Hospital costs associated with adverse events in people with diabetes in the UK

Temporal incremental healthcare costs associated with complications in Hong Kong Chinese patients with type 2 diabetes: A prospective study in Joint Asia diabetes evaluation (JADE) Register (2007-2019)

OBJECTIVE

We examined incremental healthcare costs (inpatient and outpatient) related to complications in Chinese patients with type 2 diabetes (T2D) during the year of occurrence and post-event years, utilizing the Joint Asia Diabetes Evaluation (JADE) Register cohort of Hong Kong Chinese patients with T2D between 2007 and 2019.

RESEARCH DESIGN AND METHODS

19,440 patients with T2D underwent structured evaluation utilizing the JADE platform with clinical outcomes data retrieved from territory-wide electronic medical records including inpatient, outpatient and emergency care. Two-part model was adopted to account for skewed healthcare costs distribution. Incremental healthcare costs associated with nine non-fatal diabetes complications and all-cause death were estimated, adjusted for demographic, clinical, lifestyle factors and comorbidities.

RESULTS

In this prospective cohort [mean ± SD age:59.9 ± 11.9 years, 56.6 % men, duration of diabetes:7.3 ± 7.5 years, HbA1C:7.5 ± 1.6 %] observed for 7 (interquartile range:4-9) years (142,132 patient-years), the mean annual healthcare costs, mainly due to inpatient cost, were USD$2,990 ± 9,960. Lower extremity amputation (LEA) (USD$31,302; 95 %CI: 25,706-37,004), hemorrhagic stroke (USD$21,164; 17,680-24,626), ischemic stroke (USD$17,976; $15,937-20,352) and end-stage disease (ESRD) (USD$14,774; 13,405-16,250) in the year of event incurred the highest cost. Residual healthcare costs in the post-event years were highest for ESRD, LEA, haemorrhagic stroke and incident cancer.

CONCLUSION

These comprehensive temporal healthcare cost estimates for diabetes-related complications allows the performance of long-term, patient-level, cost-effectiveness analyses on T2D prevention and treatment strategies relevant to an Asian and possibly global contexts. These may inform decision-makers on resource allocation aimed at reducing the burden of T2D and chronic diseases.

Examining the Impact of Structural Uncertainty Across 10 Type 2 Diabetes Models: Results From the 2022 Mount Hood Challenge

OBJECTIVES

The Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes.

METHODS

Ten independent modeling groups completed a blinded simulation exercise to estimate the cost-effectiveness of 3 interventions in 2 type 2 diabetes populations. Modeling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB) and the drivers of those differences.

RESULTS

Overall, modeling groups agreed which interventions had a positive NMB (ie, were cost-effective), Although estimates of NMB varied substantially-by up to £23 696 for 1 intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modeled health states was also a significant predictor of NMB.

CONCLUSIONS

This exercise demonstrates that structural uncertainty between different health economic models affects cost-effectiveness estimates. Although it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimizing the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modeling.

Evaluating the Long-Term Cost-Effectiveness of the English NHS Diabetes Prevention Programme using a Markov Model

BACKGROUND

In 2016, England launched the largest nationwide diabetes mellitus prevention programme, the NHS Diabetes Prevention Programme (NHS DPP). This paper seeks to evaluate the long-term cost-effectiveness of this programme.

METHODS

A Markov cohort state transition model was developed with a 35-year time horizon and yearly cycles to compare referral to the NHS DPP to usual care for individuals with non-diabetic hyperglycaemia. The modelled cohort of individuals mirrored the age profile of referrals received by the programme by April 2020. A health system perspective was taken, with costs in UK £ Sterling (price year 2020) and outcomes in terms of quality-adjusted life-years (QALYs). Probabilistic analysis with 10,000 Monte Carlo simulations was used. Several sensitivity analyses were conducted to explore the uncertainty surrounding the base case results, particularly varying the length of time for which the effectiveness of the programme was expected to last.

RESULTS

In the base case, using only the observed effectiveness of the NHS DPP at 3 years, it was found that the programme is likely to dominate usual care, by generating on average 40.8 incremental QALYs whilst saving £135,755 in costs for a cohort of 1000. At a willingness to pay of £20,000 per QALY, 98.1% of simulations were on or under the willingness-to-pay threshold. Scaling this up to the number of referrals actually received by the NHS DPP prior to April 2020, cost savings of £71.4 million were estimated over the 35-year time horizon and an additional 21,472 QALYs generated. These results are robust to several sensitivity analyses.

CONCLUSION

The NHS DPP is likely to be cost-effective. Indeed, in the majority of the simulations, the NHS DPP was cost-saving and generated greater QALYs, dominating usual care. This research should serve as evidence to support the continued investment or recommissioning of diabetes prevention programmes.

