沈阳地区老年代谢综合征患病率流行病学调查

2007年沈阳地区65岁及以上老年人2730例,按国际糖尿病联盟（IDF）及中华糖尿病学会（CDS）建议诊断的代谢综合征及其各组分的患病率计为：65岁以上人群代谢综合征、高血压、高尿酸血症、糖尿病前期（包括IFG和IGT）、糖尿病（DM）、超重和（或）肥胖以及高三酰甘油血症患病粗率分别为23.19%,28.13%,29.12%,26.81%,10.82%,21.14%和14.14%,按照2000年第五次全国人口普查沈阳地区人口构成比进行标化后,上述各组分标化患病率分别为10.3%,11.1%,13.2%,12.3%,8.1%,10.8%和8.1%。     

Childhood malaria in the Gambia: a case-study in model-based geostatistics

Summary. The paper develops a spatial generalized linear mixed model to describe the variation in the prevalence of malaria among a sample of village resident children in the Gambia. The response from each child is a binary indicator of the presence of malarial parasites in a blood sample. The model includes terms for the effects of child level covariates (age and bed net use), village level covariates (inclusion or exclusion from the primary health care system and greenness of surrounding vegetation as derived from satellite information) and separate components for residual spatial and non-spatial extrabinomial variation. The results confirm and quantify the progressive increase in prevalence with age, and the protective effects of bed nets. They also show that the extrabinomial variation is spatially structured, suggesting an environmental effect rather than variation in familial susceptibility. Neither inclusion in the primary health care system nor the greenness of the surrounding vegetation appeared to affect the prevalence of malaria. The method of inference was Bayesian using vague priors and a Markov chain Monte Carlo implementation.     

Behavioral epidemiology and social area analysis: The study of blackout looting

This article describes the use of social area analytic techniques in the study of the looting which occurred during the Con Edison power failure of July 13 and 14, 1977. Detailed procedures are outlined of the systematic collection and analysis of data which were developed from available data. Analyses contrasting competing explanatory theoretical models for blackout looting are presented. The central dependent variable is ratio of looted to retail stores. Both the numerator and denominator are aggregated to the census tract level.     

A Modified Knox Test of Space-Time Clustering

Tests of space-time clustering such as the Knox test are used by epidemiologists in the preliminary analysis of datasets where an infectious aetiology is suspected. The Knox test statistic is the number of cases close in both space and time to another case. The test statistic proposed here is the excess number of such cases over that expected under H₀ of no infection. It is argued that this modified test is more powerful than the Knox test, because the test statistic is not heavily tied as is the Knox test statistic. The use of the test is illustrated with examples.     

Sample size implications when biases are modelled rather than ignored

Summary.  Realistic statistical modelling of observational data often suggests a statistical model which is not fully identified, owing to potential biases that are not under the control of study investigators. Bayesian inference can be implemented with such a model, ideally with the most precise prior knowledge that can be ascertained. However, as a consequence of the non-identifiability, inference cannot be made arbitrarily accurate by choosing the sample size to be sufficiently large. In turn, this has consequences for sample size determination. The paper presents a sample size criterion that is based on a quantification of how much Bayesian learning can arise in a given non-identified model. A global perspective is adopted, whereby choosing larger sample sizes for some studies necessarily implies that some other potentially worthwhile studies cannot be undertaken. This suggests that smaller sample sizes should be selected with non-identified models, as larger sample sizes constitute a squandering of resources in making estimator variances very small compared with their biases. Particularly, consider two investigators planning the same study, one of whom admits to the potential biases at hand and consequently uses a non-identified model, whereas the other pretends that there are no biases, leading to an identified but less realistic model. It is seen that the former investigator always selects a smaller sample size than the latter, with the difference being quite marked in some illustrative cases.     

