行病学、免疫学和现场调查相结合的方法,调查三峡库区流动人口、库区移民和家畜血吸虫病传染源可能输入库区的潜在危险因素。运用钉螺生态学的方法,观察钉螺在模拟环境中的生存繁殖状况,并提出防止血吸虫病传染源和钉螺可能输入库区的对策和措施。结果 从库区流动人口来自血吸虫病疫区的175人中,查出1例间接血凝试验(IHA)和环卵沉淀试验(COPT)均阳性者。通过2个年度的观察,肋壳钉螺和光壳钉螺都能在模拟环境中生存、繁殖。结论 血吸虫病传染源已扩散到库区,一旦钉螺输入到库区,将会构成血吸虫病在库区流行。为及早杜绝隐患,提出了相应的防范措施和对策。     

湖北省血吸虫病流行区初中生血吸虫病检查情况及其影响因素

目的 了解湖北省血吸虫病流行区初中学生血吸虫病检查情况,分析其影响因素,为初中生血吸虫病防治策略提供参考依据。方法 将湖北省血吸虫疫区分为传播控制区和疫情控制区,随机整群抽取3 204 名初中生进行问卷调查。结果 中学生血吸虫病检查率为62.5%;年龄、疫区类型、血防知识水平、血吸虫病害怕程度以及接受学校、血防工作人员健康教育频次不同的中学生血吸虫病检查率不同,差异有统计学意义（均有P<0.05）;多因素Logistic回归显示疫区类型、血防知识水平和接受学校、血防工作人员健康教育是中学生参与血吸虫病检查的影响因素。结论 湖北省血吸虫病流行区初中学生血吸虫病检查率较低,在传播控制区开展健康教育,特别是血防工作人员健康教育可能是提高中学生血吸虫病检查率的有效途径。     

镜湖湿地公园血吸虫病存在危险性与防治对策

目的:探讨镜湖湿地公园血吸虫病传播流行的潜在危险因素,为完善血吸虫病防治监测方案和应急预案提供依据.方法:在镜湖公园进行钉螺模拟生存试验,并调查辖区内引进的外来生物、家畜以及往返于湖南、湖北、江西等血吸虫尚未达到控制标准县和疫情回升县的流动人口等血吸虫病传染源等潜在危险因素.找出血吸虫病监测的重点.结果:镜湖公园适宜钉螺孳生;血吸虫病传染源主要是往返于流行区的流动人口;从血吸虫病流行区引进的外来生物、家畜是血吸虫病流行的潜在危险因素;镜湖公园已成为血吸虫病的潜在流行区.结论:镜湖湿地公园环境非常适宜血吸虫是间宿主钉螺生长繁殖,一旦有外来生物携带钉螺,就会存在孳生的风险.加强输入性传染源的监测与预警,建立多部门联防联控机制,及时发现并处置输入性血吸虫疫情是巩固血吸虫病防治效果的重要保障.     

四川省血吸虫病传播控制地区疫情回升的影响因素研究

目的探讨四川省血吸虫病传播控制地区疫情回升的影响因素,为遏制疫情回升的措施提供基础。方法选择2007-11/12对疫情回升的德阳市旌阳区和中江县28个自然村的常住人口进行粪检中的阳性者为病例,粪检阴性者为对照组,采用1∶2匹配的病例对照研究,按性别、年龄±2岁匹配,共收集161对病例和对照。采用Remark Of-fice OMR 6.0和Epidata 3.1建立数据库,Epi Info 2000对数据进行单因素和多因素条件Logistic回归分析。结果经过多因素条件Logistic检验,家养黄牛(OR=1.759,P=0.02)和接触疫水(OR=2.381,P=0.008)是四川省血吸虫病传播控制地区疫情回升的危险因素。结论黄牛和减少接触疫水是四川省血吸虫病传播控制地区疫情回升的干预重点。     

IF OR函数及数据筛选在血吸虫病疫情控制达标预评估中的应用

为实现湖北省荆州市血吸虫病疫情控制目标,2007年荆州市对所辖的疫区村开展了大规模调层查病工作,对调查的流行村预评估标准数据进行汇总、分析,应用IF（选择性返回函数）、OR（取并集）及数据筛选方法,高效、便捷筛选出达到疫情控制标准的村和审核血清学检查最小样本量。现将应用结果报告如下。     

多水平logistic回归模型在血吸虫病流行因素研究中的优越性

目的探讨在血吸虫病流行因素的研究中多水平logistic回归模型相对于传统logistic回归模型的优越性。方法分别使用多水平logistic回归模型和传统logistic回归模型分析血吸虫病流行因素。结果多水平logistic回归模型分析中有统计学意义的变量在传统logistic回归分析中均有统计学意义,但在传统logistic回归分析中有意义的几个变量,如人均收入、无害化厕所比例,却没有进入多水平logistic回归模型方程。结论与传统logistic回归模型相比,多水平模型更适合用来研究不同层次的血吸虫病的流行因素。     

