碘缺乏病区儿童发硒与甲状腺肿的关系

分别在碘缺乏病区及非病区对学龄儿童进行尿碘，甲状腺肿大率及发硒含量的调查。结果显示两地儿童尿碘水平接近，说明经补碘后病区儿童布匹营养状态得到一定改善；病区儿童甲状腺肿大率明显高于病区，而发硒含量明显低于非病区，二者之间有显著性差异，提示硒元素可能对磺缺乏病的发生起着一定的作用。     

碘缺乏病区儿童发硒与甲腺肿的关系

分别在碘缺乏病区及非病区对学龄儿童进行了尿碘、甲状腺肿大率及发硒含量的调查。结果显示两地儿童尿碘水平接近，说明经补碘后病区儿童碘营养状态得到一定改善；病区儿童甲状腺肿大率明显高于病区（Ｐ＜０．００１），而发硒含量明显低于非病区（Ｐ＜０．００１），二者之间有显著性差异，提示硒元素可能对碘缺乏病的发生起着一定的作用。     

黄河三角洲地区饮用碘型地下水人群致甲状腺肿调查分析

目的探讨黄河三角洲地区饮用碘型地下水人群与甲状腺肿的关系及现状。方法对6个县(区)具有病区指征、饮用地下水的8～12岁在校学生进行整群抽样的尿碘监测。结果Ⅰ～Ⅱ度甲状腺肿大率为23.52%,尿碘与甲状腺肿成正比关系,水碘与甲状腺肿近似“u”曲线关系。结论黄河三角洲地区饮用碘型地下水人群甲状腺肿大发病率高,且分布不均衡。     

2005年许昌市碘缺乏病监测结果分析

目的评价许昌市碘缺乏病防治效果，为制定和完善防治对策提供科学依据。方法按人口容量比例概率抽样方法（PPS），检查儿童甲状腺肿大率、碘盐和尿碘水平。结果2005年监测8—10岁儿童甲状腺肿大率为3．75％，比1995年下降了13．41个百分点；碘盐合格率为98．58％，儿童尿碘中位数为289μg/L。结论许昌市实施全民食盐加碘干预措施以后，儿童甲状腺肿大率逐年下降，人群碘营养水平得到提高，碘缺乏病防治成效显著。     

碘干预对燃煤氟中毒地区儿童甲状腺影响的研究

目的了解食盐加碘以来贵州燃煤氟中毒病区儿童碘缺乏病情指标的变化。方法采用现场流行病学抽样调查方法,对贵州氟中毒重病区织金县八步乡儿童进行尿碘水平及甲状腺肿大率调查,并与历史本底资料对比。结果被调查的氟病区儿童尿碘中位数为338.7μg/L,高于补碘前3倍,66.70%的儿童尿碘水平高于300μg/L,超过WHO推荐的100~200μg的日摄入量;7~14岁儿童青少年甲状腺肿大率7.1%,是补碘前(0.3%)的23.7倍。结论贵州有37个燃煤型氟中毒病区县,病区人口1900余万,历史上是碘缺乏病的轻病区,八步乡儿童青少年甲状腺肿大率上升的原因有待进一步研究。     

衢州市2004～2007年碘缺乏病监测结果分析

衢州市地处浙西山区，常住人口245．6万人；总面积8841．12平方公里。1984年衢州市曾对本地的碘缺乏病开展过调查，共检查187个乡镇2529个村198万人，甲状腺肿大率为14．4％；甲状腺肿患者12万，患病率为6．1％，其中7—14岁儿童甲状腺肿大率为24．0％；确认为碘缺乏病区。1985年环境水碘抽样测定，含碘量均值为3．75微克／升，均低于国家标准，表明环境严重缺碘。人群尿碘抽样测定低于50μg／L。因此环境严重缺碘是导致我市碘缺乏病流行的主要原因。1985年全面落实食盐加碘为主的综合防治措施，居民碘营养得到全面改善。1999年衢州市达到消除碘缺乏病阶段目标。     

滨州市碘缺乏病区8～10岁学生甲状腺肿大防治调查与研究

滨州市位于鲁北平原,黄河三角洲腹地,小清河、黄河东西贯穿,辖区6县2区,南部邹平为丘岭山区,北部县区为黄河淤积平原,地下水因地理位置不同变化较大。1978—1980年调查,碘缺乏病区水碘均值为4!14ug/L,人群尿碘水平为47!23ug/L,居民碘缺乏病的患病率为14!71%,7～14岁学生甲状腺肿大率为37!13%,1980年开始向病区供应碘盐,1984年碘缺乏病区的甲状腺肿大率得到基本控制。2001年为摸清我市碘缺乏病区8～10岁学生碘营养水平,观察碘盐、尿碘与甲状腺肿大之间的关系,对其进行了调查分析。结果报告如下:1对象与方法1.1调查对象根据地理位置,选择能代…     

