Analysis of prognostic factors for the development of metastases and survival in renal cell carcinoma patients

Introduction and objective. A number of prognostic factors have been reported for predicting survival in patients with renal cell carcinoma. Yet few studies have analyzed the effects of those factors at different stages of the disease process. In this study, different stages of disease progression starting from nephrectomy to metastasis, from metastasis to death, and from evaluation to death were evaluated. ^ Methods. In this retrospective follow-up study, records of 97 deceased renal cell carcinoma (RCC) patients were reviewed between September 2006 to October 2006. Patients with TNM Stage IV disease before nephrectomy or with cancer diagnoses other than RCC were excluded leaving 64 records for analysis. Patient TNM staging, Furhman Grade, age, tumor size, tumor volume, histology and patient gender were analyzed in relation to time to metastases. Time from nephrectomy to metastasis, TNM staging, Furhman Grade, age, tumor size, tumor volume, histology and patient gender were tested for significance in relation to time from metastases to death. Finally, analysis of laboratory values at time of evaluation, Eastern Cooperative Oncology Group performance status (ECOG), UCLA Integrated Staging System (UISS), time from nephrectomy to metastasis, TNM staging, Furhman Grade, age, tumor size, tumor volume, histology and patient gender were tested for significance in relation to time from evaluation to death. Linear regression and Cox Proportional Hazard (univariate and multivariate) was used for testing significance. Kaplan-Meier Log-Rank test was used to detect any significance between groups at various endpoints. ^ Results. Compared to negative lymph nodes at time of nephrectomy, a single positive lymph node had significantly shorter time to metastasis (p<0.0001). Compared to other histological types, clear cell histology had significant metastasis free survival (p=0.003). Clear cell histology compared to other types (p=0.0002 univariate, p=0.038 multivariate) and time to metastasis with log conversion (p=0.028) significantly affected time from metastasis to death. A greater than one year and greater than two year metastasis free interval, compared to patients that had metastasis before one and two years, had statistically significant survival benefit (p=0.004 and p=0.0318). Time from evaluation to death was affected by greater than one year metastasis free interval (p=0.0459), alcohol consumption (p=0.044), LDH (p=0.006), ECOG performance status (p<0.001), and hemoglobin level (p=0.0092). The UISS risk stratified the patient population in a statistically significant manner for survival (p=0.001). No other factors were found to be significant. ^ Conclusion. Clear cell histology is predictive for both time to metastasis and metastasis to death. Nodal status at time of nephrectomy may predict risk of metastasis. The time interval to metastasis significantly predicts time from metastasis to death and time from evaluation to death. ECOG performance status, and hemoglobin levels predicts survival outcome at evaluation. Finally, UISS appropriately stratifies risk in our population. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Work related musculoskeletal disorders amongst therapists in physically demanding roles: Qualitative analysis of risk factors and strategies for prevention

Background Physiotherapy and occupational therapy are two professions at high risk of work related musculoskeletal disorders (WRMD). This investigation aimed to identify risk factors for WRMD as perceived by the health professionals working in these roles (Aim 1), as well as current and future strategies they perceive will allow them to continue to work in physically demanding clinical roles (Aim 2). Methods A two phase exploratory investigation was undertaken. The first phase included a survey administered via a web based platform with qualitative open response items. The second phase involved four focus group sessions which explored topics obtained from the survey. Thematic analysis of qualitative data from the survey and focus groups was undertaken. Results Overall 112 (34.3%) of invited health professionals completed the survey; 66 (58.9%) were physiotherapists and 46 (41.1%) were occupational therapists. Twenty-four health professionals participated in one of four focus groups. The risk factors most frequently perceived by health professionals included: work postures and movements, lifting or carrying, patient related factors and repetitive tasks. The six primary themes for strategies to allow therapists to continue to work in physically demanding clinical roles included: organisational strategies, workload or work allocation, work practices, work environment and equipment, physical condition and capacity, and education and training. Conclusions Risk factors as well as current and potential strategies for reducing WRMD amongst these health professionals working in clinically demanding roles have been identified and discussed. Further investigation regarding the relative effectiveness of these strategies is warranted.

Spatial analysis of risk factors for transmission of the Barmah forest virus in Queensland, Australia

Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia but few data are available on the risk factors. We assessed the impact of spatial climatic, socioeconomic and ecological factors on the transmission of BFV disease in Queensland, Australia, using spatial regression. All our analyses indicate that spatial lag models provide a superior fit to the data compared to spatial error and ordinary least square models. The residuals of the spatial lag models were found to be uncorrelated, indicating that the models adequately account for spatial and temporal autocorrelation. Our results revealed that minimum temperature, distance from coast and low tide were negatively and rainfall was positively associated with BFV disease in coastal areas, whereas minimum temperature and high tide were negatively and rainfall was positively associated with BFV disease (all P-value.

