How to Use Costs in Value-Based Healthcare: Learning from Real-life Examples

BACKGROUND

Healthcare organizations measure costs for business operations but do not routinely incorporate costs in decision-making on the value of care.

AIM

Provide guidance on how to use costs in value-based healthcare (VBHC) delivery at different levels of the healthcare system.

SETTING AND PARTICIPANTS

Integrated practice units (IPUs) for diabetes mellitus (DM) and for acute myocardial infarction (AMI) at the Leiden University Medical Center and a collaboration of seven breast cancer IPUs of the Santeon group, all in the Netherlands.

PROGRAM DESCRIPTION AND EVALUATION

VBHC aims to optimize care delivery to the patient by understanding how costs relate to outcomes. At the level of shared decision-making between patient and clinician, yearly check-up consultations for DM type I were analyzed for patient-relevant costs. In benchmarking among providers, quantities of cost drivers for breast cancer care were assessed in scorecards. In continuous learning, cost-effectiveness analysis was compared with radar chart analysis to assess the value of telemonitoring in outpatient follow-up.

DISCUSSION

Costs vary among providers in healthcare, but also between provider and patient. The joint analysis of outcomes and costs using appropriate methods helps identify and optimize the aspects of care that drive desired outcomes and value.

Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic

INTRODUCTION

Lockdown measures have a profound effect on many aspects of daily life relevant for diabetes self-management. We assessed whether lockdown measures, in the context of the COVID-19 pandemic, differentially affect perceived stress, body weight, exercise and related this to glycemic control in people with type 1 and type 2 diabetes.

RESEARCH DESIGN AND METHODS

We performed a short-term observational cohort study at the Leiden University Medical Center. People with type 1 and type 2 diabetes ≥18 years were eligible to participate. Participants filled out online questionnaires, sent in blood for hemoglobin A1c (HbA1c) analysis and shared data of their flash or continuous glucose sensors. HbA1c during the lockdown was compared with the last known HbA1c before the lockdown.

RESULTS

In total, 435 people were included (type 1 diabetes n=280, type 2 diabetes n=155). An increase in perceived stress and anxiety, weight gain and less exercise was observed in both groups. There was improvement in glycemic control in the group with the highest HbA1c tertile (type 1 diabetes: -0.39% (-4.3 mmol/mol) (p<0.0001 and type 2 diabetes: -0.62% (-6.8 mmol/mol) (p=0.0036). Perceived stress was associated with difficulty with glycemic control (p<0.0001).

CONCLUSIONS

An increase in perceived stress and anxiety, weight gain and less exercise but no deterioration of glycemic control occurs in both people with relatively well-controlled type 1 and type 2 diabetes during short-term lockdown measures. As perceived stress showed to be associated with glycemic control, this provides opportunities for healthcare professionals to put more emphasis on psychological aspects during diabetes care consultations.

Sex differences in cardiometabolic risk factors, pharmacological treatment and risk factor control in type 2 diabetes: findings from the Dutch Diabetes Pearl cohort

INTRODUCTION

Sex differences in cardiometabolic risk factors and their management in type 2 diabetes (T2D) have not been fully identified. Therefore, we aimed to examine differences in cardiometabolic risk factor levels, pharmacological treatment and achievement of risk factor control between women and men with T2D.

RESEARCH DESIGN AND METHODS

Cross-sectional data from the Dutch Diabetes Pearl cohort were used (n=6637, 40% women). Linear and Poisson regression analyses were used to examine sex differences in cardiometabolic risk factor levels, treatment, and control.

RESULTS

Compared with men, women had a significantly higher body mass index (BMI) (mean difference 1.79 kg/m² (95% CI 1.49 to 2.08)), while no differences were found in hemoglobin A_1c (HbA_1c) and systolic blood pressure (SBP). Women had lower diastolic blood pressure (-1.94 mm Hg (95% CI -2.44 to -1.43)), higher total cholesterol (TC) (0.44 mmol/L (95% CI 0.38 to 0.51)), low-density lipoprotein cholesterol (LDL-c) (0.26 mmol/L (95% CI 0.22 to 0.31)), and high-density lipoprotein cholesterol (HDL-c) sex-standardized (0.02 mmol/L (95% CI 0.00 to 0.04)), and lower TC:HDL ratio (-0.29 (95% CI -0.36 to -0.23)) and triglycerides (geometric mean ratio 0.91 (95% CI 0.85 to 0.98)). Women had a 16% higher probability of being treated with antihypertensive medication in the presence of high cardiovascular disease (CVD) risk and elevated SBP than men (relative risk 0.84 (95% CI 0.73 to 0.98)), whereas no sex differences were found for glucose-lowering medication and lipid-modifying medication. Among those treated, women were less likely to achieve treatment targets of HbA_1c (0.92 (95% CI 0.87 to 0.98)) and LDL-c (0.89 (95% CI 0.85 to 0.92)) than men, while no differences for SBP were found.

CONCLUSIONS

In this Dutch T2D population, women had a slightly different cardiometabolic risk profile compared with men and a substantially higher BMI. Women had a higher probability of being treated with antihypertensive medication in the presence of high CVD risk and elevated SBP than men, and were less likely than men to achieve treatment targets for HbA_1c and LDL levels.