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Background: Hard-to-reach population subgroups are typically investigated using convenience sampling, which may give biased estimates. Combining information from such surveys, a probability survey and clinic surveillance, can potentially minimize the bias. We developed a methodology to estimate the prevalence of undiagnosed HIV infection among men who have sex with men (MSM) in England and Wales aged 16-44 years in 2003, making fuller use of the available data than earlier work., Methods: We performed a synthesis of three data sources: genitourinary medicine clinic surveillance (11 380 tests), a venue-based convenience survey including anonymous HIV testing (3702 MSM) and a general population sexual behaviour survey (134 MSM). A logistic regression model to predict undiagnosed infection was fitted to the convenience survey data and then applied to the MSMs in the population survey to estimate the prevalence of undiagnosed infection in the general MSM population. This estimate was corrected for selection biases in the convenience survey using clinic surveillance data. A sensitivity analysis addressed uncertainty in our assumptions., Results: The estimated prevalence of undiagnosed HIV in MSM was 2.4% [95% confidence interval (95% CI 1.7-3.0%)], and between 1.6% (95% CI 1.1-2.0%) and 3.3% (95% CI 2.4-4.1%) depending on assumptions; corresponding to 5500 (3390-7180), 3610 (2180-4740) and 7570 (4790-9840) men, and undiagnosed fractions of 33, 24 and 40%, respectively., Conclusions: Our estimates are consistent with earlier work that did not make full use of data sources. Reconciling data from multiple sources, including probability-, clinic- and venue-based convenience samples can reduce bias in estimates. This methodology could be applied in other settings to take full advantage of multiple imperfect data sources.

Previous research has demonstrated that the subjective danger and usefulness of words affect lexical decision times. Usually, an interaction is found: Increasing danger predicts faster reaction times (RTs) for words low on usefulness, but increasing danger predicts slower RTs for words high on usefulness. The authors show the same interaction with immediate auditory naming. The interaction disappeared with a delayed auditory naming control experiment, suggesting that it has a perceptual basis. In an attempt to separate input (signal to ear) from output (brain to muscle) processes in word recognition, the authors ran 2 auditory perceptual identification experiments. The interaction was again significant, but performance was best for words high on both danger and usefulness. This suggests that initial demonstrations of the interaction were reflecting an output approach/withdraw response conflict induced by stimuli that are both dangerous and useful. The interaction cannot be characterized as a tradeoff of speed versus accuracy.

Background: Familial factors influence susceptibility to multiple sclerosis (MS) but it is unknown whether there are additional effects on the natural history of the disease., Method: We evaluated 1,083 families with > or =2 first-degree relatives with MS for concordance of age at onset, clinical course, and disease severity and investigated transmission patterns of these clinical features in affected parent-child pairs., Results: There is concordance for age at onset for all families (correlation coefficient 0.14; p < 0.001), as well as for affected siblings (correlation coefficient 0.15; p < 0.001), and affected parent-child pairs (correlation coefficient 0.12; p = 0.03) when each is evaluated separately. Concordance for year of onset is present among affected siblings (correlation coefficient 0.18; p < 0.001) but not the parent-child group (correlation coefficient 0.08; p = 0.15). The clinical course is similar between siblings (kappa 0.12; p < 0.001) but not affected parents and their children (kappa -0.04; p = 0.09). This influence on the natural history is present in all clinical subgroups of relapsing-remitting, and primary and secondary progressive MS, reflecting a familial effect on episodic and progressive phases of the disease. There is no concordance for disease severity within any of the considered family groups (correlation coefficients: all families analyzed together, 0.02, p = 0.53; affected sibling group, 0.02, p = 0.61; affected parent-child group, 0.02, p = 0.69). Furthermore, there are no apparent transmission patterns of any of the investigated clinical features in affected parent-child pairs and no evidence for anticipation or effects of genetic loading., Conclusion: Familial factors do not significantly affect eventual disease severity. However, they increase the probability of a progressive clinical course, either from onset or after a phase of relapsing remitting disease. The familial effect is more likely to reflect genetic than environmental conditions. The results are relevant for counseling patients and have implications for the design of studies seeking to identify factors that influence the natural history of the disease.

