Measurement error caused by spatial misalignment in environmental epidemiology

In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.     

A Latent Class Model with Hidden Markov Dependence for Array CGH Data

Summary Array CGH is a high‐throughput technique designed to detect genomic alterations linked to the development and progression of cancer. The technique yields fluorescence ratios that characterize DNA copy number change in tumor versus healthy cells. Classification of tumors based on aCGH profiles is of scientific interest but the analysis of these data is complicated by the large number of highly correlated measures. In this article, we develop a supervised Bayesian latent class approach for classification that relies on a hidden Markov model to account for the dependence in the intensity ratios. Supervision means that classification is guided by a clinical endpoint. Posterior inferences are made about class‐specific copy number gains and losses. We demonstrate our technique on a study of brain tumors, for which our approach is capable of identifying subsets of tumors with different genomic profiles, and differentiates classes by survival much better than unsupervised methods.     

A penalized latent class model for ordinal data

Latent class models provide a useful framework for clustering observations based on several features. Application of latent class methodology to correlated, high-dimensional ordinal data poses many challenges. Unconstrained analyses may not result in an estimable model. Thus, information contained in ordinal variables may not be fully exploited by researchers. We develop a penalized latent class model to facilitate analysis of high-dimensional ordinal data. By stabilizing maximum likelihood estimation, we are able to fit an ordinal latent class model that would otherwise not be identifiable without application of strict constraints. We illustrate our methodology in a study of schwannoma, a peripheral nerve sheath tumor, that included 3 clinical subtypes and 23 ordinal histological measures.     

Rapid detection, identification, and enumeration of Escherichia coli cells in municipal water by chemiluminescent in situ hybridization

A new chemiluminescent in situ hybridization (CISH) method provides simultaneous detection, identification, and enumeration of culturable Escherichia coli cells in 100 ml of municipal water within one working day. Following filtration and 5 h of growth on tryptic soy agar at 35 degrees C, individual microcolonies of E. coli were detected directly on a 47-mm-diameter membrane filter using soybean peroxidase-labeled peptide nucleic acid (PNA) probes targeting a species-specific sequence in E. coli 16S rRNA. Within each microcolony, hybridized, peroxidase-labeled PNA probe and chemiluminescent substrate generated light which was subsequently captured on film. Thus, each spot of light represented one microcolony of E. coli. Following probe selection based on 16S ribosomal DNA (rDNA) sequence alignments and sample matrix interference, the sensitivity and specificity of the probe Eco16S07C were determined by dot hybridization to RNA of eight bacterial species. Only the rRNA of E. coli and Pseudomonas aeruginosa were detected by Eco16S07C with the latter mismatch hybridization being eliminated by a PNA blocker probe targeting P. aeruginosa 16S rRNA. The sensitivity and specificity for the detection of E. coli by PNA CISH were then determined using 8 E. coli strains and 17 other bacterial species, including closely related species. No bacterial strains other than E. coli and Shigella spp. were detected, which is in accordance with 16S rDNA sequence information. Furthermore, the enumeration of microcolonies of E. coli represented by spots of light correlated 92 to 95% with visible colonies following overnight incubation. PNA CISH employs traditional membrane filtration and culturing techniques while providing the added sensitivity and specificity of PNA probes in order to yield faster and more definitive results.     

Filter-based PNA in situ hybridization for rapid detection, identification and enumeration of specific micro-organisms

AIMS: A method for rapid and simultaneous detection, identification and enumeration of specific micro-organisms using Peptide Nucleic Acid (PNA) probes is presented. METHODS AND RESULTS: The method is based on a membrane filtration technique. The membrane filter was incubated for a short period of time. The microcolonies were analysed by in situ hybridization, using peroxidase-labelled PNA probes targeting a species-specific rRNA sequence, and visualized by a chemiluminescent reaction. Microcolonies were observed as small spots of light on film, thereby providing simultaneous detection, identification and enumeration. The method showed 95-100% correlation to standard plate counts along with definitive identification due to the specificity of the probe. CONCLUSION: Using the same protocol, results were generated approximately three times faster than culture methods for Gram-positive and -negative bacterial species and yeast species. SIGNIFICANCE AND IMPACT OF THE STUDY: The method is an improvement on the current membrane filtration technique, providing rapid determination of the level of specific pathogens, spoilage or indicator micro-organisms.     

Respiratory health and air pollution: additive mixed model analyses

We conduct a reanalysis of data from the Utah Valley respiratory health/air pollution study of Pope and co-workers (Pope et al., 1991) using additive mixed models. A relatively recent statistical development (e.g. Wang, 1998; Verbyla et al., 1999; Lin and Zhang, 1999), the methods allow for smooth functional relationships, subject-specific effects and time series error structure. All three of these are apparent in the Utah Valley data.     

Effect of prenatal exposure to anticonvulsant drugs on dermal ridge patterns of fingers

BACKGROUND: An altered frequency of specific dermal ridge patterns on fingertips, such as an increased number of arches, has been observed in children exposed in utero to anticonvulsants and other teratogens. Asymmetry of the distribution of dermal ridge patterns has been attributed to environmental exposures and genetic factors. METHODS: We evaluated all of the dermal ridge patterns of 66 children who had been exposed to either the anticonvulsant phenytoin alone or phenytoin and phenobarbital. We determined the frequency of each pattern, concordance between the fingers on the left and right hands, sex differences and total ridge counts in the drug-exposed children and compared them to the findings in 716 unexposed comparison children. The frequency of each pattern was established in comparison to the most common type of pattern (ulnar loop), which showed that there were alterations in the frequency of arches, radial loops and whorls on specific fingers. RESULTS: Eight (12.1%) of 66 children had three or more arch patterns, with all but one having been exposed to phenytoin and phenobarbital. Only one of these eight children was considered by the masked examiner to have fingernail hypoplasia. There was no evidence of asymmetry in the anticonvulsant-exposed children. There were minor differences in the distribution of total ridge count. CONCLUSIONS: Subtle differences in several dermal ridge patterns, not just arch patterns, were present in anticonvulsant-exposed children, primarily in those exposed to polytherapy: phenytoin and phenobarbital.     

The use of mixed logit models to reflect heterogeneity in capture-recapture studies

We examine issues in estimating population size N with capture-recapture models when there is variable catchability among subjects. We focus on a logistic-normal mixed model, for which the logit of the probability of capture is an additive function of a random subject and a fixed sampling occasion parameter. When the probability of capture is small or the degree of heterogeneity is large, the log-likelihood surface is relatively flat and it is difficult to obtain much information about N. We also discuss a latent class model and a log-linear model that account for heterogeneity and show that the log-linear model has greater scope. Models assuming homogeneity provide much narrower intervals for N but are usually highly overly optimistic, the actual coverage probability being much lower than the nominal level.     

Chemiluminescent detection of DNA: application for DNA sequencing and hybridization.

A non-radioactive DNA detection chemistry is described and its application is shown for DNA hybridization and standard dideoxy DNA sequencing. The method employes a biotin-streptavidin system which binds an enzyme specifically to a target DNA and upon exposure to substrate, the enzyme catalyzes a chemiluminescent reaction. The image is captured within seconds by a Polaroid or X-ray film. The method is capable of detecting DNA in the hundred attomol range.