Jen Kruger

Substantial reductions in the number of diabetic ketoacidosis and severe hypoglycaemia episodes requiring emergency treatment lead to reduced costs after structured education in adults with Type 1 diabetes.

To determine the impact of structured education promoting flexible intensive insulin therapy on rates of diabetic ketoacidosis, and the costs associated with emergency treatment for severe hypoglycaemia and ketoacidosis in adults with Type 1 diabetes.

A theory-based online health behaviour intervention for new university students (U@Uni): results from a randomised controlled trial

BackgroundToo few young people engage in behaviours that reduce the risk of morbidity and premature mortality, such as eating healthily, being physically active, drinking sensibly and not smoking. This study sought to assess the efficacy and cost-effectiveness of a theory-based online health behaviour intervention (based on self-affirmation theory, the Theory of Planned Behaviour and implementation intentions) targeting these behaviours in new university students, in comparison to a measurement-only control.MethodsTwo-weeks before starting university all incoming undergraduates at the University of Sheffield were invited to take part in a study of new students’ health behaviour. A randomised controlled design, with a baseline questionnaire, and two follow-ups (1 and 6 months after starting university), was used to evaluate the intervention. Primary outcomes were measures of the four health behaviours targeted by the intervention at 6-month follow-up, i.e., portions of fruit and vegetables, metabolic equivalent of tasks (physical activity), units of alcohol, and smoking status.ResultsThe study recruited 1,445 students (intervention n = 736, control n = 709, 58% female, Mean age = 18.9 years), of whom 1,107 completed at least one follow-up (23% attrition). The intervention had a statistically significant effect on one primary outcome, smoking status at 6-month follow-up, with fewer smokers in the intervention arm (8.7%) than in the control arm (13.0%; Odds ratio = 1.92, p = .010). There were no significant intervention effects on the other primary outcomes (physical activity, alcohol or fruit and vegetable consumption) at 6-month follow-up.ConclusionsThe results of the RCT indicate that the online health behaviour intervention reduced smoking rates, but it had little effect on fruit and vegetable intake, physical activity or alcohol consumption, during the first six months at university. However, engagement with the intervention was low. Further research is needed before strong conclusions can be made regarding the likely effectiveness of the intervention to promote health lifestyle habits in new university students.Trial registrationCurrent Controlled Trials, ISRCTN67684181.

The cost-effectiveness of the Dose Adjustment for Normal Eating (DAFNE) structured education programme: an update using the Sheffield Type 1 Diabetes policy model

To estimate the cost‐effectiveness of training in flexible intensive insulin therapy [as provided in the Dose Adjustment for Normal Eating (DAFNE) structured education programme] compared with no training for adults with Type 1 diabetes mellitus in the UK using the Sheffield Type 1 Diabetes Policy Model.

A theory-based online health behavior intervention for new university students: study protocol

BackgroundToo few young people engage in behaviors that reduce the risk of morbidity and premature mortality, such as eating healthily, being physically active, drinking sensibly and not smoking. The present research developed an online intervention to target these health behaviors during the significant life transition from school to university when health beliefs and behaviors may be more open to change. This paper describes the intervention and the proposed approach to its evaluation.Methods/designPotential participants (all undergraduates about to enter the University of Sheffield) will be emailed an online questionnaire two weeks before starting university. On completion of the questionnaire, respondents will be randomly assigned to receive either an online health behavior intervention (U@Uni) or a control condition. The intervention employs three behavior change techniques (self-affirmation, theory-based messages, and implementation intentions) to target four heath behaviors (alcohol consumption, physical activity, fruit and vegetable intake, and smoking). Subsequently, all participants will be emailed follow-up questionnaires approximately one and six months after starting university. The questionnaires will assess the four targeted behaviors and associated cognitions (e.g., intentions, self-efficacy) as well as socio-demographic variables, health status, Body Mass Index (BMI), health service use and recreational drug use. A sub-sample of participants will provide a sample of hair to assess changes in biochemical markers of health behavior. A health economic evaluation of the cost effectiveness of the intervention will also be conducted.DiscussionThe findings will provide evidence on the effectiveness of online interventions as well as the potential for intervening during significant life transitions, such as the move from school to university. If successful, the intervention could be employed at other universities to promote healthy behaviors among new undergraduates.Trial registrationCurrent Controlled Trials, ISRCTN67684181.