许昌市2006年碘缺乏病监测报告

目的评价许昌市碘缺乏病防治效果,为制定和完善防治对策提供科学依据。方法按人口容量比例概率抽样方法(PPS),检查儿童甲状腺肿大率、盐碘和尿碘水平。结果2006年监测8～10岁儿童甲状腺肿大率为3.75%,碘盐合格率为97%,儿童尿碘中位数为261.1μg/L。结论实施全民食盐加碘干预措施以后,儿童甲状腺肿大率逐年下降,人群碘营养水平得到提高,碘缺乏病防治成效显著。     

山东省碘缺乏病2006-2010年动态预测研究

目的预测未来几年山东省碘缺乏病防治效果，提出相应防治措施。方法以1995-2004年山东省碘缺乏病监测数据为依据，用灰色动态预测模型对非碘盐率、居民合格碘盐食用率、甲状腺肿大率及尿碘值等指标进行了动态预测。结果居民合格碘盐食用率理论值呈逐年上升趋势，非碘盐率、儿童甲状腺肿大率及尿碘理论值呈逐年下降趋势。2010年山东省非碘盐率预测值为2．0％；合格碘盐食用率为98．0％；儿童甲状腺肿大率为5．4％；尿碘值为171．2μg/L。结论预测结果表明，只要长期坚持食用合格碘盐，碘缺乏病防治效果将持续在一个比较稳定和理想的范围。     

碘缺乏病常用测定指标的初步筛选

通过对碘缺乏病区及高碘区的非缺碘地区７～１４岁学生和居民甲状腺肿病情及Ｔ３、Ｔ４、ＴＳＨ、甲状腺摄Ｉ１３１率、尿碘等相互关系研究，观察到甲状腺摄Ｉ１３１率与尿碘呈密切负相关关系；Ｔ３、ＴＳＨ与尿碘及甲状腺摄Ｉ１３１率无明显的相关关系。在非缺碘区，Ｔ４与甲状腺摄Ｉ１３１率成负相关关系，与尿碘呈正相关关系，但在碘缺乏区未观察到类似关系。在碘缺乏区，甲状腺摄Ｉ１３１率与７～１４岁甲状腺肿率、甲状腺肿患病率、甲状腺肿积分及居民甲状腺肿患病率均呈密切正相关关系，并可用它们间的关系建立回归方程，根据甲状腺摄Ｉ１３１率估算病情，在非碘缺乏区，不存在这种关系，而相反地呈现负相关趋势。尿碘与甲状腺肿大率、患病率之间的关系表明，在碘缺乏病区，呈负相关关系；而在非碘缺乏病区，刚呈现正相关关系。其中有意义的是在碘缺乏病区，可以根据这些关系建立回归方程，并可由尿碘结果估计病情。     

Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

ABSTRACT: BACKGROUND: In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. METHODS: We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighbouring suburbs. RESULTS: This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30\% lower with a rate ratio of 0.70 (95\% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20\% and 40\%. DISCUSSION: This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic.     

How does Poisson kriging compare to the popular BYM model for mapping disease risks?

Background

Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical units, avoiding the limitations of conditional auto-regressive (CAR) models commonly used in Bayesian algorithms while allowing for the creation of isopleth risk maps. Both approaches, however, have never been compared in simulation studies, and there is a need to better understand their merits in terms of accuracy and precision of disease risk estimates.

Results

Besag, York and Mollie's (BYM) model and Poisson kriging (point and area-to-area implementations) were applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1) state of Indiana that consists of 92 counties of fairly similar size and shape, and 2) four states in the Western US (Arizona, California, Nevada and Utah) forming a set of 118 counties that are vastly different geographical units. The spatial support (i.e. point versus area) has a much smaller impact on the results than the statistical methodology (i.e. geostatistical versus Bayesian models). Differences between methods are particularly pronounced in the Western US dataset: BYM model yields smoother risk surface and prediction variance that changes mainly as a function of the predicted risk, while the Poisson kriging variance increases in large sparsely populated counties. Simulation studies showed that the geostatistical approach yields smaller prediction errors, more precise and accurate probability intervals, and allows a better discrimination between counties with high and low mortality risks. The benefit of area-to-area Poisson kriging increases as the county geography becomes more heterogeneous and when data beyond the adjacent counties are used in the estimation. The trade-off cost for the easier implementation of point Poisson kriging is slightly larger kriging variances, which reduces the precision of the model of uncertainty.

Conclusion

Bayesian spatial models are increasingly used by public health officials to map mortality risk from observed rates, a preliminary step towards the identification of areas of excess. More attention should however be paid to the spatial and distributional assumptions underlying the popular BYM model. Poisson kriging offers more flexibility in modeling the spatial structure of the risk and generates less smoothing, reducing the likelihood of missing areas of high risk.     