Quantifying Cancer Risk from Radiation

Complex statistical models fitted to data from studies of atomic bomb survivors are used to estimate the human health effects of ionizing radiation exposures. We describe and illustrate an approach to estimate population risks from ionizing radiation exposure that relaxes many assumptions about radiation‐related mortality. The approach draws on developments in methods for causal inference. The results offer a different way to quantify radiation's effects and show that conventional estimates of the population burden of excess cancer at high radiation doses are driven strongly by projecting outside the range of current data. Summary results obtained using the proposed approach are similar in magnitude to those obtained using conventional methods, although estimates of radiation‐related excess cancers differ for many age, sex, and dose groups. At low doses relevant to typical exposures, the strength of evidence in data is surprisingly weak. Statements regarding human health effects at low doses rely strongly on the use of modeling assumptions.     

Semiparametric analysis of case series data

Summary.  The case series model for estimating the association between an age-dependent exposure and an outcome event requires information only on cases and implicitly adjusts for all age-independent multiplicative confounders, while allowing for an age-dependent base-line incidence. In the paper the model is presented in greater generality than hitherto, including more general discussion of its derivation, underlying assumptions, applicability, limitations and efficiency. A semiparametric version of the model is developed, in which the age-specific relative incidence is left unspecified. Modelling covariate effects and testing assumptions are discussed. The small sample performance of this model is studied in simulations. The methods are illustrated with several examples from epidemiology.     

Bayesian methods for the cross-design synthesis of epidemiological and toxicological evidence

Summary.  Systematic review and synthesis (meta-analysis) methods are now increasingly used in many areas of health care research. We investigate the potential usefulness of these methods for combining human and animal data in human health risk assessment of exposure to environmental chemicals. Currently, risk assessments are often based on narrative review and expert judgment, but systematic review and formal synthesis methods offer a more transparent and rigorous approach. The method is illustrated by using the example of trihalomethane exposure and its possible association with low birth weight. A systematic literature review identified 13 relevant studies (five epidemiological and eight toxicological). Study-specific dose–response slope estimates were obtained for each of the studies and synthesized by using Bayesian meta-analysis models. Sensitivity analyses of the results obtained to the assumptions made suggest that some assumptions are critical. It is concluded that systematic review methods should be used in the synthesis of evidence for environmental standard setting, that meta-analysis will often be a valuable approach in these contexts and that sensitivity analyses are an important component of the approach whether or not formal synthesis methods (such as systematic review and meta-analysis) are used.     

A model of toxoplasmosis incidence in the UK: evidence synthesis and consistency of evidence

Summary.  We present a Bayesian evidence synthesis model combining data on seroprevalence, seroconversion and tests of recent infection, to produce estimates of current incidence of toxoplasmosis in the UK. The motivation for the study was the need for an estimate of current average incidence in the UK, with a realistic assessment of its uncertainty, to inform a decision model for a national screening programme to prevent congenital toxoplasmosis. The model has a hierarchical structure over geographic region, a random-walk model for temporal effects and a fixed age effect, with one or more types of data informing the regional estimates of incidence. Inference is obtained by using Markov chain Monte Carlo simulations. A key issue in the synthesis of evidence from multiple sources is model selection and the consistency of different types of evidence. Alternative models of incidence are compared by using the deviance information criterion, and we find that temporal effects are region specific. We assess the consistency of the various forms of evidence by using cross-validation where practical, and posterior and mixed prediction otherwise, and we discuss how these measures can be used to assess different aspects of consistency in a complex evidence structure. We discuss the contribution of the various forms of evidence to estimated current average incidence.     

Methods for eliciting aetiological clues from geographically clustered cases of disease, with application to leukaemia–lymphoma data

The nearest neighbour analysis method has been developed to determine whether a disease case may be regarded as being unusually close to other neighbouring cases of the same disease. Using this method, each disease case is classified as spatially 'clustered' or 'non-clustered'. The method is also used to provide a test for global clustering. 'Clusters' are constructed by amalgamating geographically neighbouring clustered cases into one contiguous 'cluster area'. This paper describes a method for studying differences between clustered and non-clustered cases, in terms of case 'attributes'. These attributes may be person related, such as age and sex, or area based, such as geographical isolation. The area-based variables are subject to geographical correlation. The comparison of clustered and non-clustered cases may reveal similarities or differences, which may, in turn, give clues to disease aetiology. A method for studying 'linkage' or similarities in attributes between cases that occur in the same clusters is also described. The methods are illustrated by application to incidence data for leukaemias and lymphomas.