Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning.
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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324378 |
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| author | Nyamdavaa Byambadorj Rohan Best Undram Mandakh Kompal Sinha |
| author_facet | Nyamdavaa Byambadorj Rohan Best Undram Mandakh Kompal Sinha |
| author_sort | Nyamdavaa Byambadorj |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-306e38b236dc4551a5fd0f6c5a1a05cb |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-306e38b236dc4551a5fd0f6c5a1a05cb2025-08-20T03:20:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032437810.1371/journal.pone.0324378Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning.Nyamdavaa ByambadorjRohan BestUndram MandakhKompal SinhaElevated 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.https://doi.org/10.1371/journal.pone.0324378 |
| spellingShingle | Nyamdavaa Byambadorj Rohan Best Undram Mandakh Kompal Sinha Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning. PLoS ONE |
| title | Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning. |
| title_full | Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning. |
| title_fullStr | Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning. |
| title_full_unstemmed | Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning. |
| title_short | Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning. |
| title_sort | protocol for evaluating the cost effectiveness of mongolia s sugar sweetened beverages tax using double machine learning |
| url | https://doi.org/10.1371/journal.pone.0324378 |
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