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INTRODUCTION: Consistent use of hearing protection devices (HPDs) decreases noise-induced hearing loss, however, many workers do not use them consistently. Past research has supported the need to use a conceptual framework to understand behaviors and guide intervention programs; however, few reports have specified a process to translate a conceptual model into an intervention. PURPOSE: The strongest predictors from the Health Promotion Model were used to design a training program to increase HPD use among construction workers. SUBJECTS/SETTING: Carpenters (n = 118), operating engineers (n = 109), and plumber/pipefitters (n = 129) in the Midwest were recruited to participate in the study. DESIGN: Written questionnaires including scales measuring the components of the Health Promotion Model were completed in classroom settings at worker trade group meetings. MEASURES: All items from scales predicting HPD use were reviewed to determine the basis for the content of a program to promote the use of HPDs. Three selection criteria were developed: (1) correlation with use of hearing protection (at least .20), (2) amenability to change, and (3) room for improvement (mean score not at ceiling). RESULTS: Linear regression and Pearson's correlation were used to assess the components of the model as predictors of HPD use. Five predictors had statistically significant regression coefficients: perceived noise exposure, self-efficacy, value of use, barriers to use, and modeling of use of hearing protection. Using items meeting the selection criteria, a 20-minute videotape with written handouts was developed as the core of an intervention. A clearly defined practice session was also incorporated in the training intervention. CONCLUSION: Determining salient factors for worker populations and specific protective equipment prior to designing an intervention is essential. These predictors provided the basis for a training program that addressed the specific needs of construction workers. Results of tests of the effectiveness of the program will be available in the near future.
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BACKGROUND: Barrett's oesophagus, columnar metaplasia of the epithelium, is a premalignant condition with a 50-100-fold increased risk of cancer. The condition is caused by chronic gastro-oesophageal reflux. Regression of metaplasia may decrease the cancer risk. AIMS: To determine whether elimination of acid gastro-oesophageal reflux induces a regression of metaplastic epithelium. METHODS: Sixty eight patients with acid reflux and proven Barrett's oesophagus were included in a prospective, randomised, double blind study with parallel groups, and were treated with profound acid secretion suppression with omeprazole 40 mg twice daily, or with mild acid secretion suppression with ranitidine 150 mg twice daily, for 24 months. Endoscopy was performed at 0, 3, 9, 15, and 24 months with measurement of length and surface area of Barrett's oesophagus; pH-metry was performed at 0 and 3 months. Per protocol analysis was performed on 26 patients treated with omeprazole, and 27 patients treated with ranitidine. RESULTS: Omeprazole reduced reflux to 0.1%, ranitidine to 9.4% per 24 hours. Symptoms were ameliorated in both groups. There was a small, but statistically significant regression of Barrett's oesophagus in the omeprazole group, both in length and in area. No change was observed in the ranitidine group. The difference between the regression in the omeprazole and ranitidine group was statistically significant for the area of Barrett's oesophagus (p=0. 02), and showed a trend in the same direction for the length of Barrett's oesophagus (p=0.06). CONCLUSIONS: Profound suppression of acid secretion, leading to elimination of acid reflux, induces partial regression of Barrett's oesophagus.
This article discusses the method of fitting a straight line to data by linear regression and focuses on examples from 36 Original Articles published in the Journal in 1978 and 1979. Medical authors generally use linear regression to summarize the data (as in 12 of 36 articles in my survey) or to calculate the correlation between two variables (21 of 36 articles). Investigators need to become better acquainted with residual plots, which give insight into how well the fitted line models the data, and with confidence bounds for regression lines. Statistical computing packages enable investigators to use these techniques easily.
Recently a great deal of attention has been given to binary regression models for clustered or correlated observations. The data of interest are of the form of a binary dependent or response variable, together with independent variables X1,...., Xk, where sets of observations are grouped together into clusters. A number of models and methods of analysis have been suggested to study such data. Many of these are extensions in some way of the familiar logistic regression model for binary data that are not grouped (i.e., each cluster is of size 1). In general, the analyses of these clustered data models proceed by assuming that the observed clusters are a simple random sample of clusters selected from a population of clusters. In this paper, we consider the application of these procedures to the case where the clusters are selected randomly in a manner that depends on the pattern of responses in the cluster. For example, we show that ignoring the retrospective nature of the sample design, by fitting standard logistic regression models for clustered binary data, may result in misleading estimates of the effects of covariates and the precision of estimated regression coefficients.
The purpose of this study was to determine the association between selected social cognitive variables and self-management behaviors among a sample of adults with epilepsy. The study, based on Social Cognitive Theory, was a partial replication and extension of a previous study that explored the role of self-efficacy, social support, regimen-specific support, self-esteem, and self-management. Data collected from 80 adults attending an epilepsy clinic were analyzed using correlation and regression procedures. Statistically significant relationships were found between self-efficacy and self-management and between regimen-specific support and self-management. Regression analyses revealed that self-efficacy and regimen-specific support made significant contributions to the variance in self-management, whereas social support and self-esteem did not.
The relationship between the size of an egg and its energy content was analyzed using published data for 47 species of echinoderms. Scaling relationships were evaluated for all species, as well as for subsets of the species, based on mode of development. Regressions were calculated using linear, power function, full allometric, and second-order polynomial models. The full allometric model is preferred because it is relatively simple and the most general. Among these species of echinoderms, larger eggs contain more energy. Egg energy content scales isometrically across a wide range of egg sizes both among and within different modes of development. The only exception is among species with feeding larval development, where there does not seem to be a clear scaling relationship. In most cases, the regressions were statistically significant and explained a very large proportion of the variance in energy content. However, there were wide confidence intervals around the estimated regression parameters. In all cases, the predictive power of the regression was poor, requiring large differences in egg size to yield significantly different predictions of energy content. Consequently, egg size is of limited value for the quantitative prediction of egg energy content and should be used with caution in life-history studies.