Assessing the cost-effectiveness of type 1 diabetes interventions: the Sheffield type 1 diabetes policy model.

To build a flexible and comprehensive long‐term Type 1 diabetes mellitus model incorporating the most up‐to‐date methodologies to allow a number of cost‐effectiveness evaluations.

The Impact of Diabetes-Related Complications on Preference-Based Measures of Health-Related Quality of Life in Adults with Type I Diabetes

Introduction. This study estimates health-related quality of life (HRQoL) or utility decrements associated with type 1 diabetes mellitus (T1DM) using data from a UK research program on the Dose Adjustment For Normal Eating (DAFNE) education program. Methods. A wide range of data was collected from 2341 individuals who undertook a DAFNE course in 2009–2012, at baseline and for 2 subsequent years. We use fixed- and random-effects linear models to generate utility estimates for T1DM using different instruments: EQ-5D, SF-6D, and EQ-VAS. We show models with and without controls for HbA1c and depression, which may be endogenous (if, for example, there is reverse causality in operation). Results. We find strong evidence of an unobserved individual effect, suggesting the superiority of the fixed-effects model. Depression shows the greatest decrement across all the models in the preferred fixed-effects model. The fixed-effects EQ-5D model also finds a significant decrement from retinopathy, body mass index, and HbA1c (%). Estimating a decrement using the fixed-effects model is not possible for some conditions where there are few new cases. In the random-effects model, diabetic foot disease shows substantial utility decrements, yet these are not significant in the fixed-effects models. Conclusion. Utility decrements have been calculated for a wide variety of health states in T1DM that can be used in economic analyses. However, despite the large data set, the low incidence of several complications leads to uncertainty in calculating the utility weights. Depression and diabetic foot disease result in a substantial loss in HRQoL for patients with T1DM. HbA1c (%) appears to have an independent negative impact on HRQoL, although concerns remain regarding the potential endogeneity of this variable.

The cost-effectiveness of a theory-based online health behaviour intervention for new university students: an economic evaluation

BackgroundToo many young people engage in unhealthy behaviours such as eating unhealthily, being physically inactive, binge drinking and smoking. This study aimed to estimate the short-term and long-term cost-effectiveness of a theory-based online health behaviour intervention (“U@Uni”) in comparison with control in young people starting university.MethodsA costing analysis was conducted to estimate the full cost of U@Uni and the cost of U@Uni roll-out. The short-term cost-effectiveness of U@Uni was estimated using statistical analysis of 6-month cost and health-related quality of life data from the U@Uni randomised controlled trial. An economic modelling analysis combined evidence from the trial with published evidence of the effect of health behaviours on mortality risk and general population data on health behaviours, to estimate the lifetime cost-effectiveness of U@Uni in terms of incremental cost per QALY. Costs and effects were discounted at 1.5% per annum. A full probabilistic sensitivity analysis was conducted to account for uncertainty in model inputs and provide an estimate of the value of information for groups of important parameters.ResultsTo implement U@Uni for the randomised controlled trial was estimated to cost £292 per participant, whereas roll-out to another university was estimated to cost £19.71, both giving a QALY gain of 0.0128 per participant. The short-term (6-month) analysis suggested that U@Uni would not be cost-effective at a willingness-to-pay threshold of £20,000 per QALY (incremental cost per QALY gained = £243,926). When a lifetime horizon was adopted the results suggest that the full implementation of U@Uni is unlikely to be cost-effective, whereas the roll-out of U@Uni to another university is extremely likely to be cost-effective. The value of information analysis suggests that the most important drivers of decision uncertainty are uncertainties in the effect of U@Uni on health behaviours.ConclusionsThe study provides the first estimate of the costs and cost-effectiveness of an online health behaviour intervention targeted at new university students. The results suggest that the roll-out, but not the full implementation, of U@Uni would be a cost-effective decision for the UK Department of Health, given a lifetime perspective and a willingness-to pay threshold of £20,000 per QALY.Trial registrationCurrent Controlled Trials ISRCTN67684181.

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes

Background. Health economic modeling has paid limited attention to the effects that patients’ psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. Methods. Multiple linear regressions were used to investigate relationships between patients’ psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. Results. The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. Limitations. The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. Conclusions. By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies.