Modelling overview

We evaluated 3 SSB tax policy scenarios that were chosen based on international scientific consensus recommendation and globally implemented SSB taxes accounting for context-relevant factors:

1. 20% ad valorem tax on SSBs based on scientific recommendations and implemented ad valorem or volumetric taxes in, e.g., Mexico, Chile, and United States (US) legislatures [4,6,7,23];
2. 20% ad valorem tax on SSBs and fruit juice, extending the tax to account for high consumption levels and the caloric content of juices [12];
3. 30% reformulation of SSBs towards lower sugar content based on tiered taxes such as the UK SDIL [4,7].

Further details on all scenarios and related assumptions are described below and in Methods A in S1 Appendix under “Policy module.”

To simulate the health and economic impact of these SSB taxation scenarios, we developed and validated an NCD microsimulation model for Germany based on the UK IMPACT_NCD framework (IMPACT_NCD Germany; hereafter IMPACT_NCD). We modelled the German population age 30 to 90 years over 20 years (2023 to 2043) and performed an economic evaluation from healthcare and societal perspectives [24–27]. For this, we created a synthetic population stratified by age and sex that captures the real demographics, exposures, dietary intakes, and disease epidemiology of the actual German population using available national data sources (see below and in Methods A in S1 Appendix under “Epidemiological engine”).

The main pathways of our model are founded on widely accepted epidemiological evidence and summarised in Fig 1. Briefly, in our main analysis we assumed that SSB taxation would, depending on the scenario, induce changes in SSB and fruit juice consumption or SSB sugar content guided by economic theory and observations from other countries in which taxes were implemented. We then modelled the effect of changed sugar consumption from SSBs and fruit juice on BMI and consequently BMI and SSB intake as exposures for T2DM, CHD, and stroke. Direct (BMI-independent) effects of SSBs on T2DM and CHD are assumed to be due to the added sugar they contain [21,28]. We also considered T2DM as a risk factor for CHD, stroke, and non-cardiovascular mortality. To analyse structural uncertainty, we (1) re-estimated all scenarios using only BMI-mediated effects, thus excluding the potentially more uncertain estimates of the direct cardiometabolic effects of SSBs from nutritional epidemiological studies [21]; and (2) cross-validated these results with the PRIMEtime cohort model (see below and Methods B and Methods C in S1 Appendix) [29].

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Fig 1. Policy, exposure, and disease pathways of the SSB tax modelling with IMPACT_NCD Germany.

Complete modelled pathways. Dashed arrows indicate direct effects of SSBs that are excluded in structural uncertainty analyses. Grey boxes indicate modelled policy pathways and corresponding numbered arrows denote underlying modelled mechanisms: (1) Economic theory suggests that SSB producers will pass some proportion of a tax on SSBs along to consumers via increased prices (tax pass-through). (2) After accounting for the tax pass-through rate, economic theory suggests that increased prices of SSBs lead to a change in SSB consumption based on their own-price elasticity of demand. (3) Tiered taxation with different tax rates according to SSB sugar content, incentivizes reformulation towards lower sugar content by producers to avoid the tax burden. (4) After accounting for the tax pass-through rate, economic theory suggests that increased prices of SSBs lead to substitution with similar goods such as fruit juice based on their cross-price elasticity of demand. Green boxes indicate exposures. Orange boxes and blue boxes indicate disease and mortality outcomes, respectively. Δ, “change in”; BMI, body-mass index; Juice, fruit juice; CHD, coronary heart disease; CVD, cardiovascular disease; ml, millilitre; SSB, sugar-sweetened beverages; T2DM, type 2 diabetes mellitus.

https://doi.org/10.1371/journal.pmed.1004311.g001

Our model choice was guided by a previous review that we conducted during the Policy Evaluation Network project [30]. The technical details of both models have been described previously [20,26,27]. For an overview of all used parameters and data sources see Tables A and G in S1 Appendix. We provide the source code for IMPACT_NCD in a public repository at https://github.com/kalleEF/IMPACT-NCD-Germany.

Sugar-sweetened beverage taxation scenarios

All SSB taxation scenarios including the additional taxation of fruit juice in Germany were modelled compared to a baseline without tax. SSBs were defined as (un-)caffeinated soft drinks or fruit drinks with added sugars (i.e., caloric sweeteners). Fruit juice was defined as 100% fruit juice and nectars or other types of juices that might contain added sugars. Unfortunately, we were not able to distinguish types of juices in detail. However, we consider the resulting bias as negligible because over 80% of consumed fruit juice in Germany does not contain added sugars [31].

