Patient versus general practitioner perception of problems with treatment adherence in type 2 diabetes: from adherence to concordance.

OBJECTIVES: To determine the prevalence of problems with treatment adherence among type-2 diabetic patients with regards to medication, dietary advice, and physical activity; to identify the associated clinical and psychosocial factors; and to investigate the degree of agreement between patient-perceived and GP-perceived adherence. METHODS: Consecutive patients were solicited during visits to 39 GPs. In total, 521 patients self-reported on treatment adherence, anxiety and depression, and disease perception. The GPs reported clinical and laboratory data and patients' adherence. A multivariate analysis identified the factors associated with adherence problems. RESULTS: Problems of adherence to medication, dietary advice, and physical activity recommendations were reported by 17%, 62%, and 47% of the patients, respectively. Six independent factors were found associated with adherence problems: young age, body-mass index (BMI) > 30 kg/m(2), glycosylated haemoglobin (HbA(1c)) > 8%, single life, depression, and perception of medication as a constraint. Agreement between patients' and GPs' assessments of treatment problems reached 70%. CONCLUSION: In type 2 diabetes, problems with dietary advice or physical activity are far more frequent than problems with medication, and not all physicians are fully aware of patients' problems. More active listening and shared decision-making should enhance adherence and improve outcomes.

Health-related quality of life in survivors of childhood cancer: the role of chronic health problems.

INTRODUCTION: The influence of specific health problems on health-related quality of life (HRQoL) in childhood cancer survivors is unknown. We compared HRQoL between survivors of childhood cancer and their siblings, determined factors associated with HRQoL, and investigated the influence of chronic health problems on HRQoL. METHODS: Within the Swiss Childhood Cancer Survivor Study, we sent a questionnaire to all survivors (≥16 years) registered in the Swiss Childhood Cancer Registry, who survived >5 years and were diagnosed 1976-2005 aged <16 years. Siblings received similar questionnaires. We assessed HRQoL using Short Form-36 (SF-36). Health problems from a standard questionnaire were classified into overweight, vision impairment, hearing, memory, digestive, musculoskeletal or neurological, and thyroid problems. RESULTS: The sample included 1,593 survivors and 695 siblings. Survivors scored significantly lower than siblings in physical function, role limitation, general health, and the Physical Component Summary (PCS). Lower score in PCS was associated with a diagnosis of central nervous system tumor, retinoblastoma or bone tumor, having had surgery, cranio-spinal irradiation, or bone marrow transplantation. Lower score in Mental Component Summary was associated with older age. All health problems decreased HRQoL in all scales. Most affected were survivors reporting memory problems and musculoskeletal or neurological problems. Health problems had the biggest impact on physical functioning, general health, and energy and vitality. CONCLUSIONS: In this study, we showed the negative impact of specific chronic health problems on survivors' HRQoL. IMPLICATIONS FOR CANCER SURVIVORS: Therapeutic preventive measures, risk-targeted follow-up, and interventions might help decrease health problems and, consequently, improve survivors' quality of life.

Parental post-traumatic reactions after premature birth: implications for sleeping and eating problems in the infant.

BACKGROUND: Progress in perinatal medicine has made it possible to increase the survival of very or extremely low birthweight infants. Developmental outcomes of surviving preterm infants have been analysed at the paediatric, neurological, cognitive, and behavioural levels, and a series of perinatal and environmental risk factors have been identified. The threat to the child's survival and invasive medical procedures can be very traumatic for the parents. Few empirical reports have considered post-traumatic stress reactions of the parents as a possible variable affecting a child's outcome. Some studies have described sleeping and eating problems as related to prematurity; these problems are especially critical for the parents. OBJECTIVE: To examine the effects of post-traumatic reactions of the parents on sleeping and eating problems of the children. DESIGN: Fifty families with a premature infant (25-33 gestation weeks) and a control group of 25 families with a full term infant participated in the study. Perinatal risks were evaluated during the hospital stay. Mothers and fathers were interviewed when their children were 18 months old about the child's problems and filled in a perinatal post-traumatic stress disorder questionnaire (PPQ). RESULTS: The severity of the perinatal risks only partly predicts a child's problems. Independently of the perinatal risks, the intensity of the post-traumatic reactions of the parents is an important predictor of these problems. CONCLUSIONS: These findings suggest that the parental response to premature birth mediates the risks of later adverse outcomes. Preventive intervention should be promoted.

Computational problems in perfect phylogeny haplotyping: typing without calling the allele.

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics, and is a variant of the perfect phylogeny haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is "full" genotypes, here we assume less informative input, and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time, by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by that tree they map onto, and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes, and present a linear algorithm for identifying those genotypes.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Collaboration entre médecin de famille et psychiatre pour les problèmes de santé mentale [Family physicians and psychiatrists' collaborative care for mental health problems].

The burden of disease linked to mental disorders represents more than one-fifth of years lived with disability in the world. Less than half of people suffering from mental disorders are adequately treated. Three quarter of those who receive treatment are followed by primary care. Collaborative care aims to increase the efficiency of direct general practitioner's treatment. Main components are sustainable and individualized consultation-liaison relationship (1/2 day of psychiatrist by 15 days for 10-15 general practitioners), and support of a clinical case manager for complex situations. Collaboration is bidirectional: early or crisis access to specialist care and long-term followup by general practitioner. This model is a challenge for the doctor-patient dual relationship and requires incentives in a public health perspective.

Prise en charge des patients avec des problèmes d'alcool: quelle efficacité? Quelques éléments de réponse à partir d'une revue de la littérature [Management of patients with alcohol problems: how effective? Several response factors based on a review of the literature]

What do we know about the effectiveness of various treatments of alcoholism? This review of literature shows that lack--or weaknesses--of published studies make it impossible to draw definite conclusions. Rigorous controlled studies show high rates of spontaneous remission and important uncertainties about specialised treatments of alcoholism. However, except for severe dependence that may well require a different approach, brief interventions conducted by non-specialists have proved highly effective for at-risk alcohol drinkers: based on minimal medical advice, they increase the chances of lowering alcohol consumption. General practitioners may thus represent on important link in the therapeutic chain.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.