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Economic Evaluation of the 2016 Chinese Guideline and Alternative Risk Thresholds of Initiating Statin Therapy for the Management of Atherosclerotic Cardiovascular Disease

  • Yawen JiangEmail author
  • Weiyi Ni
Original Research Article

Abstract

Objective

The 2016 Chinese guidelines for the management of dyslipidemia recommended mixed rules that centered around a 10% 10-year risk threshold to initiate statins for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). The present study aimed to evaluate the cost-effectiveness of the guideline statin-initiation strategy and alternative strategies.

Methods

A decision analytic model using discrete event simulation with event probabilities based on a validated ASCVD risk prediction tool for Chinese was constructed. Risk factor inputs were from the dataset of a nationally representative survey of middle-aged and elderly Chinese. Data of statin treatment effectiveness were from a published meta-analysis. Other key input data were identified from the literature or relevant databases. The strategies we evaluated were the guideline strategy, a 15% 10-year risk threshold strategy and a 20% 10-year risk threshold strategy. After excluding any extended dominance strategies, the incremental costs per quality-adjusted life year (QALY) gained of each strategy was calculated.

Results

The 20% 10-year risk threshold strategy was an extended dominance option. The incremental costs per QALY gained from the 15% 10-year risk threshold strategy compared with no treatment and the guideline strategy compared with the 15% 10-year risk threshold strategy were CN¥69,309 and CN¥154,944, respectively. The results were robust in most sensitivity analyses.

Conclusions

The guideline strategy and the 15% 10-year risk threshold strategy are optimal when using the three times and the two times the gross domestic product per capita willingness-to-pay standards, respectively.

Notes

Acknowledgements

The authors thank Erli Zhang, MD, Guangyao Li, PhD, and Xiayu Jiao, MS, for their work on reviewing the model. This analysis uses data and information from the CHARLS dataset and codebook. For more information, please refer to charls.pku.edu.cn. The contents of this article are solely the responsibility of the authors.

Author Contributions

Concept and overall approach: YJ and WN; data collection: YJ and WN; programming: YJ; analysis and interpretation: YJ and WN; drafting the manuscript: YJ; reviewing and revising the manuscript: YJ and WN.

Compliance with Ethical Standards

Funding

The authors did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors for the submitted work.

Conflict of interest

YJ was a research assistant at the University of Southern California when the submitted work was conducted and is an employee of Amgen Inc. WN was a research fellow at the University of Southern California when the submitted work was conducted and is an employee of Medtronic Inc.

Data Availability Statement

All data generated or analyzed during this study are included in this manuscript (and its online supplemental files). The Excel simulation model used for the analysis and the program code in the model were provided for peer review.

Supplementary material

40273_2019_791_MOESM1_ESM.docx (650 kb)
Supplementary material 1 (DOCX 649 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical and Health EconomicsUniversity of Southern California, USC Schaeffer CenterLos AngelesUSA

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