The modelled SSB tax policy scenarios were based on globally implemented taxes and international scientific consensus recommendations [6,23]. Three scenarios were selected according to their relevance for the German context and considering limitations of the available beverage consumption data. The modelled scenarios were:

1. 20% ad valorem tax on SSBs (i.e., the tax is calculated as a proportion of the price) based on implemented ad valorem or volumetric taxes as for example in Mexico, Chile, and US legislatures [7]. Due to lack of access to product-level ingredient and price data, we could only approximate volumetric taxes through this scenario. However, most implemented taxes were designed to increase prices by 10% to 20% based on scientific recommendations [6,23]. Evidence has shown that such taxes indeed have increased prices and reduced SSB consumption (hereafter: “ad valorem tax”) [4,6,7,23];
2. 20% ad valorem tax on SSBs and fruit juice that extends SSB taxation to account for the caloric content of fruit juices and the high baseline consumption in Germany (hereafter: “extended ad valorem tax”);
3. tiered tax design with increasing tax rates according to specific sugar thresholds that incentivizes producers to reformulate SSBs towards 30% lower sugar content. This was based on observed reformulation effects under the UK SDIL, which could serve as a blueprint policy for other European countries such as Germany (hereafter: “tiered tax”) [15,32,33].

Further details on the scenarios and their implementation in the model are described below and in Methods A in S1 Appendix under “Policy module.”

Synthetic German population

To simulate the population-level impact of the policy scenarios, we constructed a synthetic German population. For this we combined (1) data on the exposures BMI and intake of SSBs and fruit juice from 3 waves of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study (S4, F4, and FF4; 1999, 2007, and 2014), a population-based cohort in southern Germany, and the nationally representative dietary survey Nationale Verzehrsstudie (NVS) II (2006) [34–36]; (2) national data on the epidemiology of stroke, CHD, and T2DM [37]; and (3) information on death counts, population count estimates, and projections by age and sex from official sources and a de novo mortality forecast using a functional demographic model (Methods A in S1 Appendix under “Mortality calibration”) [38–41]. Detailed step-by-step descriptions of all data sources, applied statistical methods, and validation results for the synthetic population are described in Methods A in S1 Appendix under “Epidemiological engine.”

Estimating exposure distributions

To estimate exposure distributions conditional on age and sex, we used generalised additive models for location, shape and scale (GAMLSS), which can handle complex relationships between the response variable and its predictors and numerous types of distributions [42]. For the distribution of BMI, we used data from the 3 KORA waves to incorporate time trends. In KORA FF4 usual dietary intakes were calculated using a blended approach consisting of up to three 24-h food lists and a food frequency questionnaire [35]. In NVS II dietary intakes were calculated based on two 24-h dietary recalls [34]. For the estimation of the SSB and fruit juice intake distributions in ml/day, we primarily relied on KORA FF4 and supplemented this with the NVS II due to the underrepresentation of younger age groups in KORA FF4. Our method accounts for non- and high-consumers in the beverage intake distributions via mixture models. We calculated sugar intake from SSBs and fruit juice using information on beverage-specific sugar consumption in g/ml that was available in KORA FF4. We adjusted beverage and sugar intake for misreporting with the residual method (i.e., regressed on energy intake) [43]. Further details are in Methods A in S1 Appendix under “Exposure module.”

Estimating disease epidemiology

Information on the epidemiology of T2DM was gathered from the most recent data of the German national diabetes surveillance [37]. Due to data limitations for stroke and CHD incidence, we applied SCORE2 risk equations to the KORA data to estimate the yearly incidence of cardiovascular disease by age and sex [44, 45]. Incidence trends were based on the empirical trends between follow-ups S4, F4, and FF4. The proportions of stroke and CHD events in these estimates were based on results from the European Prospective Investigation into Cancer and Nutrition (EPIC) [46]. Finally, all epidemiological data was aligned and smoothed with DISMOD II before application in IMPACT_NCD to improve consistency between incidence, prevalence, and mortality data [47]. Further details are in Methods A in S1 Appendix under “Disease module.”

