Factors associated with 'stress of conscience' in healthcare

Aim: The main purpose of this study was to examine factors related to ‘stress of conscience’ i.e. stress related to a troubled conscience in healthcare. Methods: A series of questionnaires was completed by 423 healthcare employees in northern Sweden as part of this cross‐sectional study. The series of questionnaires comprised the ‘Stress of Conscience Questionnaire’, ‘Perception of Conscience Questionnaire’, ‘Revised Moral Sensitivity Questionnaire’, Social Interactions Scale, Resilience Scale and a Personal/Work Demographic form. Results: Nonautomatic stepwise regression analysis with forward inclusion resulted in a model that explained approximately 39.6% of the total variation in stress of conscience. Individual items associated with stress of conscience were; perceiving that conscience warns us against hurting others while at the same time not being able to follow one’s conscience at work and having to deaden one’s conscience to keep working in healthcare. In addition moral sensitivity items belonging to the factor ‘sense of moral burden’ were; one’s ability to sense patient’s needs means that one is doing more than one has strength for, having difficulty to deal with feelings aroused when a patient is suffering and one’s ability to sense patient’s needs means feeling inadequate all added significantly to the model. In addition, deficient social support from superiors, low levels of resilience and working in internal medicine wards were all associated with stress of conscience. Conclusion: Healthcare employees seem to experience stress of conscience in their everyday practise. Particular contributing factors are not being able to follow one’s conscience at work, and the ‘negative’ dimension of moral sensitivity – moral burden – which is an inability to deal with moral problems. Thus, in order for conscience and moral sensitivity to become an asset instead of a burden, healthcare employees need to be able to express their moral concerns.     

Health status, symptoms and health counselling among middle-aged men: comparison of men at low and high risk

Aim: To assess the levels of health indicators, health behaviour and health counselling among men at low and high risk for adverse health outcomes. Methods: A total of 273 middle‐aged men, 145 at low and 128 at high risk for adverse health outcomes, were studied. Two‐ and three‐way tables with chi‐squared tests were performed to identify differences between the groups. A step‐wise logistic regression model was used to analyse symptoms and complaints associated with the likelihood of perceived health. Results: One‐half of the low‐risk men were overweight, of whom 8% were obese. Forty per cent of the low‐risk men smoked cigarettes and one‐fifth used alcohol excessively. Headache, chest and back pain, stress, and insomnia occurred frequently (range: 20–38%) and were highly correlated with depression. Joint pain (p = 0.012) in the low‐risk men and sciatica (p = 0.047) in the high‐risk men were the only statistically significant differences related to normal weight vs. overweight status. There was a greater than sixfold odds of average/poor health among low‐risk men who were depressed than in those who were not depressed men. Only a small percentage of the low‐risk men had received counselling from professionals for different health issues, including weight control and smoking cessation; the corresponding percentages were somewhat higher when given by family members. Conclusions: A real need for better counselling was found among middle‐aged men identified with obesity and risky behaviours. Public health nurses and other health workers should be aware of the differences between men at low and high risk. Men had different health experiences and lifestyles in these groups. More research is needed to determine the most efficient counselling strategies among men.     

Thymoma with spontaneous regression and disappearance of pleural effusion

A 31-year-old woman was admitted to our hospital with sudden onset of chest pain. Chest radiography and computed tomography (CT) on admission showed an anterior mediastinal tumor with left pleural effusion, which was diagnosed as an inoperable malignant mediastinal tumor. However, 3 weeks after admission CT showed that the tumor was diminishing and the pleural effusion had disappeared without any treatment. CT-guided needle biopsy was performed, but diagnosis was impossible because most of the specimen was necrotic. A biopsy during video-assisted thoracic surgery was then performed. The intraoperative finding showed that the tumor was round, well mobilized, and did not invade adjacent structures. It was then assumed to be a benign teratoma that had been ruptured into the thoracic cavity. The operation was converted to a thoracotomy to resect it, but it could not be completely resected because of inflammatory adhesions to the mediastinum. Two months later, total thymectomy was performed through a median sternotomy because the tumor was pathologically diagnosed as a thymoma.     

