Cost-effectiveness of intensive interventions compared to standard care in individuals with type 2 diabetes: A systematic review and critical appraisal of decision-analytic models.

AIMS

The objective of this systematic review is to identify and assess the quality of published decision-analytic models evaluating the long-term cost-effectiveness of target-driven intensive interventions for single and multifactorial risk factor control compared to standard care in people with type 2 diabetes.

METHODS

We searched the electronic databases MEDLINE, the National Health Service Economic Evaluation Database, Web of Science and the Cochrane Library from inception to October 31, 2019. Articles were eligible for inclusion if the studies had used a decision-analytic model evaluating both the long-term costs and benefits associated with intensive interventions for risk factor control compared to standard care in people with type 2 diabetes. Data were extracted using a standardised form, while quality was assessed using the decision-analytic model-specific Philips-criteria.

RESULTS

Overall, nine articles (11 models) were identified, four models evaluated intensive glycaemic control, three evaluated intensive blood pressure control, two evaluated intensive lipid control, and two evaluated intensive multifactorial interventions. Six reported using discrete-time simulations modelling approach, whereas five reported using a Markov modelling framework. The majority, seven studies, reported that the intensive interventions were dominant or cost-effective, given the assumptions and analytical perspective taken. The methodological and reporting quality of the studies was generally weak, with only four studies fulfilling more than 50% of their applicable Philips-criteria.

CONCLUSIONS

This is the first systematic review of decision-analytic models of target-driven intensive interventions for single and multifactorial risk factor control in individuals with type 2 diabetes. Identified shortcomings are lack of transparency in data identification and evidence synthesis as well as for the selection of the modelling approaches. Future models should aim to include greater evaluation of the quality of the data sources used and the assessment of uncertainty in the model.