An empirical analysis of the factors and measures of Enterprise Architecture Management success

Enterprise Architecture Management (EAM) is discussed in academia and industry as a vehicle to guide IT implementations, alignment, compliance assessment, or technology management. Still, a lack of knowledge prevails about how EAM can be successfully used, and how positive impact can be realized from EAM. To determine these factors, we identify EAM success factors and measures through literature reviews and exploratory interviews and propose a theoretical model that explains key factors and measures of EAM success. We test our model with data collected from a cross-sectional survey of 133 EAM practitioners. The results confirm the existence of an impact of four distinct EAM success factors, ‘EAM product quality’, ‘EAM infrastructure quality’, ‘EAM service delivery quality’, and ‘EAM organizational anchoring’, and two important EAM success measures, ‘intentions to use EAM’ and ‘Organizational and Project Benefits’ in a confirmatory analysis of the model. We found the construct ‘EAM organizational anchoring’ to be a core focal concept that mediated the effect of success factors such as ‘EAM infrastructure quality’ and ‘EAM service quality’ on the success measures. We also found that ‘EAM satisfaction’ was irrelevant to determining or measuring success. We discuss implications for theory and EAM practice.

Missing in space: An evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Background Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. Methods We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Results Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Conclusions Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

Is there a subgroup of long-term evolution among patients with advanced lung cancer?: Hints from the analysis of survival curves from cancer registry data

Background: Recently, with the access of low toxicity biological and targeted therapies, evidence of the existence of a long-term survival subpopulation of cancer patients is appearing. We have studied an unselected population with advanced lung cancer to look for evidence of multimodality in survival distribution, and estimate the proportion of long-term survivors. Methods: We used survival data of 4944 patients with non-small-cell lung cancer (NSCLC) stages IIIb-IV at diagnostic, registered in the National Cancer Registry of Cuba (NCRC) between January 1998 and December 2006. We fitted one-component survival model and two-component mixture models to identify short-and long-term survivors. Bayesian information criterion was used for model selection. Results: For all of the selected parametric distributions the two components model presented the best fit. The population with short-term survival (almost 4 months median survival) represented 64% of patients. The population of long-term survival included 35% of patients, and showed a median survival around 12 months. None of the patients of short-term survival was still alive at month 24, while 10% of the patients of long-term survival died afterwards. Conclusions: There is a subgroup showing long-term evolution among patients with advanced lung cancer. As survival rates continue to improve with the new generation of therapies, prognostic models considering short-and long-term survival subpopulations should be considered in clinical research.

Urban mammals: what does the future hold? An analysis of the factors affecting patterns of use of residential gardens in Great Britain

1Urban areas are predicted to grow significantly in the foreseeable future because of increasing human population growth. Predicting the impact of urban development and expansion on mammal populations is of considerable interest due to possible effects on biodiversity and human-wildlife conflict. 2The British government has recently announced a substantial housing programme to meet the demands of its growing population and changing socio-economic profile. This is likely to result in the construction of high-density, low-cost housing with small residential gardens. To assess the potential effects of this programme, we analysed the factors affecting the current pattern of use of residential gardens by a range of mammal species using a questionnaire distributed in wildlife and gardening magazines and via The Mammal Society. 3Twenty-two species/species groups were recorded. However, the pattern of garden use by individual species was limited, with only six species/species groups (bats, red fox Vulpes vulpes, grey squirrel Sciurus carolinensis, hedgehog Erinaceus europaeus, mice, voles) recorded as frequent visitors to > 20% of gardens in the survey. 4There was a high degree of association between the variables recorded in the study, such that it was difficult to quantify the effects of individual variables. However, all species/species groups appeared to be negatively affected by the increased fragmentation and reduced proximity of natural and semi-natural habitats, decreasing garden size and garden structure, but to differing degrees. Patterns of garden use were most clearly affected by house location (city, town, village, rural), with garden use declining with increasing urbanization for the majority of species/species groups, except red foxes and grey squirrels. Increasing urbanization is likely to be related to a wide range of interrelated factors, any or all of which may affect a range of mammal species. 5Overall, the probable effects of the planned housing development programme in Britain are not likely to be beneficial to mammal populations, although the pattern of use examined in this study may represent patterns of habitat selection by species rather than differences in distribution or abundance. Consequently, additional data are required on the factors affecting the density of species within urban environments.

An empirical analysis of attendance factors relating to the female sports market

Although approximately seven million Australians attended sporting events in 2002, male spectators outnumbered female spectators by 25 percent (Australian Bureau of Statistics 2003). This study investigates the reasons for this difference by analysing a survey of 175 female sporting event attendees. The findings show that female attendance motivations are different for different sporting events, for example, football, horse racing and tennis. A number of other factors that were also found to influence attendance amongst the female market are explained and discussed and implications for sports venue managers are presented.

An exploratory analysis of the factors that influence food choice among young women

Food sales represent a significant proportion of hospitality industry income; however, little research has been conducted into the factors that influence food purchasing behaviour. This study employed a sequential mixed method research design to identify the factors that influence food choice among 18-30-year-old females. The study is important because this age group has considerable spending power and has been found to have different consumption priorities to their male counterparts. While confirming the importance of previous studies, this study found that physical health, time, marketing, price, and the quality of food are major factors of influence in the purchasing and consumption decision.