Instrumental variables estimation and inference in the presence of many exogenous regressors

Summary We consider a standard instrumental variables model contaminated by the presence of a large number of exogenous regressors. In an asymptotic framework where this number is proportional to the sample size, we study the impact of their ratio on the validity of existing estimators and tests. When the instruments are few, the inference using the conventional 2SLS estimator and associated t and J statistics, as well as the Anderson–Rubin and Kleibergen tests, is still valid. When the instruments are many, the LIML estimator remains consistent, but the presence of many exogenous regressors changes its asymptotic variance. Moreover, the conventional bias correction of the 2SLS estimator is no longer appropriate. We provide asymptotically correct versions of bias correction for the 2SLS estimator, derive its asymptotically correct variance estimator, extend the Hansen–Hausman–Newey LIML variance estimator to the case of many exogenous regressors, and propose asymptotically valid modifications of the J overidentification tests based on the LIML and bias‐corrected 2SLS estimators.     

Statistical Methods for Analyzing Tissue Microarray Data

Abstract

Tissue microarrays (TMAs) are a new high-throughput tool for the study of protein expression patterns in tissues and are increasingly used to evaluate the diagnostic and prognostic importance of biomarkers. TMA data are rather challenging to analyze. Covariates are highly skewed, non-normal, and may be highly correlated. We present statistical methods for relating TMA data to censored time-to-event data. We review methods for evaluating the predictive power of Cox regression models and show how to test whether biomarker data contain predictive information above and beyond standard pathology covariates. We use nonparametric bootstrap methods to validate model fitting indices such as the concordance index. We also present data mining methods for characterizing high risk patients with simple biomarker rules. Since researchers in the TMA community routinely dichotomize biomarker expression values, survival trees are a natural choice. We also use bump hunting (patient rule induction method), which we adapt to the use with survival data. The proposed methods are applied to a kidney cancer tissue microarray data set.     

肺鳞癌预后的Cox回归研究

目的 综合分析影响肺鳞癌预后的临床及社会心理等各种因素，建立肺鳞癌患者的预后指数（ＰＩ）模型，为临床上选择治疗方案、判断患者的预后及开展社会心理健康教育提供参考。方法 对３０８例肺鳞癌患者进行全程随访，运用Ｃｏｘ回归模型对各种预后因素进行分析。结果 从６９项预后因素中确定病后情绪、初治时手术方式、病后饮食情况、初治时合并其它疾病、社会支持、初治时单纯放疗方式及复治次数７项为影响肺鳞癌预后的主要因素     

Taste detection ability of elderly nursing home residents

Due to the rapid rise of aged populations throughout the world, it is essential to elucidate the cause of taste dysfunction, because it may reduce appetite, leading to inadequate dietary intake. We aimed to compare taste detection ability between dependently and independently living geriatric individuals of nearly the same age with oral status. Forty‐three elderly individuals considered to be cognitively eligible and residing in nursing homes in Japan were enrolled (n = 43, 82·3 ± 8·5 years) and were compared with an independently living elderly group (n = 949, 79·9 ± 0·8 years), aiming to compare taste detection ability between dependently and independently living elders of nearly the same age. Information regarding comorbidity and medication was obtained as general health status, and oral status including number of present teeth, denture usage and maximal occlusal force was also noted. In the dependently living group, 69·4%, 14·3%, 16·3% and 8·2% of participants could detect sweet, sour, salty and bitter tastes, respectively, which was significantly lower than the independently living group for each taste (97·9%, 70·8%, 89·6% and 43·8% for sweet, sour, salty and bitter tastes, respectively). The multivariate logistic regression analysis revealed that residing in nursing homes was associated with reduced sensitivity for four different tastes. The diseases and the situation of dependent elders were more likely the cause of the decreased taste sensitivity.     

Estimation of gene–environment interaction by pooling biospecimens

Case‐control studies are prone to low power for testing gene–environment interactions (GXE) given the need for a sufficient number of individuals on each strata of disease, gene, and environment. We propose a new study design to increase power by strategically pooling biospecimens. Pooling biospecimens allows us to increase the number of subjects significantly, thereby providing substantial increase in power. We focus on a special, although realistic case, where disease and environmental statuses are binary, and gene status is ordinal with each individual having 0, 1, or 2 minor alleles. Through pooling, we obtain an allele frequency for each level of disease and environmental status. Using the allele frequencies, we develop a new methodology for estimating and testing GXE that is comparable to the situation when we have complete data on gene status for each individual. We also explore the measurement process and its effect on the GXE estimator. Using an illustration, we show the effectiveness of pooling with an epidemiologic study, which tests an interaction for fiber and paraoxonase on anovulation. Through simulation, we show that taking 12 pooled measurements from 1000 individuals achieves more power than individually genotyping 500 individuals. Our findings suggest that strategic pooling should be considered when an investigator designs a pilot study to test for a GXE. Published 2012. This article is a US Government work and is in the public domain in the USA.