A Bayesian approach to study the space time variation of leprosy in an endemic area of Tamil Nadu, South India

Background

In leprosy endemic areas, patients are usually spatially clustered and not randomly distributed. Classical statistical techniques fail to address the problem of spatial clustering in the regression model. Bayesian method is one which allows itself to incorporate spatial dependence in the model. However little is explored in the field of leprosy. The Bayesian approach may improve our understanding about the variation of the disease prevalence of leprosy over space and time.

Methods

Data from an endemic area of leprosy, covering 148 panchayats from two taluks in South India for four time points between January 1991 and March 2003 was used. Four Bayesian models, namely, space-cohort and space-period models with and without interactions were compared using the Deviance Information Criterion. Cohort effect, period effect over four time points and spatial effect (smoothed) were obtained using WinBUGS. The spatial or panchayat effect thus estimated was compared with the raw standardized morbidity (leprosy prevalence) rate (SMR) using a choropleth map. The possible factors that might have influenced the variations of prevalence of leprosy were explored.

Results

Bayesian models with the interaction term were found to be the best fitted model. Leprosy prevalence was higher than average in the older cohorts. The last two cohorts 1987–1996 and 1992–2001 showed a notable decline in leprosy prevalence. Period effect over 4 time points varied from a high of 3.2% to a low of 1.8%. Spatial effect varied between 0.59 and 2. Twenty-six panchayats showed significantly higher prevalence of leprosy than the average when Bayesian method was used and it was 40 panchayats with the raw SMR.

Conclusion

Reduction of prevalence of leprosy was 92% for persons born after 1996, which could be attributed to various intervention and treatment programmes like vaccine trial and MDT. The estimated period effects showed a gradual decline in the risk of leprosy which could be due to better nutrition, hygiene and increased awareness about the disease. Comparison of the maps of the relative risk using the Bayesian smoothing and the raw SMR showed the variation of the geographical distribution of the leprosy prevalence in the study area. Panchayat or spatial effects using Bayesian showed clustersing of leprosy cases towards the northeastern end of the study area which was overcrowded and population belonging to poor economic status.     

A Bayesian hierarchical model for the estimation of two incomplete surveillance data sets

A model-based approach to analyze two incomplete disease surveillance datasets is described. Such data typically consist of case counts, each originating from a specific geographical area. A Bayesian hierarchical model is proposed for estimating the total number of cases with disease while simultaneously adjusting for spatial variation. This approach explicitly accounts for model uncertainty and can make use of covariates.The method is applied to two surveillance datasets maintained by the Centers for Disease Control and Prevention on Rocky Mountain spotted fever (RMSF). An inference is drawn using Markov Chain Monte Carlo simulation techniques in a fully Bayesian framework. The central feature of the model is the ability to calculate and estimate the total number of cases and disease incidence for geographical regions where RMSF is endemic.The information generated by this model could significantly reduce the public health impact of RMSF and other vector-borne zoonoses, as well as other infectious or chronic diseases, by improving knowledge of the spatial distribution of disease risk of public health officials and medical practitioners. More accurate information on populations at high risk would focus attention and resources on specific areas, thereby reducing the morbidity and mortality caused by some of the preventable and treatable diseases.     

Zoonotic cutaneous leishmaniasis in central Tunisia: spatio temporal dynamics

BACKGROUND: Zoonotic cutaneous leishmaniasis (ZCL) is endemic in many rural areas of the Southern and Eastern Mediterranean region where different transmission patterns of the disease have been described. This study was carried out in a region located in Central Tunisia and aimed to investigate the spatio-temporal dynamics of the disease from 1999 to 2004. METHODS: Incident ZCL cases were defined by clinical diagnosis, confirmed by a positive skin test and/or parasitological examination. Annual ZCL rates were calculated for 94 regional sectors that comprise the study region of Sidi-Bouzid. Spatial and temporal homogeneity were initially investigated by chi-squared tests. Next, spatial scan statistics were used to identify spatial, temporal and spatio-temporal clusters that display abnormally high incidence rates. A hierarchical Bayesian Poisson regression model with spatial effects was fitted to signify explanatory socio-geographic factors related to spatial rate variability. Temporal ZCL dynamics for the 94 sectors were described via a linear mixed model. RESULTS: A total of 15 897 ZCL cases were reported in the 6-year study period, with an annual incidence rate of 669.7/100 000. An outbreak of the disease was detected in 2004 (1114/100 000). Spatial clustering is evident for the whole time period. The most likely cluster according to the spatial scan statistic, contains seven sectors with abnormally high incidence rates and approximately 5% of the total population. ZCL rates per sector are mostly related to the urban/rural index; sectoral population density and the number of inhabitants per household do not appear to contribute much to the explanation of rate variability. The dynamics of the disease within the study period are satisfactorily described by quadratic curves that differ for urban and rural areas. CONCLUSIONS: ZCL rates vary across space and time; rural/urban areas and environmental factors may explain part of this variation. In the study region, the Sidi Saad dam-constructed in the early eighties and identified by previous studies as a major reason for the first outbreak of the disease-seems to be still related to increased ZCL rates. The most likely spatial cluster of high incidence rates contains regions located close to the dam. Our findings of increased incidences in urban areas support the hypothesis of increased incidences in peri-urban environments due to changes in sandfly/rodent living habits over recent years.     

