Byambadorj Nyamdavaa, Best Rohan, Mandakh Undram, Sinha Kompal
Department of Economics, Macquarie Business School, Macquarie University, Sydney, New South Wales, Australia.
Department of Family Medicine, School of Medicine, Mongolian National University of Medical Science, Ulaanbaatar, Mongolia.
PLoS One. 2025 Jun 10;20(6):e0324378. doi: 10.1371/journal.pone.0324378. eCollection 2025.
Elevated consumption of sugar-sweetened beverages (SSBs) has been associated with an increase in obesity, type 2 diabetes, and other non-communicable diseases (NCDs), a significant health and economic burden on Mongolia. To address this, the government has introduced a 20% SSB tax set to take effect in 2027. This study conducts a Cost-Effectiveness Analysis (CEA) using a Markov cohort model, incorporating Double Machine Learning (DML) to estimate price elasticity and assess policy-driven consumption changes while addressing potential confounding. The analysis integrates DML-estimated price elasticity and consumption shifts with disease transition probabilities, simulating outcomes for the 2023 Mongolian population, aged over 15 years old, over two time horizons of 20 years and a lifetime. The model estimates changes in obesity prevalence, healthcare costs, and disease burden, translating them into Disability-Adjusted Life Years (DALYs) averted, and Quality-Adjusted Life Years (QALYs) gained. Tax revenue projections and sensitivity analyses further assess the robustness of assumptions. By combining machine learning-based causal inference with economic modelling, this study provides policy-relevant evidence on the cost-effectiveness of SSB taxation, supporting data-driven decision-making for public health strategies in Mongolia, highlighting the tax's potential to reduce the burden of NCDs and promote healthier behaviours.
含糖饮料（SSB）的高消费量与肥胖、2型糖尿病及其他非传染性疾病（NCD）的增加有关，给蒙古带来了重大的健康和经济负担。为解决这一问题，政府已引入一项20%的含糖饮料税，将于2027年生效。本研究使用马尔可夫队列模型进行成本效益分析（CEA），纳入双重机器学习（DML）以估计价格弹性，并在解决潜在混杂因素的同时评估政策驱动的消费变化。该分析将DML估计的价格弹性和消费变化与疾病转变概率相结合，模拟了2023年15岁以上蒙古人口在20年和一生两个时间范围内的结果。该模型估计肥胖患病率、医疗成本和疾病负担的变化，将其转化为避免的伤残调整生命年（DALY）和获得的质量调整生命年（QALY）。税收收入预测和敏感性分析进一步评估了假设的稳健性。通过将基于机器学习的因果推断与经济建模相结合，本研究提供了关于含糖饮料征税成本效益的政策相关证据，支持蒙古公共卫生战略的数据驱动决策，突出了该税收在减轻非传染性疾病负担和促进更健康行为方面的潜力。