Effect of SSB taxation scenarios on sugar intake

According to economic theory, taxation can have both demand and supply side effects [48]. In the case of SSBs, taxation leads to increased consumer prices as producers pass some proportion of the tax along (i.e., tax pass-through) [48]. Price changes influence consumption of goods due to own- and cross-price elasticities of demand (i.e., %-change in consumption following price increase of the same or another product category by 1%) [49]. However, SSB taxes (e.g., the UK SDIL) can be designed with tax levels that depend on beverage sugar content and thus incentivize product reformulation, giving producers a way to avoid the tax. These mechanisms have important implications on implementing the analysed policy scenarios in our model. For further details, see Methods A in S1 Appendix under “Policy module.”

In the “ad valorem tax” and “extended ad valorem tax” scenarios, the effect of price increases on SSB and fruit juice consumption was modelled with national price elasticities of demand. We first calculated the change in consumption of SSB and fruit juice and consequently the change in respective sugar intake. In the former we considered substitution from SSBs to fruit juice. In both scenarios, we assumed that the policy immediately affected beverage consumption. Because national elasticities were not available, we estimated de novo uncompensated price elasticities for beverage categories with an Almost Ideal Demand System using data from the German household consumption survey (Einkommens- und Verbrauchsstichprobe 2013 and 2018) (Methods A in S1 Appendix under “Policy module”). We estimated the own-price elasticity of SSBs and fruit juice to be −0.956 (95%-confidence interval [−1.174, −0.738], p < 0.001) and −1.106 (95%-CI [−1.397, −0.814], p < 0.001), respectively. The cross-price elasticity for SSBs and fruit juice was estimated to be 0.052 (95%-CI [−0.138, 0.242], p = 0.593) (Table J in S1 Appendix). Based on a recent meta-analysis, we assumed that the tax pass-through was 82% (95%-CI [66,98]; p < 0.001) [4].

To implement the effects of reformulation in the “tiered tax” scenario, we had to make several assumptions. Based on a recent evaluation of the UK SDIL, we assumed that individual intake of sugar from SSBs would be reduced by 30% without changing consumption [15]. We additionally assumed that this reduction would be gradually come into effect over 3 years. The reformulation effect can only be approximated since we did not have access to product-level data in the KORA or NVS studies and were thus unable to directly specify the underlying sugar thresholds. This means we assumed that a tiered tax, replicating the design of the UK SDIL and taking German SSB price levels and sugar content into account, would gradually lead to similar reformulation effects as in the UK.

Effects of exposures on cardiometabolic risk

For all modelled exposure and disease pathways in Fig 1, we used high-quality evidence from meta-analyses of prospective cohort studies or randomised controlled trials and cohort pooling projects to translate changes in sugar and SSB intake into changes in BMI and the risk for stroke, CHD, and T2DM. For simplicity, we considered the metabolic effects of sugar from SSBs and fruit juice on BMI to be the same [50].

We modelled the ceteris paribus effect of reduced consumption of sugar from beverages (i.e., no compensation behaviour beyond considered beverages) on the long-term reduction in BMI with effect estimates from a meta-analysis of prospective cohorts [51]. The predicted reductions in weight are conservative and generally lower than using traditional energy balance equations [52,53] (for comparisons, see Table G in S1 Appendix).

Like previous diet policy modelling studies, we considered (1) BMI-mediated effects on stroke, CHD, and T2DM risk [27,54,55]; (2) BMI-independent (direct) effects of SSBs on CHD and T2DM risk due to their sugar content [21,50,56]; and (3) T2DM as a risk factor for stroke, CHD, and non-cardiovascular mortality [57]. We included direct effects of SSBs to reflect potential underlying mechanisms related to insulin resistance and inflammation but excluded them in structural uncertainty analyses (see below) [21,50,58,59]. However, we did not model potential direct effects of fruit juice due to higher uncertainty in these estimates [21]. An overview of all used risk parameters can be found in Table G in S1 Appendix.

IMPACT_NCD microsimulation

IMPACT_NCD is a dynamic, discrete-time, stochastic, open-cohort microsimulation model that simulates the life course of individuals and their counterfactuals under alternative policy scenarios. It enables the detailed simulation of diet policies and their impact on relevant exposures, subsequent disease epidemiology, and mortality in a competing risk framework accounting for different lag-times between exposures and outcomes. The effect of individual exposure changes on disease risk is achieved with individualised attributable fractions (Methods A in S1 Appendix under “Disease module”). Our model was calibrated to observed (2013 to 2019) and future (2020 to 2043) non-cardiovascular (CVD) mortality rates projected with a functional demographic model [38]. Epidemiological and economic outputs of the model on a population level are highly flexible and aggregated from individual life courses. We mainly report (incident) cases and (prevalent) case-years prevented/postponed (i.e., either completely prevented or delayed by 1 or more years). A detailed technical description of the modelling process is given in Methods A in S1 Appendix.

