Can health beliefs help in explaining attendance to follow-up care? The Swiss Childhood Cancer Survivor Study

Objective: Improved treatment has increased the survival of childhood cancer patients in recent decades, but follow-up care is recommended to detect and treat late effects. We investigated relationships between health beliefs and follow-up attendance in adult childhood cancer survivors. Methods: Childhood cancer survivors aged younger than 16 years when diagnosed between 1976 and 2003, who had survived for more than 5 years and were currently aged 201 years, received a postal questionnaire. We asked survivors whether they attended follow-up in the past year. Concepts from the Health Belief Model (perceived susceptibility and severity of future late effects, potential benefits and barriers to follow-up, general health value and cues to action) were assessed. Medical information was extracted from the Swiss Childhood Cancer Registry. Results: Of 1075 survivors (response rate 72.3%), 250 (23.3%) still attended regular followup care. In unadjusted analyses, all health belief concepts were significantly associated with follow-up (po0.05). Adjusting for other health beliefs, demographic, and medical variables, only barriers (OR50.59; 95%CI: 0.43–0.82) remained significant. Younger survivors, those with lower educational background, diagnosed at an older age, treated with chemotherapy, radiotherapy, or bone marrow transplantation and with a relapse were more likely to attend follow-up care. Conclusions: Our study showed that more survivors at high risk of cancer- and treatmentrelated late effects attend follow-up care in Switzerland. Patient-perceived barriers hinder attendance even after accounting for medical variables. Information about the potential effectiveness and value of follow-up needs to be available to increase the attendance among childhood cancer survivors.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Maternal mental health in the first 3-week postpartum: the impact of caregiver support and the subjective experience of childbirth - a longitudinal path model

Acute stress reactions (ASR) and postpartum depressive symptoms (PDS) are frequent after childbirth. The present study addresses the change and overlap of ASR and PDS from the 1- to 3-week postpartum and examines the interplay of caregiver support and subjective birth experience with regard to the development of ASR/PDS within a longitudinal path model.

Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention

This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.

Towards a new model of care: integrating mental health, substance use, and somatic care

Although research and clinical interventions for patients with dual disorders have been described since as early as the 1980s, the day-to-day treatment of these patients remains problematic and challenging in many countries. Throughout this book, many approaches and possible pathways have been outlined. Based upon these experiences, some key points can be extracted in order to guide to future developments. (1) New diagnostic approaches are warranted when dealing with patients who have multiple problems, given the limitations of the current categorical systems. (2) Greater emphasis should be placed on secondary prevention and early intervention for children and adolescents at an increased risk of later-life dual disorders. (3) Mental, addiction, and somatic care systems can be integrated, adopting a patient-focused approach to care delivery. (4) Recovery should be taken into consideration when defining treatment intervention and outcome goals. (5) It is important to reduce societal risk factors, such as poverty and early childhood adversity. (6) More resources are needed to provide adequate mental health care in the various countries. The development of European guidance initiatives would provide benefits in many of these areas, making it possible to ensure a more harmonized standard of care for patients with dual disorders.