Research project on advocacy and autism

This article presents the findings of a qualitative research project about the difficulties in accessing advocacy faced by adults labelled as having autism and Asperger's Syndrome in the Northwest of England. It is also an example of partnership working between three organisations. The article examines both the process of team-led (emancipatory) research and the project findings. Seven main themes emerged throughout the research: late diagnosis and lack of service support; bad experiences with systems of care; feelings of 'not belonging' (identity issues); barriers around communication and sensitivity towards individuals; lack of awareness and access to advocacy and rights; difficulties of 'fitting into' what is already available; and interest in developing knowledge around advocacy. Examination of the main themes pointed to a disturbing link between poor service response, episodes of crisis and mental ill health. The Carlisle People First Research Team is made up of 6 researchers who are labelled as having 'learning difficulties' who work in partnership with one other researcher.     

Exposure Reconstruction for the TCDD-Exposed NIOSH Cohort Using a Concentration- and Age-Dependent Model of Elimination

Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.     

When You Can't Afford to Leave Home: Clinical Implications of Economic Realities

Young adults entering the job market are finding that, despite strong corporate profits, economic growth is not trickling down to worker earnings, and federal policies are moving from the notion of entitlement to one of blame (Uchitelle, 1996, Sec. 4, p. 1). Adolescence is becoming increasingly confusing, since for many Americans economic opportunity is more fantasy than reality. This article highlights the declining opportunity for young adults and points out the limitations of existing adult developmental theory, both of which call for altered treatment conceptualizations that include the clinician operating as an anthropologist and utilizing the concepts of relationship-differentiation and response/ability from Stone Center writings.     

Strength of attachment: Survey coverage of people with tenuous ties to residences

This article draws on evidence from an exploratory survey of living situations to assess the validity of assumptions about residence and to offer methodological innovations to improve coverage of people with tenuous attachment to households. These innovations include more inclusive probes and questions used to compile the roster of household residents; a review of the places a person stayed the previous few months; and a Residential Attachment scale that measures strength of attachment to households. This scale shows that it takes more probing to list tenuously attached people than most surveys do, suggesting that tenuously attached people are likely to be omitted from the typical survey roster. The Residential Attachment scale is correlated with social and economic participation in households, suggesting that participation is fairly high even among those with tenuous or no residential attachment.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

On estimation and diagnostics analysis in log-generalized gamma regression model for interval-censored data

The interval-censored survival data appear very frequently, where the event of interest is not observed exactly but it is only known to occur within some time interval. In this paper, we propose a location-scale regression model based on the log-generalized gamma distribution for modelling interval-censored data. We shall be concerned only with parametric forms. The proposed model for interval-censored data represents a parametric family of models that has, as special submodels, other regression models which are broadly used in lifetime data analysis. Assuming interval-censored data, we consider a frequentist analysis, a Jackknife estimator and a non-parametric bootstrap for the model parameters. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some techniques to perform global influence.     

A Goodness-of-Fit Test for Location-Scale Max-Stable Distributions

In this article, a technique based on the sample correlation coefficient to construct goodness-of-fit tests for max-stable distributions with unknown location and scale parameters and finite second moment is proposed. Specific details to test for the Gumbel distribution are given, including critical values for small sample sizes as well as approximate critical values for larger sample sizes by using normal quantiles. A comparison by Monte Carlo simulation shows that the proposed test for the Gumbel hypothesis is substantially more powerful than some other known tests against some alternative distributions with positive skewness coefficient.