对我国目前麻风防治策略的思考

1949-2008年我国累计登记麻风患者48万余例,主要分布在北纬38°以南的东南沿海和长江流域省份.新中国成立后,经过积极防治,至20世纪80年代初,以氨苯砜单疗治愈的患者达31万余例;1986年在全国普遍推广WHO联合化疗方案,至2008年底全国累计接受联合化疗的麻风患者近8万例.     

1950-2009年理县麻风病流行病学分析

目的 分析理县1950 -1989年麻风病流行病学特征.方法 采用描述流行病学方法对理县1950 -2009年麻风疫情资料进行统计分析.结果 理县1950 -1989年累计报告麻风病患者78例,年均发病率为5.49/10万.最高发现年份是1959年51.88/10万(19例),最低年份是1989年为2.39/10万(1例),1990 - 2009年无新发病例.结论 理县已达到基本消灭麻风病标准,早期发现患者,及时联合化疗,开展各种形式的麻风知识宣传教育,可以有效地控制麻风的流行.     

广东顺德1954~2013年麻风复发病例分析

目的研究广东顺德1954²103年用氨苯砜单疗或联合化疗后麻风病患者的复发情况。方法对19542103年用氨苯砜单疗或联合化疗后麻风病患者的复发情况。方法对1954²013年顺德开展麻风病防治工作较完整的麻风个案资料进行分析。结果 19542013年顺德开展麻风病防治工作较完整的麻风个案资料进行分析。结果 1954²013年间顺德累计复发患者64例,累计复发率为4.77%。平均治愈-复发间隔为(131.52±12.64)个月。多菌型的累计复发率高于少菌型的累计复发率(2=76.771,p<0.000 1)。男女的复发率无统计学意义(2=2.349,p=0.125)。利福平600 mg+氧氟沙星400 mg联合化疗治愈患者的复发率为27.27%,高于其他联合化疗方案和氨苯砜单疗的复发率(2=7.974,p=0.034)。结论采用利福平和氧氟沙星联合化疗方案治愈的患者,其复发率远高于其他化疗方案,不建议继续使用和推广。麻风复发有长期危险性,长期监测随访,有助于发现复发患者。     

中国2009年麻风病流行病学特征分析

目的 了解2009年全国麻风病流行病学特征.方法 收集2009年度全国麻风病疫情监测资料,采用描述性分析.结果 2009年度共发现新麻风病例1597例,发现率为0.120/10万,其中儿童占2.4%、多菌型占84.3%、2级畸残占22.8%.2009年度共发现复发病例148例,其中69例为联合化疗后复发.至2009年底全国尚有现症病例6603例,患病率为0.049/万,其中3332例尚在接受联合化疗.结论 全国麻风病总体处于低流行水平,但分布不均衡,重点流行地区为西南省份.     

短程联合化疗对麻风病流行的影响

目的 研究短程联合化疗对麻风病流行趋势的影响。方法 对贵州省毕节地区、黔西南两个麻风病高流行地区,实施短程联合化疗(MDT)方案治疗病人。结果 1986～2000年,两地区共发现2 338例麻风新病人,其中儿童病人122例,占新发现病人数的5.2％;畸残病人623例,平均畸残率为26.6％。两地区累计短程MDT治疗麻风病人4 051例,其中累计死亡及其他减少549例,累计完成短程MDT疗程3 131例,尚有现症病人371例。毕节地区、黔西南州1985年的麻风患病率分别为0.36‰、0.46‰,2000年麻风患病率下降到0.02‰、0.06‰,分别下降94.4％和87.0％;新发现病人数逐年减少,发病率由1986年的3.6/10万、4.0/10万下降到2000年0.8/10万和1.4/10万下降幅度分别为77.8％和65％。两地区累计MDT治愈后复发病人3例,复发率为0.09％。结论 短程MDT可迅速杀死麻风杆菌、降低病人的传染性、缩短疗程、减少病人复发,所以对两地区麻风患病率和发病率的下降起了主要作用,有效地控制了麻风病的流行。     

有多少“艾”可以重来

新闻前线： 2007年11月29日公布的《中国艾滋病防治联合评估报告（2007）》指出，截至10月底，全国累计报告22.35万余例艾滋病病毒感染者和艾滋病病人，其中病人数为62838例，估计有5万例新发艾滋病病毒感染者，每天新增137人，死亡报告2万余例，日均死亡55人。     

