Preference-based measure of health-related quality of life and its determinants in sickle cell disease in Nigeria.

BACKGROUND: Health-related quality of life (HRQL) and economic burden are important issues for people with sickle cell disease (SCD) owing to better survival due to medical advances. Preference-based or utility information is necessary to make informed economic decisions on treatment and alternative therapies. This study aimed to assess preference-based measures of HRQL in sickle cell patients. METHODS AND FINDINGS: Data were collected from two SCD outpatient clinics in Ibadan, Nigeria. A standard algorithm was used to derive utility scores, and measure SF-6D from the SF-36. A multivariate regression model was used to assess predictors and their impact. A combination of socio-demographic, bio-physiological and psychosocial variables predicted utility score in people with SCD. Socio-demographic and bio-physiological factors explained 7.5% and 17.9% of the variance respectively, while psychosocial factors explained 4.9%. Women had lower utility scores with a small effect size (d = 0.17). Utility score increased with level of education but decreased with age, anxiety, frequency of pain episodes and number of co-morbidities. CONCLUSIONS: Utility score in SCD was low indicating a substantial impact of the disease on HRQL of patients and the value they place on their health state due to the limitations they experienced. Interventions should include both clinical and psychosocial approach to help in improving their quality of life of the patients.

Explainable statistical learning in public health for policy development: the case of real-world suicide data.

BACKGROUND: In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy. METHODS: Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and explanation of suicide. RESULTS: Multi-collinearity analysis was effective in reducing the indicator set of predictors by 30% and principal component analysis further reduced the set by 86%. Regression for prediction identified four significant indicator predictors of suicide behaviour (emergency hospital admissions for intentional self-harm, children leaving care, statutory homelessness and self-reported well-being/low happiness) and two main component predictors (relatedness dysfunction, and behavioural problems and mental illness). Regression for explanation identified significant moderation of a well-being predictor (low happiness) of suicide behaviour by a social factor (living alone), thereby supporting existing theory and providing insight beyond the results of regression for prediction. Two independent predictors capturing relatedness needs in social care service delivery were also identified. CONCLUSIONS: We demonstrate the effectiveness of regression techniques in the analysis of online public health data. Regression analysis for prediction and explanation can both be appropriate for public health data analysis for a better understanding of public health outcomes. It is therefore essential to clarify the aim of the analysis (prediction accuracy or theory development) as a basis for choosing the most appropriate model. We apply these techniques to the analysis of suicide data; however, we argue that the analysis presented in this study should be applied to datasets across public health in order to improve the quality of health policy recommendations.

Perceived Autonomy Support and Motivation in Young People: A Comparative Investigation of Physical Education and Leisure-Time in Four Countries.

Physical education focuses on the development of sports skills as well as fitness for health. In Central European countries there has been a shift in these focuses since the fall of Communism to follow internationally-recognised health-related physical activity recommendations, similar to Western European countries. In this study we investigated the extent to which motivation from school physical education transfers to leisure-time physical activity providing autonomy support by three social agents: school (physical education teachers), family and peers. Our study utilised the Aetological Approach (AA), Ecological Model (EM) and the Trans-Contextual Model (TCM) that consists of the Theory of Planned Behaviour (TPB) and the Self-Determination Theory (SDT) to explore how autonomous motivation is transferred between contexts (physical education, leisure-time and current behaviour). Nine-hundred and seventy-four students aged 11-18 (55% girls) participated in our study from four countries: Hungary, United Kingdom, Romania and Slovakia. A prospective research design was employed, and questionnaires were administered at three time points. Using 7-point Likert scales, attitude, usefulness, and affectiveness were measured. Furthermore, subjective norms and perceived behavioural control (PBC) were tested within TPB. Autonomous and controlling motivation were measured within SDT by administering the Behavioural Regulation in Exercise questionnaires (BREQ and BREQ-2). Finally, past physical activity, intention and current physical activity behaviours were tested. Results indicated that perceived autonomy support from family and friends predicted autonomous motivation towards leisure-time physical activity in all four countries. However, teachers' behaviour in some Eastern European countries did not predict this transfer. In general, in line with previous literature, boys reported more physical activity than girls. A strong influencing factor in the path model was that past behaviour predicted current behaviour, and according to that factor, boys reported being more active than girls.Boys also perceived more support from PE teachers than girls which was likely to have influenced their autonomous motivation in PE, which in turn transferred to leisure time. We discuss these results in the context of theories exploring the role of motivation and social environment on children's choices related to physical activity. In conclusion, we suggest providing more autonomy support, especially by schools, for the enhancement of autonomous motivation of young people to promote their leisure time physical activity.

A systematic review of the application of Wilson and Cleary health-related quality of life model in chronic diseases.

BACKGROUND: A conceptual model approach to clarify the elements of health-related quality of life (HRQL), their determinants and causal pathways is needed to aid researchers, health practitioners and policy makers in their bid to improve HRQL outcomes in patients. The aim of this systematic review was to appraise empirical evidence on the performance of the Wilson and Cleary Model of HRQL. METHODS: We conducted a search of MEDLINE, Science Direct, PsyARTICLES and CINAHL databases to identify articles that used Wilson and Cleary model to examine HRQL in chronic diseases. A narrative synthesis was employed in the review of the articles. RESULTS: Evidence supports linkages between adjacent concepts and between non-adjacent concepts of the Wilson and Cleary model indicating that in practice there is a need to examine relationships among constructs - or to consider interventions in terms of - both with and without mediators. Symptoms status has the highest magnitude of relative impact on health-related quality of life. CONCLUSION: The Wilson and Cleary model demonstrated good features suitable for evaluating health-related quality of life in chronic diseases.