三峡库区血吸虫病潜在流行因素与监测指标研究

[目的]研究三峡建坝后血吸虫病潜在传播的危险因素,为制定三峡库区血吸虫病监测和预防方案提供依据. [方法]2005～2006年,调查三峡建坝后生态环境变化、自血吸虫病疫区引进的植物情况;流动人口和引进的动物的血吸虫病感染情况(血清HHA检测);建坝后社会经济发展变化对血吸虫病传播的潜在影响因素. [结果]三峡库区存在适宜钉螺孳生的环境、从血吸虫病疫区引进大量植物,未发现钉螺;调查流动人口552人,曾感染血吸虫病患病率为2.17%,血清抗体阳性率为1.45%;流动人员的血防知晓率(19.02%)显著高于当地居民(4.85%);从疫区引进大量牲畜,未发现感染血吸虫动物.社会经济发展可能增加血吸虫病传染源传入库区的危险;当地居民生活生产习惯有利于血吸虫感染.监测指标包括引进疫区植物数量、钉螺监测、血清抗体阳性率、粪检阳性率、血防知识知晓率等.[结论]三峡库区存在血吸虫病潜在传播的危险因素,应加强植物引进、流动人员和动物的传染源输入的监测和预防.     

卫生统计

校正中心效应的非劣性检验;基于秩次的R类稳健回归;用自回归模型预测流感样病例数的变化趋势;多层模型方法及其在血吸虫病传播风险因子识别研究中的应用;综合医院综合效益评价的病例组合模型研究.     

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births ( n  = 254, multiple 18%; n  = 176, multiple 9%; n  = 10 098, multiple 3%; n  = 1585, multiple 8%) were analysed.
With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling.
We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.     

Structured correlation in models for clustered data

Correlation is always a concern in the analysis of clustered data. One area of interest is to develop a general correlation modelling approach for high dimensional data with unbalanced hierarchical and heterogeneous data structures, e.g. multilevel data. Commonly used correlation structures might have limitation for such situations. In this paper, we propose two extensions, multiblock and multilayer correlations. These methods are very flexible in modelling correlation and can be incorporated in many multivariate approaches, while the major discussion focuses on the applications under the generalized estimating equations (GEE) methods. The approaches are especially useful in GEE when each cluster is large and complex but the number of clusters is small. If an incorrect correlation is applied to such data, the results are less efficient. Multiblock and multilayer correlations extend GEE methods to model complicated multilevel data with arbitrary number of levels and cluster size. The extended estimating equation for correlation parameters has an orthogonal property, and the computation is very efficient. A simulation study compares the conventional methods versus the proposed methods, and it shows the gain in relative efficiency and the flexibility in modelling various structures.     

Multilevel modelling and malaria: a new method for an old disease

BACKGROUND: Malaria is influenced by a web of individual and ecological factors, i.e. factors relating to people and relating to environment. For a long time analysing these factors concurrently has raised statistical problems. Multilevel modelling provides a new attractive solution, which is still uncommon in tropical medicine. METHODS: Using an actual data set of 3864 individuals from 38 villages of the Highland Madagascar, a two-level modelling process is presented. Individual malaria parasitaemia is modelled step by step according to age (individual factor), altitude, and DDT indoor house-spraying status (village factors). RESULTS: The hierarchical organization of a data set in levels, fixed and random effects, and cross-level interactions are considered. Accurate estimations of standard errors, impact of unknown or unmeasured variables quantified and accounted for through random effects, are the highlighted advantages of multilevel modelling. CONCLUSION: While not denying the importance of understanding an aetiological chain, the authors recommend an increased use of multilevel modelling, mainly to identify accurately ecological targets for public health policy.     

A multilevel model framework for meta-analysis of clinical trials with binary outcomes

In this paper we explore the potential of multilevel models for meta-analysis of trials with binary outcomes for both summary data, such as log-odds ratios, and individual patient data. Conventional fixed effect and random effects models are put into a multilevel model framework, which provides maximum likelihood or restricted maximum likelihood estimation. To exemplify the methods, we use the results from 22 trials to prevent respiratory tract infections; we also make comparisons with a second example data set comprising fewer trials. Within summary data methods, confidence intervals for the overall treatment effect and for the between-trial variance may be derived from likelihood based methods or a parametric bootstrap as well as from Wald methods; the bootstrap intervals are preferred because they relax the assumptions required by the other two methods. When modelling individual patient data, a bias corrected bootstrap may be used to provide unbiased estimation and correctly located confidence intervals; this method is particularly valuable for the between-trial variance. The trial effects may be modelled as either fixed or random within individual data models, and we discuss the corresponding assumptions and implications. If random trial effects are used, the covariance between these and the random treatment effects should be included; the resulting model is equivalent to a bivariate approach to meta-analysis. Having implemented these techniques, the flexibility of multilevel modelling may be exploited in facilitating extensions to standard meta-analysis methods.     