云南省丘北县麻风持续传播影响因素的研究

目的 探讨云南省丘北县多种药物联合化疗实施25年后麻风仍持续传播的影响因素.方法 分别采用ELISA、PCR检测高流行区患者与家庭内接触者、普通人群血清中麻风菌特异性PGL-1抗体以及鼻分泌物中的麻风菌,采用PCR方法检测疫村环境水中的麻风菌.采用数目可变的串联重复序列基因分型方法探讨麻风菌传播途径及传播链等.结果 在2001年以前从麻风患者家庭成员中检测到的比例低,而延迟期>2年患者的检出比例高.2001年后防治工作加强使这两个指标改进,但麻风发现率仍一直维持在4/10万至5/10万.疫村人群PGL-1抗体阳性率为20%～30%,主要感染人群为青少年;不仅从患者、家内接触者的鼻分泌物与环境水中检出麻风菌,且与未经治疗患者的皮肤组织和鼻分泌物中麻风菌基因型一致.菌株分型证实该县北部地区不仅有多个高发家庭聚集,而且家庭内患者的菌株基因型一致.在北部地区基因型匹配菌株的比例高于南部地区.结论 丘北县麻风在家庭内和疫村中的传播严重,疫村环境水中存在麻风菌等是影响该病持续传播的重要因素.     

1260例多菌型麻风短程联合化疗后的监测

复发与控制复发是麻风防治工作的重要环节 ,是评价治疗方案的主要指标〔1〕。造成麻风复发的主要原因 :一是部分患者产生耐药菌 ;二是麻风持久菌的长期存在。 1986年 ,我国开始在全国推行联合化疗 (ＭＤＴ)方案。贵州省黔西南州作为麻风高流行区于同年实施短程ＭＤＴ治疗麻风 ,累计治疗 12 60例ＭＢ麻风病人 ,其中包括 62 5例新发现病人和 63 5例经过ＤＤＳ(氨苯砜 )或ＤＤＳ ＲＦＰ(利福平 )或ＤＤＳ治愈复发的活动性病人 ,疗效评价按麻风防治手册标准 ,并对完成短程ＭＤＴ疗程停药后的病人进行 1～ 9年随访监测。现报道如下。结果　 12 6…     

龙岩市实施麻风联合化疗14年成本-效益研究

[目的]科学评价龙岩市麻风联合化疗的成果。[方法]应用成本-效益分析法研究龙岩市实施麻风联合化疗14年的成本效益。[结果]实施联合化疗后，缩短麻风治愈疗程35.16个月，节省住院费用，未发生复发病例，减少直接经济损失1757.48万元；减少发病人数67例，减少畸残18.6例，减法复发1.9例，减少各项间接经济损失2469.56万元。总效益达4227.04万元，14年的总成本投入457.11万元，获得直接净效益1300.37万元。成本与直接效益比值1：3.8;间接净效益2012.45万元，成本与间接效益比值1:5.4；总净效益为3313.82万元，成本与总效益比值1：9.2。[结论]实施麻风联合化疗每投入1元成本可获得3.8元的效益，并为国家创造5.4元财富。     

麻风病人联合化疗实施八年的管理方法

麻风病人联合化疗实施八年的管理方法云南省大理州卫生防疫站（６７１０００）王超英彭金虎戴聪对麻风病人进行联合化疗是切断麻风传染源，控制其流行的主要措施，自１９８８年３月全面采用世界卫生组织推荐的联合化疗方案以来，全州累计化疗人数为１０６８人，联疗率为１...     

江苏省常州市城区7～12岁儿童肥胖与高血压的流行病学调查

肥胖症已被确认为20世纪主要的健康问题之一;与肥胖症相关的代谢紊乱、心血管疾病的早期改变可能从儿童时期开始.近年来儿童期肥胖症在我国大中城市呈逐年增长的趋势,为掌握常州市城区儿童青少年超重肥胖的发病情况,及其与高血压的关系;对常州市区7～12岁小学生血压及BMI监测资料进行了分析.     

