Utilising a hermeneutic phenomenological approach twenty women’s experiences of breast cancer were explored through semi-structured interviews at three time points during their healthcare trajectory from recent diagnosis to early follow-up. Phenomenological analysis guided by van Manen’s (1990) principles revealed numerous multifaceted themes some of which were time limited while others spanned the data collection period. Use was made of an adapted life grid approach in order to enhance the implicit meanings to be elicited through interpretation of text. Central themes depicting the medicalisation of breast cancer, perceptions and management of the body and participants’ emotional journey were uncovered. Breast cancer was found to represent a biographical disruption that had a long-term impact on a woman’s body, self, identity and sense of embodiment. Changes to the body, due to breast cancer treatment, and an altered way of being in the world, elicited disruption to the body-self relationship, a separation that was reinforced by the healthcare system. Participants were found to adopt a variety of coping strategies to manage ongoing change and the stress elicited by experiencing breast cancer as a chronic illness. Avoidance, information management, conscious passivity in treatment decision-making and positive cognitive restructuring are examples of such mechanisms. However, women’s experiences of each theme identified and the emergence and maintenance of these themes varied according to women’s biography, diagnosis and prescribed treatment regime, cancer schema, and social support. Accordingly, models of care must address women’s individual experiences and recognise their changing needs throughout the year post diagnosis.

BACKGROUND: Tobacco use remains the leading cause of morbidity and mortality in the US. Effective tobacco cessation aids are widely available, yet underutilized. Tobacco cessation brief interventions (BIs) increase quit rates. However, BI training has focused on conventional medical providers, overlooking other health practitioners with regular contact with tobacco users. The 2007 National Health Interview Survey found that approximately 20% of those who use provider-based complementary and alternative medicine (CAM) are tobacco users. Thus, CAM practitioners potentially represent a large, untapped community resource for promoting tobacco cessation and use of effective cessation aids. Existing BI training is not well suited for CAM practitioners' background and practice patterns, because it assumes a conventional biomedical foundation of knowledge and philosophical approaches to health, healing and the patient-practitioner relationship. There is a pressing need to develop and test the effectiveness of BI training that is both grounded in Public Health Service (PHS) Guidelines for tobacco dependence treatment and that is relevant and appropriate for CAM practitioners. METHODS/DESIGN: The CAM Reach (CAMR) intervention is a tobacco cessation BI training and office system intervention tailored specifically for chiropractors, acupuncturists and massage therapists. The CAMR study utilizes a single group one-way crossover design to examine the CAMR intervention's impact on CAM practitioners' tobacco-related practice behaviors. Primary outcomes included CAM practitioners' self-reported conduct of tobacco use screening and BIs. Secondary outcomes include tobacco using patients' readiness to quit, quit attempts, use of guideline-based treatments, and quit rates and also non-tobacco-using patients' actions to help someone else quit. DISCUSSION: CAM practitioners provide care to significant numbers of tobacco users. Their practice patterns and philosophical approaches to health and healing are well suited for providing BIs. The CAMR study is examining the impact of the CAMR intervention on practitioners' tobacco-related practice behaviors, CAM patient behaviors, and documenting factors important to the conduct of practice-based research in real-world CAM practices.

