OBJECTIVES: Lipid screening is routine in type 2 diabetes (T2D) care for preventing cardiovascular disease. External quality assessment (EQA) of testing practice has demonstrated that bias is inherent to equipment and methods used in LDL testing. This research aims to demonstrate that health economic methods can be incorporated into EQA practices through estimating the costs and consequences of bias in LDL testing methods among otherwise healthy T2D patients in two geographic regions in Sweden.

METHODS: Real world EQA data obtained from each laboratory in the two regions were used to estimate the probability of false tests at each laboratory. These data were used together with Swedish T2D guidelines to develop a decision tree model to compare the costs of positive and negative tests, and the number false positives and false negatives resulting from equipment bias. A separate well-validated diabetes model, The Swedish Institute for Health Economics’ Cohort Diabetes Model, was used to estimate the long-term costs and consequences associated with false negative tests, where patients do not receive lipid lowering therapy when needed.

RESULTS: From 2019 to 2021, an estimated 6,000 patients received biased test results, where 3,600 were false negatives. The decision tree model estimated that an additional cost of 11 million SEK if the false tests were corrected. The IHE-DCM estimated that in correcting the 3,600 false negative tests, 16 Quality Adjusted Life years would be gained, SEK 320 000 would be saved, and that approximately six macrovascular events would be avoided.

CONCLUSIONS: This study demonstrates that health economic methods can be incorporated into the EQA practice to translate laboratory testing bias into costs and consequences that can be interpreted by decision makers. Our next analysis will move into the cancer disease area where the costs and effects of treatment have more serious impacts for patients and society.