The purpose of this study was to determine if Enterococcus spp. are more prevalent in endodontically treated teeth with periradicular lesions compared with teeth that require retreatment but have no periradicular rarefaction. Fifty-eight teeth that had received root canal therapy more than 1 yr previously and required retreatment were included. Designation of lesion versus no lesion was determined by two experienced endodontists. DNA extraction and PCR amplification were performed using ubiquitous 16S rDNA bacterial primers, as well as Enterococcus spp.-specific primers. The results showed that the overall prevalence of bacteria was 90% and Enterococcus spp. was 12%. chi analysis revealed a statistically significant relationship between the presence of a lesion and the presence of bacteria, as detected by the universal primers (p = 0.032). Using logistic regression, a statistically significant relationship was found between teeth with normal periapex and the presence of Enterococcus spp. (p = 0.023). This study revealed that bacteria are significantly associated with endodontic treatment failure but enterococci are not associated with disease.
A causal approach to the calculation of coherence and transfer function between systolic pressure (SP) and RR interval variability was applied in eight patients and eight control subjects during prolonged tilt test for investigating the impairment of cardiovascular control related to neurally mediated syncope. The causal analysis showed a depressed baroreflex regulation in resting patients, with reduced gain and increased latency from SP to RR, and a drop of the baroreflex coupling immediately before syncope. These findings, which were not elicited by traditional cross-spectral analysis, strongly suggest the use of the causal approach for the study of syncope mechanisms.
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The present study has the aim of evaluating gene-environment interaction on the levels of different biomarkers in coke-oven workers exposed to PAH. In order to assess whether the levels of some biomarkers (PAH-DNA adducts, nitro-PAH adducts to Hb and MN frequency) could be modulated by the genetic metabolic polymorphisms for CYP1A1 and GSTM1, we analysed in 76 coke-oven workers and 18 controls the CYP1A1 (MspI and Ile/Val sites) and the GSTM1 genotypes by a PCR assay. In individuals with shared setup of CYP1A1 or GSTM1 genotypes, we analysed how the specified biomarkers correlated with total PAH exposure (urinary levels of 1-hydroxypyrene) both by a stratified analysis and logistic regression modelling. Statistically significant (P = 0.03 and P = 0.01) higher percentages of the more susceptible GSTM1- subjects compared to the GSTM1+ subjects and of the more susceptible CYP1A1 Ile/Val individuals compared to the CYP1A1 Ile/Ile individuals were detected for high levels of PAH-DNA adducts in the high exposure group (namely high levels of 1-OHP). A statistically significant association was observed between increased PAH-DNA adduct levels and the more susceptible GSTM1- genotype (P.O.R. = 4.18, P = 0.03) in a logistic regression modelling and a significant interaction between PAH exposure and GSTM1-genotype was found for PAH-DNA adducts. No effect of these metabolic genotypes was observed for MN frequency and nitro-PAH adducts to Hb. In conclusion, a gene-environment interaction between PAH exposure and two metabolic genotypes involved in activation (CYP1A1) and detoxification (GSTM1) of PAHs, respectively, has been identified.
51Chromium release-derived cytotoxicity data yield curvilinear plots when the x axis displays the effector:target ratio and the y axis displays the percentage of cytotoxicity. To facilitate data analysis, several biomathematical models (simple linear regression, exponential fit, and Von Krogh) have been used to express these cytotoxicity curves as a single numerical value, termed the lytic unit. Other than using raw cytotoxicity data, the lytic unit has been the most common method of data presentation in human and animal tumor immune studies involving natural killer cells, lymphokine-activated killer cells, and cytotoxic T cells. Unfortunately, the models for determining lytic unit values incorporate assumptions and methods of calculation that can result in inaccurate model-predicted cytotoxicity in comparison with the actual observed cytotoxicity data. Even when the model is accurate in predicting cytotoxicity values (i.e., the nonlinear regression-calculated three-parameter Von Krogh model), comparisons between donors of minimally different or highly different cytotoxicity are still fraught with potential error due to statistically verifiable violations of assumptions of parallelism. Although more cumbersome, donor cytotoxicity comparisons using a range of effector:target ratios are not subject to the above problems. Researchers may therefore want to reconsider the use of lytic units when evaluating and reporting cytotoxicity data.
Concentrations of cerebrospinal fluid (CSF) Ig classes G, A, M, D, and E have been reported in various neurologic disorders, viz, aseptic meningitis, multiple sclerosis, benign tumor, malignant tumor, and hydrocephalus. In aseptic meningitis, IgG and IgM were found to be either normal or elevated, whereas concentrations of IgA, IgD, and IgE were normal. Multiple sclerosis patients had IgG, IgA, and IgM, either normal or elevated, but IgD and IgE were on the lower end of the normal. Malignant tumor patients had elevated concentrations of IgG, IgA, IgM, and IgE, but their IgD was on the lower end of the normal. In benign tumor population IgA and IgE, and in hydrocephalus IgG and IgA, were found to be elevated in most of the patients. On statistical analysis, significant differences were observed between the means of the normal group and that of the patients' group for all the five immunoglobulins. By linear regression, a statistical relationship was observed between IgA - IgG, IgM - IgG, IgA - IgM and IgA + IgM - IgG in these neurologic disorders.
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