Health care professionals' preferences for extending mammographic breast screening to the over 70s

Background Breast screening improves survival in women aged 50–70. The relative benefits of screening in women over 70 are reduced but women up to age 80 may still benefit. In the UK the National Health Service provides screening by self referral to women >70. This research has investigated health care professionals' (HCPs') preferences for extending screening to older women and factors they consider when advising about screening. Materials and methods UK HCPs for breast or elderly care were recruited. A questionnaire relating to screening in the >70s was administered. A sample of respondents were also interviewed to give added insight. Results Questionnaires were distributed to 488 HCPs and 139 replies received, (29%). A total of 26 professionals were also interviewed. Most felt the current system of self referral was under-utilized due to poor user awareness. Predicted life expectancy, co-morbidity and patient preference were viewed as important factors influencing screening recommendation. Chronological age was thought less important. The present system was thought flawed, but there was little enthusiasm for extending screening due to perceived risks and reduced cost efficacy. Some form of selectivity for fitter women was advocated. Conclusions There was acceptance that fitter older women may benefit from screening whilst the less fit may be harmed suggesting that some form of selective invitation would be preferable to the present system but would be practically difficult and costly to administer. The present system of self referral was felt to be inadequate due to low levels of awareness and uptake

A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring.

This paper presents a parametric method of fitting semi-Markov models with piecewise-constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three-state illness-death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time-varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation-Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study

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Sawja: Static Analysis Workshop for Java

Static analysis is a powerful technique for automatic verification of programs but raises major engineering challenges when developing a full-fledged analyzer for a realistic language such as Java. This paper describes the Sawja library: a static analysis framework fully compliant with Java 6 which provides OCaml modules for efficiently manipulating Java bytecode programs. We present the main features of the library, including (i) efficient functional data-structures for representing program with implicit sharing and lazy parsing, (ii) an intermediate stack-less representation, and (iii) fast computation and manipulation of complete programs

Separating within and between effects in family studies: an application to the study of blood pressure in children.

In epidemiology the analyses of family or twin studies do not always fully exploit the data, as information on differences between siblings is often used while between-families effect are not considered. We show how cross-sectional time-series linear regression analysis can be easily implemented to estimate within- and between-families effects simultaneously and how these effects can then be compared using the Hausman test. We illustrate this approach with data from the Uppsala family study on blood pressure in children with age ranging from 5.5 to 12.3 years for the younger and from 7.5 to 13.8 years for the older siblings. Comparing the effect of differences in birth weight on blood pressure within-family (in full siblings) and between-families (in unrelated children) allows us to study the contributions of fixed and pregnancy-specific maternal effects on birth weight and consequently on blood pressure. Our data showed a 0.88 mmHg decrease (95 per cent confidence interval: -1.7 to -0.03 mmHg) in systolic blood pressure for one standard deviation increase in birth weight between siblings within a family and 0.88 mmHg (95 per cent confidence interval: -1.6 to -0.2 mmHg) decrease in systolic blood pressure for one standard deviation increase in birth weight between unrelated children. These estimates were controlled for sex, age, pubertal stage, body size and pulse rate of the children at examination and for maternal body size and systolic blood pressure. The within- and between-families effects were not significantly different, p = 0.19, suggesting that fixed and pregnancy-specific factors have similar effects on childhood systolic blood pressure

Structural Encoding of Static Single Assignment Form

AbstractStatic Single Assignment (SSA) form is often used as an intermediate representation during code optimization in Java Virtual Machines. Recently, SSA has successfully been used for bytecode verification. However, constructing SSA at the code consumer is costly. SSA-based mobile code transport formats have been shown to eliminate this cost by shifting SSA creation to the code producer. These new formats, however, are not backward compatible with the established Java class-file format. We propose a novel approach to transport SSA information implicitly through structural code properties of standard Java bytecode. While the resulting bytecode sequence can still be directly executed by traditional Virtual Machines, our novel VM can infer SSA form and confirm its safety with virtually no overhead

Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results

BACKGROUND: This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient costs. However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples. In particular, the requirement that the number of clusters randomised should be large would not be satisfied in many cluster RCTs performed to date. METHODS: The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted bootstrap procedures were compared in terms of confidence interval coverage in a large number of simulations. Parameters varied included the intracluster correlation coefficient, the sample size and the distributions used to generate the data. RESULTS: The bootstrap's advantage in dealing with skewed data was found to be outweighed by its poor confidence interval coverage when the number of clusters was at the level frequently found in cluster RCTs in practice. Simulations showed that confidence intervals based on robust methods of standard error estimation achieved coverage rates between 93.5% and 94.8% for a 95% nominal level whereas those for the bootstrap ranged between 86.4% and 93.8%. CONCLUSION: In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of clusters and extremely skewed data would be necessary for the bootstrap to be considered in favour of the robust method. There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately

Autistic symptomatology and language ability in Autism Spectrum Disorder and Specific Language Impairment

Background: Autism spectrum disorders (ASD) and specific language impairment (SLI) are common developmental disorders characterised by deficits in language and communication. The nature of the relationship between them continues to be a matter of debate. This study investigates whether the co-occurrence of ASD and language impairment is associated with differences in severity or pattern of autistic symptomatology or language profile. Methods: Participants (N = 97) were drawn from a total population cohort of 56,946 screened as part of study to ascertain the prevalence of ASD, aged 9 to 14 years. All children received an ICD-10 clinical diagnosis of ASD or No ASD. Children with nonverbal IQ 80 were divided into those with a language impairment (language score of 77 or less) and those without, creating three groups: children with ASD and a language impairment (ALI; N = 41), those with ASD and but no language impairment (ANL; N = 31) and those with language impairment but no ASD (SLI; N = 25). Results: Children with ALI did not show more current autistic symptoms than those with ANL. Children with SLI were well below the threshold for ASD. Their social adaptation was higher than the ASD groups, but still nearly 2 SD below average. In ALI the combination of ASD and language impairment was associated with weaker functional communication and more severe receptive language difficulties than those found in SLI. Receptive and expressive language were equally impaired in ALI, whereas in SLI receptive language was stronger than expressive. Conclusions: Co-occurrence of ASD and language impairment is not associated with increased current autistic symptomatology but appears to be associated with greater impairment in receptive language and functional communication