Knee osteoarthritis (OA) is the commonest form of lower limb OA with a lifetime risk of over 40%. It is a disease characterised by symptoms such as pain and loss of function. In addition there are typical structural features on both radiographs and MRI. Knee OA represents a spectrum of disease, ranging from early preclinical cartilage change to established full thickness disease. Anteromedial knee OA is a particular phenotype of knee OA where disease is confined to the medial compartment. Whilst end stage arthritis is treated reliably with joint arthroplasty, those with early stage disease are treated with a variety of non- surgical interventions with varying success. This thesis is concerned with understanding the disease of patients that have early radiographic changes but symptoms not controlled by conservative measures. Up to 150 of these patients a year present to the Nuffield Orthopaedic Centre, Oxford. They have been described as being in the “Treatment Gap”. A series of validation studies were performed to determine the optimal method for diagnosing cartilage defects within the knee. The three commonest diagnostic methods were examined for their validity. Arthroscopic assessments of cartilage lesions demonstrated a moderate level of intra and inter observer reliability. In contrast, radiographs and MRI demonstrated high levels of reliability. When using MRI as a criterion standard, both radiographs and arthroscopic assessment were found to have poor accuracy. Based on the work in this thesis a formal definition of the cartilage changes exhibited in early knee OA was proposed. A cross sectional cohort of 100 patients with the symptoms and radiological features of early knee OA were identified. Their pain and function profile was compared to two comparison groups of patients at the end stage of knee OA (defined by the need for partial or total arthroplasty). In up to 78% of individual cases those with early OA had pain and function profiles as bad as those with end stage disease. The cross sectional symptoms of early knee OA demonstrate a marked discordance with their mild radiographic changes. The same cohort was extended to 125 patients. They were followed over one year with monthly PROM assessments to determine how symptoms change over time. 43% of patients experience a clinical improvement over 12 months, 31% experience a clinical deterioration and 26% remain unchanged. The range in OKS variation over 12 months was on average 12 points, with clinically relevant variation occurring on 45% of monthly measurements. Patients with early knee OA can expect to experience considerable variation in their symptoms over 12 months and this must be considered when planning interventions. A number of patients with early knee OA were noticed to demonstrate medial meniscal extrusion. Using data from the Osteo Arthritis Initiative (OAI) a nested case control study was designed to determine how the presence of meniscal extrusion in an otherwise normal knee affects the risk of developing knee OA over the next 48 months. This demonstrated an Odds Ratio of 3.5, suggesting that meniscal extrusion is a considerable risk factor for the development of OA. The presence of a knee injury or operative intervention to the index meniscus was shown to increase this risk. Many phenotypes of OA are known to demonstrate familial aggregation. In an attempt to determine where the earliest structural changes occur in medial compartment knee OA, a cohort of patients selected only for their family history of the disease were developed. This cohort was compared to spouse controls for the presence of knee OA, as well as meniscal extrusion and long leg alignment. In addition, a functional analysis of their cartilage was performed. This cohort was not shown to be at increased risk of disease compared to controls. Discussion of the possible reasons for this finding is presented. Early knee osteoarthritis is a considerable clinical problem. This thesis has aided the understanding of the condition by firstly defining the radiological description of these patients. Secondly, their cross sectional and longitudinal symptom profile have been described for the first time. In addition, the presence of an extruded meniscus has been demonstrated as a substantial risk factor for the disease. Finally, family history has not been demonstrated as a risk factor for the disease within the limits of the study described here. Future work has been proposed.

This thesis considers why some crimes persist beyond the moment of newsworthiness and how they are able to transcend this period of intense reporting to become a feature of popular memory. The central argument is that the popular memory of a crime is built up over time through a synthesis of public discourses, which are predominantly developed in news reporting, people s everyday experience and the normative social frameworks of everyday life. A temporally sensitive analysis of two case studies, the murder of James Bulger and the murder of Sarah Payne, tests this hypothesis by exploring the connections and disconnections between the ongoing reporting of these crimes and the remembering of them. The study finds that the personal past and public discourse intertwine in remembered accounts of these crimes and considers that this is evidence of the ways audiences utilise crime news as an imaginative resource for understanding crime and criminality more broadly. It can thus be said that audiences use the news to frame, but not define their understandings of the world around us.

Puberty is the process of becoming sexually mature and it has fundamental somatic and psychosocial implications. The focus of this dissertation was the short and long term developmental significance, concerning both soma et psyche, of female pubertal timing. Four studies were designed to accomplish these aims. Six samples of different ages from different countries and from different time points, comprising several thousand females some of which were followed longitudinally, were used. Age at menarche was used as the primary measure of pubertal maturation. The first main aim of this dissertation was to explore the mechanisms that might explain the well-established link between female pubertal timing and problem behavior, and to identify contextual conditions at which associations are stronger or weaker. Existing explanations are unsatisfactory and little is known about conditions that might affect the strength of the associations. In Paper I, we tested and confirmed a peer socialization hypothesis as a satisfactory explanation for the link between early puberty and problematic adjustment. In short, this hypothesis posits that early developing girls associate with older peers and boyfriends because they feel more mature than their same age peers, and through these peers and boyfriends the early developed girls are channeled into more socially advanced behaviors, including normbreaking. This should be particularly true in contexts where heterosexual relationships are sanctioned and where there is an abundance of deviant youth. In Paper II, I used a biopsychosocial approach and studied pubertal timing along with self-perceptions of maturity and early romantic relationships. The findings revealed that early puberty had very different implications depending on the psychological and social contexts in which it was embedded. For instance, when early puberty was coupled with feeling mature and having early romantic relationships, it was associated with adjustment problems. When early puberty was coupled with neither, it was not linked to particularly high levels of problem behavior. In stark contrast to the vast literature on the role of female pubertal timing in adolescence, the literature on long-term implications is remarkably limited. For this reason, the second main aim of this dissertation was to study the adult implications of female pubertal timing. In Papers III and IV, we examined long term implications of pubertal timing, particularly as it relates to somatic development. The findings suggested that pubertal timing does have future implications for women’s body perception and composition, with early developing females having higher body mass indexes in adulthood, but only under certain circumstances. The findings of this dissertation help further understanding of the soma et psyche implications of female pubertal timing. They indicate that pubertal timing has concurrent and future implications. It seems, however, that timing is not everything. The developmental significance of female pubertal timing appears to be very different under different contextual conditions. Thus, it is only when girls’ psychological and social contexts are considered that fruitful predictions can be made. As such, the findings have important implications for prevention, policy, and practice.