Health-related medical, patient time, and productivity costs

We estimated medical, patient time, and productivity costs related to morbidity and mortality over the simulation period based on the life course of synthetic individuals. Economic impacts were calculated from healthcare and societal perspectives following contemporary economic evaluation guidelines using a human capital approach [60,61]. We assessed formal health sector costs by applying medical costs for the treatment of stroke, CHD, and T2DM from the newest available national evidence to each incident or prevalent case year [62,63]. In the societal perspective, we additionally assessed informal health sector (i.e., patient time costs for T2DM self-management and health services use) and productivity costs (i.e., sick leave days and early retirement associated with stroke and T2DM and premature death) [64]. These were valued according to published national estimates, except premature death costs that were calculated using average annual gross wages including fringe benefits [65–68]. Medical and patient time costs were applied until death, while productivity losses accumulated until the German retirement age of 65 years. We inflated health sector costs, informal health sector costs, and productivity losses to 2022 prices using the German medical sector price index and the German labour cost index (see Methods A in S1 Appendix under “Health economics module”) [69,70]. All costs were discounted at 3% per year [71].

Quality-adjusted life years

Cumulative quality-adjusted life years (QALYs) over the simulation period were estimated taking the health-related quality of life of synthetic individuals, including morbidity and mortality, over their life course into account. For this, we re-estimated recently published national health utility decrements accounting for age, sex, BMI, stroke, CHD, and T2DM to improve consistency with our analysis (see Methods A in S1 Appendix under “Health economics module”) [72]. QALYs were discounted at 3% per year [71].

Uncertainty and sensitivity analyses

IMPACT_NCD incorporates stochastic (first-order) and parameter (second-order) uncertainty, as well as individual heterogeneity with extensive probabilistic (Monte Carlo) uncertainty analyses (PUA) using 500 iterations [73,74]. We also assessed structural uncertainty in the predicted policy impact with regards to BMI-independent effects of SSBs by re-estimating all scenarios using only BMI-mediated effects (Fig 1) [74].

We conducted several sensitivity analyses to contextualise our results by providing further comparison scenarios and varying important policy-related assumptions. We modelled: (1) impacts of observed voluntary reformulation of SSBs by industry (i.e., 2% per 6 years) [15]; (2) the tiered tax scenario with reformulation by 10%; (3) tax rates of 10% and 30% for the “ad valorem tax” scenario; (4) the “ad valorem tax” scenario without substitution effects to fruit juice (i.e., setting the cross-price elasticity to 0); (5) the impact of price changes on SSB consumption with a meta-analytic estimate [75]; (6) a maximum impact scenario that combines reformulation and consumption reduction; and (7) varied discount rates for costs and QALYs (0%, 5%, and 10%). Further details on uncertainty and sensitivity analyses are available in Methods A and B in S1 Appendix.

Model validation

We performed extensive analyses to validate IMPACT_NCD according to current guidelines [29]: (1) To ensure internal and face validity, the computational implementation, model outputs, and structure were discussed during meetings among the author group. We also compared inputs and model outputs of disease-specific prevalence and incidence and assessed the ability of the model to track past observed and projected mortality (Fig S–AD in S1 Appendix). (2) To assess external validity, we compared simulated disease-specific epidemiological data from the baseline scenario to comparable external information not used to inform model inputs (Fig AE–AM in S1 Appendix). (3) To cross-validate the predicted policy impact, we modelled all policy scenarios with an adapted version of PRIMEtime, which is a discrete-time proportional multistate life table Markov cohort model. Due to PRIMEtime’s preexisting structure, cross-validation was performed for the analysis including only BMI-mediated effects. As cross-validation targets, we used disease-specific cases prevented/postponed and QALYs. We minimised potential differences between models, by generating exposure and epidemiological inputs for PRIMEtime based on the synthetic German population; using the same data sources where possible; and aligning preexisting disease risk parameters. Uncertainty in PRIMEtime was separately assessed with 1,000 PUA iterations and only includes parameter (second-order) uncertainty. Technical descriptions of PRIMEtime have been published previously [20,76]. See Methods B and C in S1 Appendix for an overview of PRIMEtime and an extensive description of the cross-validation.

