Dataset Information

Cost-effectiveness analysis for HbA1c test intervals to screen patients with type 2 diabetes based on risk stratification.

ABSTRACT:

Background

The best HbA1c test interval strategy for detecting new type 2 diabetes mellitus (T2DM) cases in healthy individuals should be determined with consideration of HbA1c test characteristics, risk stratification towards T2DM and cost effectiveness.

Methods

State transition models were constructed to investigate the optimal screening interval for new cases of T2DM among each age- and BMI-stratified health individuals. Age was stratified into 30-44-, 45-59-, and 60-74-year-old age groups, and BMI was also stratified into underweight, normal, overweight and obesity. In each model, different HbA1c test intervals were evaluated with respect to the incremental cost-effectiveness ratio (ICER) and costs per quality-adjusted life year (QALY). Annual intervals (Japanese current strategy), every 3 years (recommendations in US and UK) and intervals which are tailored to each risk stratification group were compared. All model parameters, including costs for screening and treatment, rates for complications and mortality and utilities, were taken from published studies. The willingness-to-pay threshold in the cost-effectiveness analysis was set to US $50,000/QALY.

Results

The HbA1c test interval for detecting T2DM in healthy individuals varies by age and BMI. Three-year intervals were the most cost effective in obesity at all ages-30-44: $15,034/QALY, 45-59: $11,849/QALY, 60-74: $8685/QALY-compared with the other two interval strategies. The three-year interval was also the most cost effective in the 60-74-year-old age groups-underweight: $11,377/QALY, normal: $18,123/QALY, overweight: $12,537/QALY-and in the overweight 45-59-year-old group; $18,918/QALY. In other groups, the screening interval for detecting T2DM was found to be longer than 3 years, as previously reported. Annual screenings were dominated in many groups with low BMI and in younger age groups. Based on the probability distribution of the ICER, results were consistent among any groups.

Conclusions

The three-year screening interval was optimal among elderly at all ages, the obesity at all ages and the overweight in 45-59-year-old group. For those sin the low-BMI and younger age groups, the optimal HbA1c test interval could be longer than 3 years. Annual screening to detect T2DM was not cost effective and should not be applied in any population.

SUBMITTER: Ohde S

PROVIDER: S-EPMC8141129 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

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