Background: The study design with the smallest bias for causal inference is a perfect randomized clinical trial. Since this design is often not feasible in epidemiologic studies, an important challenge is to model bias properly and take random and systematic variation properly into account. A value for a target parameter might be said to be "incompatible" with the data (under the model used) if the parameter's confidence interval excludes it. However, this "incompatibility" may be due to bias and/or extra-variation., Discussion: We propose the following way of re-interpreting conventional results. Given a specified focal value for a target parameter (typically the null value, but possibly a non-null value like that representing a twofold risk), the difference between the focal value and the nearest boundary of the confidence interval for the parameter is calculated. This represents the maximum correction of the interval boundary, for bias and extra-variation, that would still leave the focal value outside the interval, so that the focal value remained "incompatible" with the data. We describe a short example application concerning a meta analysis of air versus pure oxygen resuscitation treatment in newborn infants. Some general guidelines are provided for how to assess the probability that the appropriate correction for a particular study would be greater than this maximum (e.g. using knowledge of the general effects of bias and extra-variation from published bias-adjusted results)., Summary: Although this approach does not yet provide a method, because the latter probability can not be objectively assessed, this paper aims to stimulate the re-interpretation of conventional confidence intervals, and more and better studies of the effects of different biases.

A major aim of association studies is the identification of polymorphisms (usually SNPs) associated with a trait. Tests of association may be based on individual SNPs or on sets of neighboring SNPs, by use of (for example) a product P value method or Hotelling's T test. Linkage disequilibrium, the nonindependence of SNPs in physical proximity, causes problems for all these tests. First, multiple-testing correction for individual-SNP tests or for multilocus tests either leads to conservative P values (if Bonferroni correction is used) or is computationally expensive (if permutation is used). Second, calculation of product P values usually requires permutation. Here, we present the direct simulation approach (DSA), a method that accurately approximates P values obtained by permutation but is much faster. It may be used whenever tests are based on score statistics--for example, with Armitage's trend test or its multivariate analogue. The DSA can be used with binary, continuous, or count traits and allows adjustment for covariates. We demonstrate the accuracy of the DSA on real and simulated data and illustrate how it might be used in the analysis of a whole-genome association study.

Objective: A simple method for accounting efficiently for multiple testing of many SNPs in an association study was recently proposed by Nyholt, but its performance was not extensively evaluated. The method involves estimating an 'effective number' of independent tests and then adjusting the smallest observed p value using Sidák's formula based on this number of tests. We sought to carry out an empirical and theoretical evaluation of Nyholt's method., Methods: Nyholt's method was applied to a sample of 31 genes typed at a total of 291 SNPs and permutation used to determine the type-I error rate for each gene. Based on our empirical results, we algebraically investigated the effective number of independent tests for a simple model of haplotype block structure., Results: The nominal 5% type I error rate varied from under 3% to over 7%, and was dependent on linkage disequilibrium. Theoretical considerations show further that the method can be very conservative in the presence of haplotype block structure., Conclusion: Although Nyholt's approach may be useful as an exploratory tool, it is not an adequate substitute for permutation tests., (Copyright 2005 S. Karger AG, Basel.)

Objective: In affected sib pair studies without genotyped parents the effect of genotyping error is generally to reduce the type I error rate and power of tests for linkage. The effect of genotyping error when parents have been genotyped is unknown. We investigated the type I error rate of the single-point Mean test for studies in which genotypes of both parents are available., Methods: Datasets were simulated assuming no linkage and one of five models for genotyping error. In each dataset, Mendelian-inconsistent families were either excluded or regenotyped, and then the Mean test applied., Results: We found that genotyping errors lead to an inflated type I error rate when inconsistent families are excluded. Depending on the genotyping-error model assumed, regenotyping inconsistent families has one of several effects. It may produce the same type I error rate as if inconsistent families are excluded; it may reduce the type I error, but still leave an anti-conservative test; or it may give a conservative test. Departures of the type I error rate from its nominal level increase with both the genotyping error rate and sample size., Conclusion: We recommend that markers with high error rates either be excluded from the analysis or be regenotyped in all families., (Copyright (c) 2005 S. Karger AG, Basel.)