Statistical synthesis of contextual knowledge to increase the effectiveness of theory-based behaviour change interventions

Tailored implementation strategies targeting health professionals' adoption of evidence-based recommendations are currently being developed. Research has focused on how to select an appropriate theoretical base, how to use that theoretical base to explore the local context, and how to translate theoretical constructs associated with the key factors found to influence innovation adoption into feasible and tailored implementation strategies. The reasons why an intervention is thought not to have worked are often cited as being: inappropriate choice of theoretical base; unsystematic development of the implementation strategies; and a poor evidence base to guide the process. One area of implementation research that is commonly overlooked is how to synthesize the data collected in a local context in order to identify what factors to target with the implementation strategies. This is suggested to be a critical process in the development of a theory-based intervention. The potential of multilevel modelling techniques to synthesize data collected at different hierarchical levels, for example, individual attitudes and team level variables, is discussed. Future research is needed to explore further the potential of multilevel modelling for synthesizing contextual data in implementation studies, as well as techniques for synthesizing qualitative and quantitative data.     

Multilevel modelling of the incidence of visceral leishmaniasis in Teresina, Brazil

Epidemics of visceral leishmaniasis (VL) in major Brazilian cities are new phenomena since 1980. As determinants of transmission in urban settings probably operate at different geographic scales, and information is not available for each scale, a multilevel approach was used to examine the effect of canine infection and environmental and socio-economic factors on the spatial variability of incidence rates of VL in the city of Teresina. Details on an outbreak of greater than 1200 cases of VL in Teresina during 1993-1996 were available at two hierarchical levels: census tracts (socio-economic characteristics, incidence rates of human VL) and districts, which encompass census tracts (prevalence of canine infection). Remotely sensed data obtained by satellite generated environmental information at both levels. Data from census tracts and districts were analysed simultaneously by multilevel modelling. Poor socio-economic conditions and increased vegetation were associated with a high incidence of human VL. Increasing prevalence of canine infection also predicted a high incidence of human VL, as did high prevalence of canine infection before and during the epidemic. Poor socio-economic conditions had an amplifying effect on the association between canine infection and the incidence of human VL. Focusing interventions on areas with characteristics identified by multilevel analysis could be a cost-effective strategy for controlling VL. Because risk factors for infectious diseases operate simultaneously at several levels and ecological data usually are available at different geographical scales, multilevel modelling is a valuable tool for epidemiological investigation of disease transmission.     

Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control

This paper reviews recent studies on the spatial epidemiology of human schistosomiasis in Africa. The integrated use of geographical information systems, remote sensing and geostatistics has provided new insights into the ecology and epidemiology of schistosomiasis at a variety of spatial scales. Because large-scale patterns of transmission are influenced by climatic conditions, an increasing number of studies have used remotely sensed environmental data to predict spatial distributions, most recently using Bayesian methods of inference. Such data-driven approaches allow for a more rational implementation of intervention strategies across the continent. It is suggested that improved incorporation of transmission dynamics into spatial models and assessment of uncertainties inherent in data and modelling approaches represent important future research directions.     

Randomized clinical trials with added rescue medication: some approaches to their analysis and interpretation

Patients in some randomized clinical trials may start additional non-randomized medication because of an exacerbation of symptoms or insufficient therapeutic effect. Typically this rescue medication reduces the observed treatment effect in intention-to-treat analysis. We discuss methods of analysis which take account of rescue medication in order to achieve a more meaningful comparison of the randomized treatments, focusing on trials with a repeated quantitative outcome. Ignoring all data after rescue is likely to be biased because rescued patients are a highly selected group. Instead we propose using methods based on ranks or multilevel models. The rank-based methods assume that rescued patients have especially bad underlying outcomes. The multilevel regression model relates a patient's outcome to allocated treatment and rescue status at each time, and requires correct modelling of all prognostic factors which predict starting rescue medication and of the covariance between outcomes measured at different times. We also describe sensitivity analyses over a range of possible models for the effect of rescue medication. We illustrate these methods in a trial in Parkinson's disease. It appears that adjustment for rescue medication will not radically alter the randomized treatment comparison unless rescue medication is substantially imbalanced between randomized groups and has a substantial effect on the outcome.