Providing psychological services for people with diabetes

Previous research and service development guidelines have highlighted the importance of psychological issues in diabetes care, and both people with diabetes and diabetes professionals recognise the need for specialist psychological input. This article outlines the development of a service delivery model for psychological services in diabetes care, based on a patient needs assessment and the advice of diabetes professionals. This involved an assessment of the psychological needs of people with diabetes within an urban Health Trust in Northern Ireland, and the collation of the views of local diabetes professionals. Questionnaires to assess for depression, anxiety, binge eating behaviour and diabetes-specific worries were completed by 300 people with diabetes. The participants were accessed through both primary and secondary care diabetes teams. As expected, a high level of psychological distress relative to population norms was illustrated by the patient needs assessment. Particularly high levels of binge eating behaviour were reported, and levels of distress were higher for community-managed patients than for hospital-managed patients. The diabetes professionals unanimously agreed that there is a need for specialist psychological input and contributed to the service delivery model which is outlined in this article.

Risk adjustment for hospital use using social security data: cross sectional small area analysis

Objectives: To identify demographic and socioeconomic determinants of need for acute hospital treatment at small area level. To establish whether there is a relation between poverty and use of inpatient services. To devise a risk adjustment formula for distributing public funds for hospital services using, as far as possible, variables that can be updated between censuses. Design: Cross sectional analysis. Spatial interactive modelling was used to quantify the proximity of the population to health service facilities. Two stage weighted least squares regression was used to model use against supply of hospital and community services and a wide range of potential needs drivers including health, socioeconomic census variables, uptake of income support and family credit, and religious denomination. Setting: Northern Ireland. Main outcome measure: Intensity of use of inpatient services. Results: After endogeneity of supply and use was taken into account, a statistical model was produced that predicted use based on five variables: income support, family credit, elderly people living alone, all ages standardised mortality ratio, and low birth weight. The main effect of the formula produced is to move resources from urban to rural areas. Conclusions: This work has produced a population risk adjustment formula for acute hospital treatment in which four of the five variables can be updated annually rather than relying on census derived data. Inclusion of the social security data makes a substantial difference to the model and to the results produced by the formula.