Complementation analysis of the flavivirus Kunjin NS3 and NS5 proteins defines the minimal regions essential for formation of a replication complex and shows a requirement of NS3 in cis for virus assembly

We have previously reported successful trans-complementation of defective Kunjin virus genomic RNAs with a range of large lethal deletions in the nonstructural genes NSI, NS3, and NS5 (A. A. Khromykh et al., J. Virol. 74:3253-3263, 2000). In this study we have mapped further the minimal region in the NS5 gene essential for efficient trans-complementation of genome-length RNAs in repBHK cells to the first 316 of the 905 codons. To allow amplification and easy detection of complemented defective RNAs with deletions apparently affecting virus assembly, we have developed a dual replicon complementation system. In this system defective replicon RNAs with a deletion(s) in the nonstructural genes also encoded the puromycin resistance gene (PAC gene) and the reporter gene for beta-galactosidase (beta-Gal). Complementation of these defective replicon RNAs in repBHK cells resulted in expression of PAC and beta-Gal which allowed establishment of cell lines stably producing replicating defective RNAs by selection with puromycin and comparison of replication efficiencies of complemented defective RNAs by beta-Gal assay. Using this system we demonstrated that deletions in the C-terminal 434 codons of NS3 (codons 178 to 611) were complemented for RNA replication, while any deletions in the first 178 codons were not. None of the genome-length RNAs containing deletions in NS3 shown to be complementable for RNA replication produced secreted defective viruses during complementation in repBHK cells. In contrast, structural proteins produced from these complemented defective RNAs were able to package helper replicon RNA. The results define minimal regions in the NS3 and NS5 genes essential for the formation of complementable replication complex and show a requirement of NS3 in cis for virus assembly.

Checklist of health promotion environments at worksites (CHEW): Development and measurement characteristics

Purpose. Health promotion policy frameworks, recent theorizing, and research all emphasize understanding and mobilizing environmental influences to change particular health-related behaviors in specific settings. The workplace is a key environmental setting. The Checklist of Health Promotion Environments at Worksites (CHEW) was designed as a direct observation instrument to assess characteristics of worksite environments that are known to influence health-related behaviors. Methods. The CHEW is a 112-item checklist of workplace environment features hypothesized to be associated, both positively and negatively, with physical activity, healthy eating, alcohol consumption, and smoking. The three environmental domains assessed are (1) physical characteristics of the worksite, (2) features of the information environment, and (3) characteristics of the immediate neighborhood around the workplace. The conceptual rationale and development studies for the CHEW are described, and data from observational studies of 20 worksites are reported. Results. The data on CHEW-derived environmental attributes showed generally good reliability and identified meaningful sets of variables that plausibly may influence health-related behaviors. With the exception of one information environment attribute, intraclass correlation coefficients ranged from 0.80 to 1.00. Descriptive statistics on selected physical and information environment characteristics indicated that vending machines, showers, bulletin boards, and signs prohibiting smoking were common across worksites. Bicycle racks, visible stairways, and signs related to alcohol consumption, nutrition, and health. promotion were relatively uncommon. Conclusions. These findings illustrate the types of data on environmental attributes that can be derived, their relevance for program planning, and how they can characterize variability across worksites. The CHEW is a promising observational measure that has the potential to assess environmental influences on health behaviors and to evaluate workplace health promotion programs.

Impact numbers in health policy decisions

Objective: To outline the major methodological issues appropriate to the use of the population impact number (PIN) and the disease impact number (DIN) in health policy decision making. Design: Review of literature and calculation of PIN and DIN statistics in different settings. Setting: Previously proposed extensions to the number needed to treat (NNT): the DIN and the PIN, which give a population perspective to this measure. Main results: The PIN and DIN allow us to compare the population impact of different interventions either within the same disease or in different diseases or conditions. The primary studies used for relative risk estimates should have outcomes, time periods and comparison groups that are congruent and relevant to the local setting. These need to be combined with local data on disease rates and population size. Depending on the particular problem, the target may be disease incidence or prevalence and the effects of interest may be either the incremental impact or the total impact of each intervention. For practical application, it will be important to use sensitivity analyses to determine plausible intervals for the impact numbers. Conclusions: Attention to various methodological issues will permit the DIN and PIN to be used to assist health policy makers assign a population perspective to measures of risk.

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.

A longitudinal analysis of sex differences in bone mineral accrual in healthy 8-19-year-old boys and girls

Background: Although early in life there is little discernible difference in bone mass between boys and girls, at puberty sex differences are observed. It is uncertain if these differences represent differences in bone mass or just differences in anthropometric dimensions. Aim: The study aimed to identify whether sex independently affects bone mineral content (BMC) accrual in growing boys and girls. Three sites are investigated: total body (TB), femoral neck (FN) and lumbar spine (LS). Subjects and methods: 85 boys and 67 girls were assessed annually for seven consecutive years. BMC was assessed by dual energy X-ray absorptiometry (DXA). Biological age was defined as years from age at peak height velocity (PHV). Data were analysed using a hierarchical (random effects) modelling approach. Results: When biological age, body size and body composition were controlled, boys had statistically significantly higher TB and FN BMC at all maturity levels (p < 0.05). No independent sex differences were found at the LS (p > 0.05). Conclusion: Although a statistical significant sex effect is observed, it is less than the error of the measurement, and thus sex difference are debatable. In general, sex difference are explained by anthropometric difference