The overall aim of this study was to investigate the association between family structure in childhood and mental health problems in young adulthood. A prospective cohort study was conducted with 481,777 individuals with complete follow-up information, which was obtained from national registers in Sweden. Individuals who were living with only one biological parent at age 17 were compared with those who grew up with two parents with regard to retrieval of prescribed psychotropic drugs at age 35. The association was examined by Cox regression analyses with equal survival time for all individuals included in the analyses. The results demonstrated a higher risk for retrieval of psychotropic medicines among the individuals who grew up with only one parent, with hazard ratio of 1,21 (95%CI: 1,19-1,23). The multivariate analyses showed that a part of the association was explained by familial and individual factors, namely parents’ country of origin, area of residence, parents’ and the individual’s educational attainment, receipt of social benefits and parents’ history of psychiatric disorder. The results indicated that the increased risk of mental health problems among individuals who grew up with only one parent might be accounted for by various psychological, social and economic factors associated to parental separation.

Microbiome plays an important role in human health. The analysis of association between microbiome and clinical outcome has become an active direction in biostatistics research. Testing the microbiome effect on clinical phenotypes directly using operational taxonomic unit abundance data is a challenging problem due to the high dimensionality, non-normality and phylogenetic structure of the data. Most of the studies only focus on describing the change of microbe population that occur in patients who have the specific clinical condition. Instead, a statistical strategy utilizing distance-based or similarity-based non-parametric testing, in which a distance or similarity measure is defined between any two microbiome samples, is developed to assess association between microbiome composition and outcomes of interest. Despite the improvements, this test is still not easily interpretable and not able to adjust for potential covariates. A novel approach, kernel-based semi-parametric regression framework, is applied in evaluating the association while controlling the covariates. The framework utilizes a kernel function which is a measure of similarity between samples' microbiome compositions and characterizes the relationship between the microbiome and the outcome of interest. This kernel-based regression model, however, cannot be applied in longitudinal studies since it could not model the correlation between the repeated measurements. We proposed microbiome association exact tests (MAETs) in linear mixed model can deal with longitudinal microbiome data. MAETs can test not only the effect of overall microbiome but also the effect from specific cluster of the OTUs while controlling for others by introducing more random effects in the model. The current methods for multiple variance component testing are based on either asymptotic distribution or parametric bootstrap which require large sample size or high computational cost. The exact (R)LRT tests, an computational efficient and powerful testing methodology, was derived by Crainiceanu. Since the exact (R)LRT can only be used in testing one variance component, we proposed an approach that combines the recent development of exact (R)LRT and a strategy for simplifying linear mixed model with multiple variance components to a single case. The Monte Carlo simulation studies present correctly controlled type I error and provided superior power in testing association between microbiome and outcomes in longitudinal studies. Finally, the MAETs were applied to longitudinal pulmonary microbiome datasets to demonstrate that microbiome composition is associated with lung function and immunological outcomes. We also successfully found two interesting genera Prevotella and Veillonella which are associated with forced vital capacity.