Estimating Costs Associated with Disease Model States Using Generalized Linear Models: A Tutorial

Estimates of costs associated with disease states are required to inform decision analytic disease models to evaluate interventions that modify disease trajectory. Increasingly, decision analytic models are developed using patient-level data with a focus on heterogeneity between patients, and there is a demand for costs informing such models to reflect individual patient costs. Statistical models of health care costs need to recognize the specific features of costs data which typically include a large number of zero observations for non-users, and a skewed and heavy right-hand tailed distribution due to a small number of heavy healthcare users. Different methods are available for modelling costs, such as generalized linear models (GLMs), extended estimating equations and latent class approaches. While there are tutorials addressing approaches to decision modelling, there is no practical guidance on the cost estimation to inform such models. Therefore, this tutorial aims to provide a general guidance on estimating healthcare costs associated with disease states in decision analytic models. Specifically, we present a step-by-step guide to how individual participant data can be used to estimate costs over discrete periods for participants with particular characteristics, based on the GLM framework. We focus on the practical aspects of cost modelling from the conceptualization of the research question to the derivation of costs for an individual in particular disease states. We provide a practical example with step-by-step R code illustrating the process of modelling the hospital costs associated with disease states for a cardiovascular disease model.

Social Return on Investment of Social Prescribing via a Diabetes Technician for Preventing Type 2 Diabetes Progression

In Wales, the prevalence of Type 2 Diabetes Mellitus (T2DM) has increased from 7.3% in 2016 to 8% in 2020, creating a major concern for the National Health Service (NHS). Social prescribing (SP) has been found to decrease T2DM prevalence and improve wellbeing. The MY LIFE programme, a scheme evaluated between June 2021 and February 2022 in the Conwy West Primary Care Cluster, aimed to prevent T2DM by referring prediabetic patients with a BMI of ≥30 to a diabetes technician (DT), who then signposted patients to community-based SP programmes, such as the National Exercise Referral Scheme (NERS), KindEating, and Slimming World. Although some patients engaged with SP, others chose to connect only with the DT. A Social Return on Investment (SROI) analysis was conducted to evaluate those patients who engaged with the DT plus SP, and those who connected solely with the DT. Relevant participant outcomes included 'mental wellbeing' and 'good overall health', which were measured at baseline (n = 54) and at the eight-week follow-up (n = 24). The estimated social value for every GBP 1 invested for participants who engaged with the 'DT only' ranged from GBP 4.67 to 4.70. The social value for participants who engaged with the 'DT plus SP programme' ranged from GBP 4.23 to 5.07. The results indicated that most of the social value generated was associated with connecting with the DT.

Nomogram model and risk score to predict 5-year risk of progression from prediabetes to diabetes in Chinese adults: Development and validation of a novel model

AIM

To develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes.

METHODS

There were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance.

RESULTS

We developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively.

CONCLUSIONS

We developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Burden of established cardiovascular disease in people with type 2 diabetes and matched controls: Hospital-based care, days absent from work, costs and mortality

AIMS

To assess hospital-based care, work absence, associated costs, and mortality in patients with type 2 diabetes with and without established cardiovascular disease (eCVD) compared to matched controls.

MATERIALS AND METHODS

In a population-based cohort study, we analysed individual-level data from national health, social insurance and socio-economic registers for people diagnosed with type 2 diabetes before age 70 years and controls (5:1) in Sweden. Regression analysis was used to attribute costs and days absent due to eCVD. Mortality was analysed using Cox proportional hazard regression, stratified by birth year and adjusted for sex and education.

RESULTS

Thirty percent (n = 136 135 of 454 983) of people with type 2 diabetes had ≥1 person-year with eCVD (women 24%; men 34%). The mean annual costs of hospital-based care for diabetes complications were EUR 2629 (95% confidence interval [CI] 2601-2657) of which EUR 2337 (95% CI 2309-2365) were attributed to eCVD (89%). The most costly person-years (10th percentile) were observed in a broad subgroup, 42% of people with type 2 diabetes and eCVD. People with type 2 diabetes had on average 146 days absent (95% CI 145-147) per year, of which 68 days (47%; 95% CI 67-70) were attributed to eCVD. The mortality hazard ratio for type 2 diabetes with eCVD was 4.63 (95%CI 4.58-4.68) and without eCVD was 1.86 (95% CI 1.84-1.88) compared to controls without eCVD.

CONCLUSION

The sizable burden of eCVD on both the individual with type 2 diabetes and society calls for efficient management in order to reduce the risks for those living with eCVD and to postpone its onset.