Until recently most models of word recognition have assumed that semantic auditory naming effects come into play only after the identification of the word in question. What little evidence exists for early semantic effects in word recognition lexical decision has relied primarily on priming manipulations using the lexical decision task, and has used visual stimulus presentation. The current study uses semantics auditory stimulus presentation and multiple experimental tasks, and does not use priming. Response latencies for 100 common nouns were found to speech perception depend on perceptual dimensions identified by Osgood (1969): Evaluation, Potency, and Activity. In addition, the two-way interactions between these word recognition dimensions were significant. All effects were above and beyond the effects of concreteness, word length, frequency, onset phoneme characteristics, stress, and neighborhood density. Results are discussed against evidence from several areas of research suggesting a role of behaviorally important information in perception.

We present a new statistical model for the analysis of case-control or cohort studies examining a highly polymorphic candidate disease susceptibility gene. Many genotypes are possible for such a gene. Consequently, the average number of subjects having each genotype will be modest. If analyzed separately, the risks associated with most genotypes will be estimated imprecisely. Our Bayesian partition model clusters genotypes according to risk, only allowing partitions that satisfy a particular assumption about the joint effect of the two alleles making up a genotype. This assumption is genetically plausible, imposes structure on the set of genotype risks, and still leaves a highly flexible model. By Bayesian model averaging over partitions, the model becomes, in effect, a semiparametric model for genotype risk. It allows borrowing of strength, i.e., estimates of risk for one genotype are informed by the risk estimates of all the genotypes. We present the results of fitting the model to two datasets, one simulated and one genuine case-control study of the NAT1 gene and lung cancer, and compare it in a simulation study with a haplotype relative risk model. The partition model enables genotype risks to be estimated more accurately and the alleles to be ranked according to risk., (Copyright 2002 Wiley-Liss, Inc.)

Interval-censored survival data are data in which the failure times are not known precisely, but are known to lie within an interval. Such data can be analysed using a proportional hazards model with piecewise-exponential baseline hazard, a model which can be fitted by an EM algorithm easily programmed in standard statistical software. In this paper we extend the model to allow for time-dependent covariates and left-truncation, and demonstrate its use by assessing the effect of imprisonment on hazard of HIV infection in a cohort of injecting drug users from Edinburgh. No conclusive effect of incarceration on hazard of HIV infection was found, but there was a suggestion that imprisonment might have been a significant relative risk factor for infection in the later period, when risk behaviour among drug users in the community was reduced., (Copyright 2001 John Wiley & Sons, Ltd.)

Background: The prevalence of HIV in prisons is often higher than in the surrounding community, because prisons contain a high proportion of injecting drug users (IDUs). Reliable estimation of HIV prevalence in UK prisons only began in the 1990s. Edinburgh, Scotland, experienced a major IDU-related HIV epidemic which began in 1983. We sought retrospectively to estimate HIV prevalence in Edinburgh Prison over the period 1983-94., Methods: Prison records of all 477 male HIV-positive patients (332 IDUs) in the Edinburgh City Hospital Cohort (believed to include three-quarters of HIV-positive Edinburgh IDUs) were abstracted from Edinburgh Prison. Using this information and the seroconversion intervals of the patients, the number of person-years spent inside the prison by these individuals while HIV-positive was estimated for each calendar month. From this, HIV prevalence was inferred., Results: HIV prevalence in the prison rose from January 1983, as prevalence among Edinburgh IDUs increased, reaching a peak of 8% in December 1984. Prevalence during 1985-86 was 5-6% and then gradually declined, as the surviving HIV-infected IDUs spent less time in the prison., Discussion: These figures are probably underestimates, as some HIV-positive prisoners are not in the cohort. However, the degree of underestimation should not be great and trends over time are reliable. Our estimate for August 1991, 4.1%, compares favourably with the estimate 4.5%, from an anonymous unlinked survey conducted in the prison that month. Prevalence estimates from other UK prisons are reviewed and suggestions made for other uses of database linkage in HIV and IDU epidemiology.