急性脑出血患者入院时血压与住院期间临床结局的关系

脑卒中是导致居民死亡和残疾的主要原因[1].在我国脑卒中分别占男性和女性总死亡的21.6%和20.8%[2].与西方人群相比,中国人群出血性脑卒中的发病率更高[2].出血性脑卒中具有病死率高、致残率高及复发率高的特点[4].高血压是脑卒中发病最重要的危险因素.一些研究结果表明血压的升高与脑出血不良结局呈正相关[5,6];另一些研究则显示血压与脑出血不良结局之间呈"U"形关联[7,8]或者负相关[9].目前,国内还少有急性期患者血压与脑出血临床结局关系的大样本研究报道.本研究回顾性分析了1604例急性脑出血患者的临床资料,探讨患者入院时SBP和DBP与住院期间死亡和残疾危险性之间的关系,为急性脑出血患者的血压管理提供参考依据.     

多阶段抽样调查资料的加权估计法

多阶段抽样技术广泛应用于流行病学现况调查中,但针对其所得资料的统计分析方法往往选择不当.文中介绍一种用于多阶段抽样调查资料的统计分析方法--加权估计法,以推广针对此类资料的恰当的分析方法.在介绍加权估计法基本原理的基础上通过两个二阶段分层整群抽样的实际调查资料实现这种算法.加权估计法可以校正由多阶段抽样导致的三种效应:群效应、分层效应、不等概率性,给出无偏点估计和比较客观的误差估计,并作出正确的统计推断.     

整群抽样调查数据分析中应正确计算抽样误差

为了澄清整群抽样调查数据分析中正确计算抽样误差的必要性，以在某市15岁及以上人群中开展的一次两阶段整群抽样调查为例，分别采用适用于单纯随机抽样数据的方法和考虑了复杂抽样设计的方法对数据进行分析。结果显示，忽略对复杂抽样设计的考虑，不恰当的采用适用于单纯随机抽样数据的方法进行数据分析，不仅有可能大大低估样本统计量的抽样误差，在进行假设检验时，甚至会得到错误的结果，故正确分析和报告整群抽样调查数据的抽样误差是非常必要的。     

Adjusting for confounding by neighborhood using complex survey data

Recently, we examined methods of adjusting for confounding by neighborhood of an individual exposure effect on a binary outcome, using complex survey data; the methods were found to fail when the neighborhood sample sizes are small and the selection bias is strongly informative. More recently, other authors have adapted an older method from the genetics literature for application to complex survey data; their adaptation achieves a consistent estimator under a broad range of circumstances. The method is based on weighted pseudolikelihoods, in which the contribution from each neighborhood involves all pairs of cases and controls in the neighborhood. The pairs are treated as if they were independent, a pairwise pseudo-conditional likelihood is thus derived, and then the corresponding score equation is weighted with inverse-probabilities of sampling each case-control pair. We have greatly simplified the implementation by translating the pairwise pseudo-conditional likelihood into an equivalent ordinary weighted log-likelihood formulation. We show how to program the method using standard software for ordinary logistic regression with complex survey data (e.g. SAS PROC SURVEYLOGISTIC). We also show that the methodology applies to a broader set of sampling scenarios than the ones considered by the previous authors. We demonstrate the validity of our simplified implementation by applying it to a simulation for which previous methods failed; the new method performs beautifully. We also apply the new method to an analysis of 2009 National Health Interview Survey (NHIS) public-use data, to estimate the effect of education on health insurance coverage, adjusting for confounding by neighborhood.     

Sampling plan for the National Survey Sexual Behavior and Perceptions of the Brazilian Population concerning HIV/AIDS, 2005

OBJECTIVE: To describe the sampling plan and estimation methods used to collect and analyze data in the survey Sexual Behavior and Perceptions of the Brazilian Population concerning HIV/AIDS in 2005. METHODS: The study presents the decisions that were made concerning population definition, strata of interest to the survey and to the sampling plan, main procedures for data analysis and sample performance in the field. SAMPLING RESULTS: A probabilistic plan was designed with 5,040 sampling units obtained from the Brazilian population, with individuals aged between 16 and 65 years living in large Brazilian urban centers. It is a complex sampling plan distributed over eight main estimation domains, designed in multiple stages. A man or a woman was interviewed in the last stage. Each interviewed unit and each household have specific probability of belonging to the sample.     

Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents

Originally, 2‐stage group testing was developed for efficiently screening individuals for a disease. In response to the HIV/AIDS epidemic, 1‐stage group testing was adopted for estimating prevalences of a single or multiple traits from testing groups of size q, so individuals were not tested. This paper extends the methodology of 1‐stage group testing to surveys with sample weighted complex multistage‐cluster designs. Sample weighted‐generalized estimating equations are used to estimate the prevalences of categorical traits while accounting for the error rates inherent in the tests. Two difficulties arise when using group testing in complex samples: (1) How does one weight the results of the test on each group as the sample weights will differ among observations in the same group. Furthermore, if the sample weights are related to positivity of the diagnostic test, then group‐level weighting is needed to reduce bias in the prevalence estimation; (2) How does one form groups that will allow accurate estimation of the standard errors of prevalence estimates under multistage‐cluster sampling allowing for intracluster correlation of the test results. We study 5 different grouping methods to address the weighting and cluster sampling aspects of complex designed samples. Finite sample properties of the estimators of prevalences, variances, and confidence interval coverage for these grouping methods are studied using simulations. National Health and Nutrition Examination Survey data are used to illustrate the methods.     

Applying sample survey methods to clinical trials data

This paper outlines the utility of statistical methods for sample surveys in analysing clinical trials data. Sample survey statisticians face a variety of complex data analysis issues deriving from the use of multi-stage probability sampling from finite populations. One such issue is that of clustering of observations at the various stages of sampling. Survey data analysis approaches developed to accommodate clustering in the sample design have more general application to clinical studies in which repeated measures structures are encountered. Situations where these methods are of interest include multi-visit studies where responses are observed at two or more time points for each patient, multi-period cross-over studies, and epidemiological studies for repeated occurrences of adverse events or illnesses. We describe statistical procedures for fitting multiple regression models to sample survey data that are more effective for repeated measures studies with complicated data structures than the more traditional approaches of multivariate repeated measures analysis. In this setting, one can specify a primary sampling unit within which repeated measures have intraclass correlation. This intraclass correlation is taken into account by sample survey regression methods through robust estimates of the standard errors of the regression coefficients. Regression estimates are obtained from model fitting estimation equations which ignore the correlation structure of the data (that is, computing procedures which assume that all observational units are independent or are from simple random samples). The analytic approach is straightforward to apply with logistic models for dichotomous data, proportional odds models for ordinal data, and linear models for continuously scaled data, and results are interpretable in terms of population average parameters. Through the features summarized here, the sample survey regression methods have many similarities to the broader family of methods based on generalized estimating equations (GEE). Sample survey methods for the analysis of time-to-event data have more recently been developed and implemented in the context of finite probability sampling. Given the importance of survival endpoints in late phase studies for drug development, these methods have clear utility in the area of clinical trials data analysis. A brief overview of methods for sample survey data analysis is first provided, followed by motivation for applying these methods to clinical trials data. Examples drawn from three clinical studies are provided to illustrate survey methods for logistic regression, proportional odds regression and proportional hazards regression. Potential problems with the proposed methods and ways of addressing them are discussed.     

Causal analysis of case-control data

In a series of papers, Robins and colleagues describe inverse probability of treatment weighted (IPTW) estimation in marginal structural models (MSMs), a method of causal analysis of longitudinal data based on counterfactual principles. This family of statistical techniques is similar in concept to weighting of survey data, except that the weights are estimated using study data rather than defined so as to reflect sampling design and post-stratification to an external population. Several decades ago Miettinen described an elementary method of causal analysis of case-control data based on indirect standardization. In this paper we extend the Miettinen approach using ideas closely related to IPTW estimation in MSMs. The technique is illustrated using data from a case-control study of oral contraceptives and myocardial infarction.     

Estimation and sample design in prevalence surveys of dementia.

Population prevalence rates of dementia using stratified sampling have previously been estimated using two methods: standard weighted estimates and a logistic model-based approach. An earlier study described this application of the model-based approach and reported a small computer simulation comparing the performance of this estimator to the standard weighted estimator. In this article we use large-scale computer simulations based on data from the recently completed Kame survey of prevalent dementia in the Japanese-American residents of King County, Washington, to describe the performance of these estimators. We found that the standard weighted estimator was unbiased. This estimator performed well for a sample design with proportional allocation, but performed poorly for a sample design that included large strata that were lightly sampled. The logistic model-based estimator performed consistently well for all sample designs considered in terms of the extent of variability in estimation, although some modest bias was observed.