Patient and public involvement

No patients or members of the public were involved in the design and conduct of this study or the interpretation of its results. The results of this study will be shared as policy briefs with public representatives. We acknowledge that this analysis would not have been possible without data from participants of the respective cohort and survey studies.

Health impact of SSB taxation scenarios

A 20% ad valorem tax (scenario: “ad valorem tax”) on SSBs could prevent/postpone around 1,900 cases of stroke (95%-UI [0, 4,500]), 39,200 cases of CHD (95%-UI [21,100, 58,100]), 132,100 cases of T2DM (95%-UI [61,700, 202,900]), and 31,600 (95%-UI [−5,400, 72,600]) cases of obesity, compared to the counterfactual without the policy, and 1,109,300 (95%-UI [481,700, 1,838,200]) case-years lived with T2DM and 733,800 (95%-UI [99,600, 1,431,500]) case-years lived with obesity could be mitigated (Table 2).

Expanding the tax to fruit juice (scenario: “extended ad valorem tax”) would lead to increased overall health gains (cases prevented/postponed: 4,500 for stroke, 95%-UI [1,900, 8,500]; 45,800 for CHD, 95%-UI [27,500, 66,200]; 190,800 for T2DM, 95%-UI [112,000, 269,700]) (Table 2), and the largest impact on obesity, particularly among women due to their relatively high fruit juice consumption (Methods A in S1 Appendix under “Exposure module”). Obesity cases prevented/postponed would increase to 109,700 (95%-UI [70,400, 162,200]) for men and 50,500 (95%-UI [22,100, 80,900]) for women (Table X in S1 Appendix).

In the “tiered tax” scenario, health impacts for CHD and T2DM were substantially higher than in the “(extended) ad valorem tax” scenarios. Here, the cases prevented/postponed increased to 3,400 for stroke (95%-UI [800, 7,100]), 69,800 for CHD (95%-UI [38,800, 101,900]), 244,100 for T2DM (95%-UI [118,200, 365,300]), and 72,300 (95%-UI [36,400, 105,500]) for obesity (Table 2).

For all scenarios, the policy impact was highest among men due to their higher baseline SSB consumption, particularly in younger ages. Most cases of stroke, CHD, and T2DM would be prevented in the age groups below 70 years (Table W in S1 Appendix).

Economic impact of SSB taxation scenarios

Healthcare costs saved, productivity loss averted and QALYs gained over the 20-year simulation period were substantial in all scenarios and mirror the reported health impacts in their relative magnitude between policy scenarios and distribution according to sex and age (Table 2, Tables W and X in S1 Appendix). All 3 policy scenarios were cost-saving and dominant from healthcare and societal perspectives compared to their counterfactual without SSB taxation (Fig 2). Cumulative cost savings over time per scenario and perspective are shown in Fig 3. The total QALYs gained were 106,000 (95%-UI [57,200, 107,100]) for the “ad valorem tax,” 252,400 (95%-UI [176,700, 325,800]) for the “extended ad valorem tax,” and 192,300 (95%-UI [130,100, 254,200]) for the “tiered tax.” We estimated that from a healthcare perspective €2,262 million (95%-UI [€1,189, €3,596]) costs could be saved with the “ad valorem tax”; €3,141 million (95%-UI [1,942, 4,568]) with the “extended ad valorem tax”; and €3,850 million (95%-UI [€2,070, €6,075]) with the “tiered tax.” The largest share of disease-specific costs saved in all scenarios was due to T2DM (Table 2). From a societal perspective, economic gains were many times bigger (€9,584 million, 95%-UI [€4,714, €15,304] savings for the “ad valorem tax”; €11,827 million, 95%-UI [€6,702, €17,887] for the “extended ad valorem tax”; €16,013 million, 95%-UI [€8,090, €25,500] for the “tiered tax”). Productivity gains were largely determined by the prevention of premature deaths and T2DM-related sick leave and time costs (Table 2).

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Fig 3. Cumulative costs saved through SSB taxation from 2023 to 2043 by scenario and health economic perspective.