Objective: To assess whether injecting drug users have a higher than usual risk of death from overdose in the 2 weeks after release from prison., Design: Soundex coding of surnames and information on date of birth were used to link entry and release dates from the local prison between 1983 and 1994 with clinical data from Edinburgh City Hospital's cohort of male injecting drug users who are infected with HIV., Setting: Edinburgh City Hospital and Edinburgh Prison., Subjects: 316/332 male injecting drug users infected with HIV in the City Hospital HIV cohort; 16 were excluded because they were enrolled after developing AIDS or because their precise date of death was not available., Main Outcome Measure: Relative risk of dying from overdose before developing AIDS and relative risk of dying of all causes before developing AIDS during the 2 weeks after release from prison; this was compared with relative risks of death during other time at liberty., Results: 238/316 (75%) injecting drug users served time in the prison between 1983 and 1994. 33 out of 316 injecting drug users who were infected with HIV died before developing AIDS during 517,177 days at risk. 20 of these men died of an overdose; 6 of these deaths occurred within 2 weeks of release during 5903 days at risk. Death rates from overdose before the development of AIDS were 1.02/1000 days during the 2 weeks after release (recently released) and 0.029/1000 days during other times of liberty. The relative risk of death from overdose became 7.7 (1.5 to 39.1) after temporal matching (when the comparison was limited to the first 2 weeks after release v the next 10 weeks). The crude relative risk in an analysis combining stratified prison term and the 2 weeks after release was 4.5 (1.7 to 11.7) for death from overdose. After temporal matching these risks became 1.8 (0.4 to 9.2)., Conclusion: Prisons should evaluate interventions to reduce the risk of death from overdose after release.

In this paper, we look at the incidence and predictive factors of pre-AIDS mortality among HIV-infected individuals, and injecting drug users (IDUs) in particular, and compare IDUs with non-IDUs. 627 patients (73 per cent IDUs) of the Edinburgh City Hospital HIV Cohort were enrolled pre-AIDS and followed up until September 1994. Analyses were performed using cumulative hazard and cumulative incidence estimators for a competing risks model, the Cox proportional hazards model and the non-parametric hazard estimator of Fusaro et al. (1993). The effects of age and CD4 T-lymphocyte cell count, progressively depleted during HIV progression, were investigated. 60 deaths occurred in AIDS-free patients during follow-up; 25 were drug-related deaths in IDUs. Pre-AIDS mortality was higher among IDUs than non-IDUs (p = 0.07). The cumulative incidences of pre-AIDS death after five years from enrollment were 11 per cent in IDUs and 6 per cent in non-IDUs; the cumulative AIDS incidences were, respectively, 19 per cent and 32 per cent. After eight years, cumulative pre-AIDS death incidence was 15 per cent among IDUs; cumulative AIDS incidence among IDUs was 35 per cent. Both groups had similar risks of medically-related (non-AIDS)-MRNA-death. Age and CD4 count were both individually predictive of MRNA death (relative risks (RRs); 2.1 per decade of life, p < 0.01; and 1.9 for each 100 cells per 100 microliters lost, p < 0.0001), although when used together age was less significant (RR 1.6, p = 0.07). Neither was statistically significant for drug-related mortality, although hazard may be lower in older individuals and may increase with falling CD4 count. The drug-related mortality was 1.1 per cent: 2.3 per cent in the first two years after enrollment, and 0.4 per cent thereafter. We conclude that older HIV-infected individuals are at greater risk of medically-related death before AIDS. This risk increases as CD4 count declines. Drug-related hazard may be greater in younger individuals and may increase as CD4 counts fall, but neither effect was formally significant.