Modelling the geographical distribution of co-infection risk from single-disease surveys

Background: The need to deliver interventions targeting multiple diseases in a cost‐effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co‐infection is particularly high. Co‐infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Methods: Bayesian geostatistical shared component models (allowing for covariates, disease‐specific and shared spatial and non‐spatial random effects) are proposed to model the geographical distribution and burden of co‐infection risk from single‐disease surveys. The ability of the models to capture co‐infection risk is assessed on simulated data sets based on multinomial distributions assuming light‐ and heavy‐dependent diseases, and a real data set of Schistosoma mansoni –hookworm co‐infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single‐disease surveys. The estimated results of co‐infection risk, together with independent and multinomial model results, were compared via different validation techniques. Results: The results showed that shared component models result in more accurate estimates of co‐infection risk than models assuming independence in settings of heavy‐dependent diseases. The shared spatial random effects are similar to the spatial co‐infection random effects of the multinomial model for heavy‐dependent data. Conclusions: In the absence of true co‐infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co‐infection risk from single‐disease survey data, especially in settings of heavy‐dependent diseases. Copyright © 2011 John Wiley & Sons, Ltd.     

全国血吸虫病流行状况的地理信息系统空间分析

目的：了解全国血吸虫病流行情况和空间分布。方法：收集全国两次血吸虫病抽样调查资料,建立相关数据库,在Ａｒｃ Ｖｉｅｗ３．０ａ软件的支持下,与建立的数据库相联,并进行空间分析。对抽样调查的人群和耕牛感染率作相关分析。结果：目前我国血吸虫病分布的高危地区主要分布于沿长江的江湖洲滩,可将全国血吸虫病病区划分为５个主要空间分布区域。发现耕牛血吸虫病的空间分布范围及粪检阳性率较人群血吸虫病的严重。两次抽样调     

A comparison of conditional autoregressive models used in Bayesian disease mapping

Disease mapping is the area of epidemiology that estimates the spatial pattern in disease risk over an extended geographical region, so that areas with elevated risk levels can be identified. Bayesian hierarchical models are typically used in this context, which represent the risk surface using a combination of available covariate data and a set of spatial random effects. These random effects are included to model any overdispersion or spatial correlation in the disease data, that has not been accounted for by the available covariate information. The random effects are typically modelled by a conditional autoregressive (CAR) prior distribution, and a number of alternative specifications have been proposed. This paper critiques four of the most common models within the CAR class, and assesses their appropriateness via a simulation study. The four models are then applied to a new study mapping cancer incidence in Greater Glasgow, Scotland, between 2001 and 2005.     

Hierarchical Bayesian spatial modelling of small-area rates of non-rare disease

We present Bayesian hierarchical spatial models for the analysis of the geographical distribution of a non-rare disease or event. The work is motivated by the need for ascertaining regional variations in health services outcomes and resource use and for assessing the potential sources of these variations. The models discussed herein readily accommodate random spatial effects and covariate effects. We discuss Bayesian inferential framework and implementation of a hybrid Markov chain Monte Carlo method for full Bayesian model inference. The methods are illustrated through an analysis of regional variation in chronic lung disease (CLD) rates among neonatal intensive care unit (NICU) patients across Canada. Specifically, we first present a random effects binomial model for spatially correlated CLD rates, with random spatial effects accounting for latent or covariate effects. These random spatial effects depict regional or spatial variation in chronic lung disease occurrence. We then extend this model to include covariates. With this extension, we assess residual spatial effects and the extent to which risk factors such as illness severity at NICU admission, low birth weight, and very low birth weight influence the CLD rate variation.     

Application of Poisson kriging to the mapping of cholera and dysentery incidence in an endemic area of Bangladesh

Background  

Disease maps can serve to display incidence rates geographically, to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Poisson kriging was recently introduced to filter the noise attached to rates recorded over sparsely populated administrative units. Its benefit over simple population-weighted averages and empirical Bayesian smoothers was demonstrated by simulation studies using county-level cancer mortality rates. This paper presents the first application of Poisson kriging to the spatial interpolation of local disease rates, resulting in continuous maps of disease rate estimates and the associated prediction variance. The methodology is illustrated using cholera and dysentery data collected in a cholera endemic area (Matlab) of Bangladesh.