The cost-effectiveness of weight management programmes in a postnatal population.

OBJECTIVES: The aim of the study was to estimate the cost-effectiveness of a weight management programme including elements of physical exercise and dietary restriction which are designed to help women lose excess weight gained during pregnancy in the vulnerable postnatal period and inhibit the development of behaviours which could lead to future excess weight gain and obesity. STUDY DESIGN: A mathematical model based on a regression equation predicting change in weight over a fifteen year postnatal period was developed. METHODS: The model included programme effectiveness and resource data based on a randomized controlled trial of a weight management programme implemented in a postnatal population in the United States. Utility and mortality data based on body mass index categories were also included. The model adopted a National Health Service (NHS) and personal social services (PSS) perspective, a lifetime time horizon and estimated the cost effectiveness of a weight management programme against a no change comparator in terms of an incremental cost-effectiveness ratio (ICER). RESULTS: The baseline results show that the difference in weight between women who received the weight management programme and women who received the control intervention was 3.02 kg at six months and 3.53 kg at fifteen years following childbirth. This results in an ICER of £7355 per quality adjusted life year (QALY) for women who were married at childbirth. CONCLUSION: The estimated ICER would suggest that such a weight management programme is cost-effective at a NICE threshold of £20,000 per QALY. However significant structural and evidence based uncertainty is present in the analysis.

Cost-effectiveness analysis of different embryo transfer strategies in England.

OBJECTIVE: The objective of this study was to assess the cost-effectiveness of different embryo transfer strategies for a single cycle when two embryos are available, and taking the NHS cost perspective. DESIGN: Cost-effectiveness model. SETTING: Five in vitro fertilisation (IVF) centres in England between 2003/04 and 2004/05. POPULATION: Women with two embryos available for transfer in three age groups (<30, 30-35 and 36-39 years). METHODS: A decision analytic model was constructed using observational data collected from a sample of fertility centres in England. Costs and adverse outcomes are estimated up to 5 years after the birth. Incremental cost per live birth was calculated for different embryo transfer strategies and for three separate age groups: less than 30, 30-35 and 36-39 years. MAIN OUTCOME MEASURES: Premature birth, neonatal intensive care unit admissions and days, cerebral palsy and incremental cost-effectiveness ratios. RESULTS: Single fresh embryo transfer (SET) plus frozen single embryo transfer (fzSET) is the more costly in terms of IVF costs, but the lower rates of multiple births mean that in terms of total costs, it is less costly than double embryo transfer (DET). Adverse events increase when moving from SET to SET+fzSET to DET. The probability of SET+fzSET being cost-effective decreases with age. When SET is included in the analysis, SET+fzSET no longer becomes a cost-effective option at any threshold value for all age groups studied. CONCLUSIONS: The analyses show that the choice of embryo transfer strategy is a function of four factors: the age of the mother, the relevance of the SET option, the value placed on a live birth and the relative importance placed on adverse outcomes. For each patient group, the choice of strategy is a trade-off between the value placed on a live birth and cost.

Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-beta and glatiramer acetate for multiple sclerosis.

OBJECTIVES: To develop methods for performing expected value of perfect information (EVPI) analysis in computationally expensive models and to report on the developments on the health economics of interferon-beta and glatiramer acetate in the management of multiple sclerosis (MS) using this methodological framework. DATA SOURCES: Electronic databases and Internet resources, reference lists of relevant articles. REVIEW METHODS: A methodological framework was developed for undertaking EVPI analysis for complex models. The framework identifies conditions whereby EVPI may be calculated numerically, where the one-level algorithm sufficiently approximates the two-level algorithm, and whereby metamodelling techniques may accurately approximate the original simulation model. Metamodelling techniques, including linear regression, neural networks and Gaussian processes (GP), were systematically reviewed and critically appraised. Linear regression metamodelling, GP metamodelling and the one-level EVPI approximation were used to estimate partial EVPIs using the ScHARR MS cost-effectiveness model. RESULTS: The review of metamodelling approaches suggested that in general the simpler techniques such as linear regression may be easier to implement, as they require little specialist expertise although may provide only limited predictive accuracy. More complex methods such as Gaussian process metamodelling and neural networks tend to use less-restrictive assumptions concerning the relationship between the model inputs and net benefits, and therefore may permit greater accuracy in estimating EVPIs. Assuming independent treatment efficacy, the 'per patient' EVPI for all uncertainty parameters within the ScHARR MS model is 8855 British pounds. This leads to a population EVPI of 86,208,936 British pounds, which represents the upper estimate for the overall EVPI over 10 years. Assuming all treatment efficacies are perfectly correlated, the overall per patient EVPI is 4271 British pounds. This leads to a population EVPI of 41,581,273 British pounds, which represents the lower estimate for the overall EVPI over 10 years. The partial EVPI analysis, undertaken using both the linear regression metamodel and Gaussian process metamodel clearly, suggests that further research is indicated on the long-term impact of these therapies on disease progression, the proportion of patients dropping off therapy and the relationship between the EDSS, quality of life and costs of care. CONCLUSIONS: The applied methodology points towards using more sophisticated metamodelling approaches in order to obtain greater accuracy in EVPI estimation. Programming requirements, software availability and statistical accuracy should be considered when choosing between metamodelling techniques. Simpler, more accessible techniques are open to greater predictive error, whilst sophisticated methodologies may enhance accuracy within non-linear models, but are considerably more difficult to implement and may require specialist expertise. These techniques have been applied in only a limited number of cases hence their suitability for use in EVPI analysis has not yet been demonstrated. A number of areas requiring further research have been highlighted. Further clinical research is required concerning the relationship between the EDSS, costs of care and health outcomes, the rates at which patients drop off therapy and in particular the impact of disease-modifying therapies on the progression of MS. Further methodological research is indicated concerning the inclusion of epidemiological population parameters within the sensitivity analysis; the development of criteria for selecting a metamodelling approach; the application of metamodelling techniques within health economic models and in the specific application to EVI analyses; and the use of metamodelling for EVSI and ENBS analysis.

Gaussian process modeling in conjunction with individual patient simulation modeling: a case study describing the calculation of cost-effectiveness ratios for the treatment of established osteoporosis.

Individual patient-level models can simulate more complex disease processes than cohort-based approaches. However, large numbers of patients need to be simulated to reduce 1st-order uncertainty, increasing the computational time required and often resulting in the inability to perform extensive sensitivity analyses. A solution, employing Gaussian process techniques, is presented using a case study, evaluating the cost-effectiveness of a sample of treatments for established osteoporosis. The Gaussian process model accurately formulated a statistical relationship between the inputs to the individual patient model and its outputs. This model reduced the time required for future runs from 150 min to virtually-instantaneous, allowing probabilistic sensitivity analyses-to be undertaken. This reduction in computational time was achieved with minimal loss in accuracy. The authors believe that this case study demonstrates the value of this technique in handling 1st- and 2nd-order uncertainty in the context of health economic modeling, particularly when more widely used techniques are computationally expensive or are unable to accurately model patient histories.

[Modeling long-term results of basiliximab (SIMULECT) use in patients after kidney transplantation in Great Britain]. / Modelowanie dlugotrwalych skutków stosowania basiliximabu (SIMULECTU) u chorych po przeszczepieniu nerki w Wielkiej Brytanii.

Kidney graft survival prognosis could be performed in relation to the number of acute rejection episodes and their severity. The episodes of rejection could be modelled and based and the results obtained on this way could serve for prediction of a length of graft survival as well as for basiliximab treatment planning.

Is it feasible to plan secondary care services for coronary heart disease rationally? A quantified modelling approach for a UK Health Authority.

BACKGROUND: Coronary heart disease (CHD) is the major cause of mortality in the UK. This paper explores the difficulties facing health authorities in applying a rational and needs based approach to the planning of hospital based services and describes a simple model used to bring available information to bear on this problem. METHOD: Published estimates of CHD incidence were identified and methodologies were critically appraised. Estimates were extrapolated to a district population. A three month cohort study of patients with suspected CHD was undertaken within a district general hospital and a model of these clinical pathways was used to examine the volumes of patients and services required to meet the estimated levels of need. RESULTS: From published studies, estimates of CHD incidence ranged from 83 to 3600 per 100 000. From the cohort study, of patients referred with possible CHD 62% received a definitive diagnosis of CHD, 56% underwent an exercise ECG, 16% received an angiogram, 4% received a CABG and 2% a PTCA. Using these figures together with the cohort study, estimated activity ranges from 247 to 6475 surgical interventions per million population compared with the National Service Framework for Coronary Heart Disease recommendations of 1500 procedures per million. CONCLUSIONS: Current research on CHD incidence gives a very wide variation in estimated need. This makes its value for service planning questionable and the model highlights a need for further high quality research. The model provides a link between epidemiological research and secondary care service planning and supports the implementation of recommendations within the National Service Framework for Coronary Heart Disease.