Cost-effectiveness of the psycho-educational blended (group and online) intervention HypoAware compared with usual care for people with Type 1 and insulin-treated Type 2 diabetes with problematic hypoglycaemia: analyses of a cluster-randomized controlled trial

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes

BACKGROUND

There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations.

OBJECTIVES

To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care.

SEARCH METHODS

We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section.

SELECTION CRITERIA

We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy.

DATA COLLECTION AND ANALYSIS

We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors.  Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available.

MAIN RESULTS

We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted.  Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three.  Combinations of the three most effective QI strategies were estimated to lead to the below effects:  - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%;  - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg;  - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other).

AUTHORS' CONCLUSIONS

There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.

The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?

INTRODUCTION

For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data.

METHODS

Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR).

RESULTS

For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs.

CONCLUSION

LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated.

The statistical approach in trial-based economic evaluations matters: get your statistics together!

Background

Baseline imbalances, skewed costs, the correlation between costs and effects, and missing data are statistical challenges that are often not adequately accounted for in the analysis of cost-effectiveness data. This study aims to illustrate the impact of accounting for these statistical challenges in trial-based economic evaluations.

Methods

Data from two trial-based economic evaluations, the REALISE and HypoAware studies, were used. In total, 14 full cost-effectiveness analyses were performed per study, in which the four statistical challenges in trial-based economic evaluations were taken into account step-by-step. Statistical approaches were compared in terms of the resulting cost and effect differences, ICERs, and probabilities of cost-effectiveness.

Results

In the REALISE study and HypoAware study, the ICER ranged from 636,744€/QALY and 90,989€/QALY when ignoring all statistical challenges to − 7502€/QALY and 46,592€/QALY when accounting for all statistical challenges, respectively. The probabilities of the intervention being cost-effective at 0€/ QALY gained were 0.67 and 0.59 when ignoring all statistical challenges, and 0.54 and 0.27 when all of the statistical challenges were taken into account for the REALISE study and HypoAware study, respectively.

Conclusions

Not accounting for baseline imbalances, skewed costs, correlated costs and effects, and missing data in trial-based economic evaluations may notably impact results. Therefore, when conducting trial-based economic evaluations, it is important to align the statistical approach with the identified statistical challenges in cost-effectiveness data. To facilitate researchers in handling statistical challenges in trial-based economic evaluations, software code is provided.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06513-1.

Assessing the Effect of Including Social Costs in Economic Evaluations of Diabetes-Related Interventions: A Systematic Review

Background

The economic burden of diabetes from a societal perspective is well documented in the cost-of-illness literature. However, the effect of considering social costs in the results and conclusions of economic evaluations of diabetes-related interventions remains unknown.

Objective

To investigate whether the inclusion of social costs (productivity losses and/or informal care) might change the results and conclusions of economic evaluations of diabetes-related interventions.

Methods

A systematic review was designed and launched on Medline and the Cost-Effectiveness Analysis Registry from the University of Tufts, from the year 2000 until 2018. Included studies had to fulfil the following criteria: i) being an original study published in a scientific journal, ii) being an economic evaluation of an intervention on diabetes, iii) including social costs, iv) being written in English, v) using quality-adjusted life years as outcome, and vi) separating the results according to the perspective applied.

Results

From the 691 records identified, 47 studies (6.8%) were selected. Productivity losses were included in 45 of the selected articles (73% used the human capital approach) whereas informal care costs in only 13 (when stated, the opportunity cost method was used in seven studies and the replacement cost in one). The 47 studies resulted in 110 economic evaluation estimations. The inclusion of social costs changed the conclusions in 8 estimations (17%), 6 of them switching from not cost-effective from the healthcare perspective to cost-effective or dominant from the societal perspective. Considering social costs altered the results from cost-effective to dominant in 9 estimations (19%).

Conclusion

When social costs are considered, the results and conclusions of economic evaluations performed in diabetes-related interventions can alter. Wide methodological variations have been observed, which limit the comparability of studies and advocate for the inclusion of a wider perspective via the consideration of social costs in economic evaluations and methodological guidelines relating to their estimation and valuation.

Comparing the EQ-5D-5L crosswalks and value sets for England, the Netherlands and Spain: Exploring their impact on cost-utility results

This study compares the five-level EuroQol five-dimension questionnaire (EQ-5D-5L) crosswalks and the 5L value sets for England, the Netherlands, and Spain and explores the implication of using one or the other for the results of cost-utility analyses. Data from two randomized controlled trials in depression and diabetes were used. Utility value distributions were compared, and mean differences in utility values between the EQ-5D-5L crosswalk and the 5L value set were described by country. Quality-adjusted life years (QALYs) were calculated using the area-under-the-curve method. Incremental cost-effectiveness ratios (ICERs) were calculated, and uncertainty around ICERs was estimated using bootstrapping and graphically shown in cost-effectiveness acceptability curves. For all countries investigated, utility value distributions differed between the EQ-5D-5L crosswalk and 5L value set. In both case studies, mean utility values were lower for the EQ-5D-5L crosswalk compared with the 5L value set in England and Spain, but higher in the Netherlands. However, these differences in utility values did not translate into relevant differences across utility estimation methods in incremental QALYs and the interventions' probability of cost-effectiveness. Thus, our results suggest that EQ-5D-5L crosswalks and 5L value sets can be used interchangeably in patients affected by mild or moderate conditions. Further research is needed to establish whether these findings are generalizable to economic evaluations among severely ill patients.

How has psycho-behavioural research advanced our understanding of hypoglycaemia in type 1 diabetes?

Almost 100 years since the discovery of insulin, hypoglycaemia remains a barrier for people with type 1 diabetes to achieve and maintain blood glucose at levels which prevent long-term diabetes-related complications. Although hypoglycaemia is primarily attributable to the limitations of current treatment and defective hormonal counter-regulation in type 1 diabetes, the central role of psycho-behavioural factors in preventing, recognizing and treating hypoglycaemia has been acknowledged since the early 1980s. Over the past 25 years, as documented in the present review, there has been a substantial increase in psycho-behavioural research focused on understanding the experience and impact of hypoglycaemia. The significant contributions have been in understanding the impact of hypoglycaemia on a person's emotional well-being and aspects of life (e.g. sleep, driving, work/social life), identifying modifiable psychological and behavioural risk factors, as well as in developing psycho-behavioural interventions to prevent and better manage (severe) hypoglycaemia. The impact of hypoglycaemia on family members has also been confirmed. Structured diabetes education programmes and psycho-behavioural interventions with a focus on hypoglycaemia have both been shown to be effective in addressing problematic hypoglycaemia. However, the findings have also revealed the complexity of the problem and the need for a personalized approach, taking into account the individual's knowledge of, and emotional/behavioural reactions to hypoglycaemia. Evidence is emerging that people with persistent and recurrent severe hypoglycaemia, characterized by deeply entrenched cognitions and lack of concern around hypoglycaemia, can benefit from tailored cognitive behavioural therapy.