Line plots of median cumulative costs saved in €-millions from 2023 to 2043 as a consequence of SSB taxation scenarios stratified health economic perspective. Shaded areas indicate 95%-uncertainty intervals. *“Ad valorem tax” refers to a 20% ad valorem tax on SSBs with a pass-through to consumers of 82% (for details, see section “Sugar-sweetened beverage taxation scenarios”). ^#“Extended ad valorem tax” refers to a 20% ad valorem tax on SSBs and fruit juice with a pass-through to consumers of 82% (for details, see section “Sugar-sweetened beverage taxation scenarios”). ^‡“Tiered tax” refers to a tiered tax on SSBs similar to the UK SDIL that leads to a reduction in SSB sugar content by 30% through reformulation (for details, see section “Sugar-sweetened beverage taxation scenarios”). SSB, sugar-sweetened beverages; UK SDIL, United Kingdom Soft Drinks Industry Levy.

https://doi.org/10.1371/journal.pmed.1004311.g003

Validation results

IMPACT_NCD performed very well in internal and external validation analyses (Fig S–AM in S1 Appendix). In the cross-validation with PRIMEtime, we found that both models estimated similar health impacts, supporting the overall robustness of our findings. Uncertainty in IMPACT_NCD was generally higher than in PRIMEtime but for all scenarios and outcomes, uncertainty intervals overlapped (Fig 4, Fig AN and AO in S1 Appendix).

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Fig 4. Cross-validation results of IMPACT_NCD Germany and PRIMEtime with key health outcomes.

Horizontal bar chart comparing cross-validation outcomes for (A) CHD cases prevented/postponed, (B) stroke cases prevented/postponed, (C) type 2 diabetes cases prevented/postponed, and (D) QALYs gained between the IMPACT_NCD microsimulation (purple) and the PRIMEtime cohort model (green). Only BMI-mediated exposure pathways are modelled. Error bars indicate 95%-uncertainty intervals. “Ad valorem tax” refers to a 20% ad valorem tax on SSBs with a pass-through to consumers of 82% (for details, see section “Sugar-sweetened beverage taxation scenarios”). “Extended ad valorem tax” refers to a 20% ad valorem tax on SSBs and fruit juice with a pass-through to consumers of 82% (for details, see section “Sugar-sweetened beverage taxation scenarios”). “Tiered tax” refers to a tiered tax on SSBs similar to the UK SDIL that leads to a reduction in SSB sugar content by 30% through reformulation (for details, see section “Sugar-sweetened beverage taxation scenarios”). BMI, body mass index; NCD, noncommunicable disease; QALY, quality-adjusted life year; SSB, sugar-sweetened beverages; UK SDIL, United Kingdom Soft Drinks Industry Levy.

https://doi.org/10.1371/journal.pmed.1004311.g004

Structural uncertainty and sensitivity analyses

When excluding direct, BMI-independent effects of SSBs, the health and economic impact across all scenarios, particularly on T2DM and CHD, was considerably smaller (Fig 2, Table Y in S1 Appendix). Here, the largest health gains would be achieved in the “extended ad valorem tax” scenario (177,600 QALYs gained, 95%-UI [121,500, 242,300]; €2,704 million costs saved from societal perspective, 95%-UI [€1,345, €5,002]). However, considering only SSBs, the “tiered tax” would on average produce larger benefits (73,500 QALYs gained, 95%-UI [51,600, 98,400]; €1,292 million costs saved from societal perspective, 95%-UI [€620, €2,292]) than estimated in the “ad valorem tax” scenario (32,600 QALYs gained, 95%-UI [3,700, 60,700]; €589 million costs saved from societal perspective, 95%-UI [€128, €1,352]). A detailed comparison for all health and economic outcomes is available in Table Y in S1 Appendix.

Health and economic impacts changed sometimes significantly when varying the tax rate for the “ad valorem tax” scenario between 10% (change in QALYs: −43%) and 30% (+48%), excluding substitution effects from SSBs to fruit juice (+6%), or implementing the effect of taxation using a meta-analytic estimate (−25%); decreasing reformulation for the “tiered tax” to 10% (−66%); or varying the discount rate for costs and QALYs (−66% to +30% depending on the discount rate and scenario). We estimated that observed voluntary industry commitments to reduce sugar content in SSBs would only gain 16,400 QALYs and that a combined reformulation and consumption reduction of SSBs would lead with 322,200 QALYs gained to the highest impact (Tables T–V in S1 Appendix).