Thesis (MCom)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: A multi-state model is a way of describing a process in which a subject moves through a series of states in continuous time. The series of states might be the measurement of a disease for example in state 1 we might have subjects that are free from disease, in state 2 we might have subjects that have a disease but the disease is mild, in state 3 we might have subjects having a severe disease and in last state 4 we have those that die because of the disease. So Markov models estimates the transition probabilities and transition intensity rates that describe the movement of subjects between these states. The transition might be for example a particular subject or patient might be slightly sick at age 30 but after 5 years he or she might be worse. So Markov model will estimate what probability will be for that patient for moving from state 2 to state 3. Markov multi-state models were studied in this thesis with the view of assessing the Markov models assumptions such as homogeneity of the transition rates through time, homogeneity of the transition rates across the subject population and Markov property or assumption. The assessments of these assumptions were based on simulated panel or longitudinal dataset which was simulated using the R package named msm package developed by Christopher Jackson (2014). The R code that was written using this package is attached as appendix. Longitudinal dataset consists of repeated measurements of the state of a subject and the time between observations. The period of time with observations in longitudinal dataset is being made on subject at regular or irregular time intervals until the subject dies then the study ends. / AFRIKAANSE OPSOMMING: ’n Meertoestandmodel is ’n manier om ’n proses te beskryf waarin ’n subjek in ’n ononderbroke tydperk deur verskeie toestande beweeg. Die verskillende toestande kan byvoorbeeld vir die meting van siekte gebruik word, waar toestand 1 uit gesonde subjekte bestaan, toestand 2 uit subjekte wat siek is, dog slegs matig, toestand 3 uit subjekte wat ernstig siek is, en toestand 4 uit subjekte wat aan die siekte sterf. ’n Markov-model raam die oorgangswaarskynlikhede en -intensiteit wat die subjekte se vordering deur hierdie toestande beskryf. Die oorgang is byvoorbeeld wanneer ’n bepaalde subjek of pasiënt op 30-jarige ouderdom net lig aangetas is, maar na vyf jaar veel ernstiger siek is. Die Markov-model raam dus die waarskynlikheid dat so ’n pasiënt van toestand 2 tot toestand 3 sal vorder. Hierdie tesis het ondersoek ingestel na Markov-meertoestandmodelle ten einde die aannames van die modelle, soos die homogeniteit van oorgangstempo’s oor tyd, die homogeniteit van oorgangstempo’s oor die subjekpopulasie en tipiese Markov-eienskappe, te beoordeel. Die beoordeling van hierdie aannames was gegrond op ’n gesimuleerde paneel of longitudinale datastel wat met behulp van Christopher Jackson (2014) se R-pakket genaamd msm gesimuleer is. Die R-kode wat met behulp van hierdie pakket geskryf is, word as bylae aangeheg. Die longitudinale datastel bestaan uit herhaalde metings van die toestand waarin ’n subjek verkeer en die tydsverloop tussen waarnemings. Waarnemings van die longitudinale datastel word met gereelde of ongereelde tussenposes onderneem totdat die subjek sterf, wanneer die studie dan ook ten einde loop.

A prospective, longitudinal and observational study, using a non-clinical population of mother-child dyads was conducted to evaluate the contribution of family-environmental factors in predicting child food avoidance and feeding problems across early childhood. The contribution of maternal feeding practices, mealtime structure and interactional behaviour during mealtimes, were explored in predicting child food avoidance between 2 and 5 years, whilst also evaluating the role of maternal psychopathology and child temperament. This thesis also assessed the validity of maternal reports of child eating behaviour and feeding practices by obtaining independent observations of these constructs, and explored the longitudinal stability and continuity of both independent observations and maternal reports of child eating behaviour and maternal feeding practices. Concurrently and prospectively, observations of mothers eating with their child, displaying high sensitivity, low control, and more positive emotion and verbalisation during mealtimes predicted less avoidant child eating behaviour. Reports of mothers providing a healthy food-related home environment, encouraging balanced food intake, and involving their child in food planning, in addition to a less emotional child temperament, were also significant longitudinal predictors of less avoidant child eating behaviour. Maternal descriptions of their child s eating behaviour were validated by independent observations; however maternal descriptions of their own feeding practices were not. Child eating behaviour and maternal feeding practices were predominantly stable and continuous across early childhood, with the exception of child difficulty to feed and maternal pressure to eat which decreased between the ages of 3 and 4. This thesis demonstrates many interesting and novel findings but primarily through the utilisation of observational and longitudinal data it demonstrates the important causal contribution of family-environmental factors in the development of food avoidant eating behaviours during early childhood.

Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models. Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was proposed to handle uneven follow-up assessments or skipped visits, resulting in the interval censored data. Simulations were used to compare the performance of the proposed model with the traditional discrete time stationary Markov chain under different types of observation schemes. We applied these two methods to the well-known Nun study, a longitudinal study of 672 participants aged ≥ 75 years at baseline and followed longitudinally with up to ten cognitive assessments per participant. Secondly, we constructed a non-homogenous Markov model for this type of panel data. The baseline intensity was assumed to be Weibull distributed to accommodate the non-homogenous property. The proportional hazards method was used to incorporate risk factors into the transition intensities. Simulation studies showed that the Weibull assumption does not affect the accuracy of the parameter estimates for the risk factors. We applied our model to data from the BRAiNS study, a longitudinal cohort of 531 subjects each cognitively intact at baseline. Last, we presented a parametric method of fitting semi-Markov models based on Weibull transition intensities with interval censored cognitive data with death as a competing risk. We relaxed the Markov assumption and took interval censoring into account by integrating out all possible unobserved transitions. The proposed model also allowed for incorporating time-dependent covariates. We provided a goodness-of-fit assessment for the proposed model by the means of prevalence counts. To illustrate the methods, we applied our model to the BRAiNS study.