The impact of a sugar-sweetened beverages tax on oral health and costs of dental care in Australia

The impact of a sugar-sweetened beverages tax on oral health and costs of dental care in Australia Abstract Background Despite a clear causal link between frequent consumption of sugar-sweetened beverages (SSBs) and dental disease, little is known about the implications of a tax on SSBs in the context of oral health. The aim of our study was to estimate the impacts of a SSB tax on the Australian population in the context of oral health outcomes, dental care utilisation and associated costs. Methods We designed a cohort model that accounted for the consequences of the tax through the mechanisms of consumer response to price increase, the effect on oral health due to change in sugar intake, and the implications for dental care use. Results Our results indicate that in the adult population an ad valorem tax of 20% would lead to a reduction in decayed, missing and filled teeth (DMFT) by 3.9 million units over 10 years, resulting in cost savings of A$666 million. Scenario analyses show that the outcomes are sensitive to the choice of the time horizon, tax rate, price elasticity of demand for SSBs, and the definition of target population. Conclusion We found that the total and per-person consequences of SSB tax were considerable, both in terms of dental caries (tooth decay) averted and dental care avoided. These results have to be compounded with the implications of SSB tax for other aspects of health and health care, especially in the context of chronic diseases. On the other hand, the improved outcomes have to be weighted against a welfare loss associated with introducing a tax. Introduction Frequent and excessive consumption of sugar-sweetened beverages (SSBs), due to their sugar and calorie content, constitutes a major risk factor for numerous chronic conditions including obesity and diabetes.1 In an attempt to address the increasingly pressing issue, the WHO has recently encouraged the use of a sugar tax, urging governments to use their fiscal policies to manage the overconsumption of such drinks. A number of countries including Hungary, Mexico and selected localities in the United States have already imposed a form of SSB tax.2 Further governments are considering its introduction, notably South Africa and the UK. The biological mechanisms linking sugar consumption to a range of non-communicable diseases are well established.3 The research of SSB tax implications for sugar consumption and health outcomes has so far focussed on obesity,4 diabetes,5 stroke6 and broad health and health sector impacts,7,8 also taking into consideration the socio-economic status of the persons affected.9 Sugar consumption has also been demonstrated to contribute to dental problems: tooth decay10 and tooth loss11 which in turn can affect overall health status and quality of life.12 In spite of this, the evidence of SSB tax effectiveness in this area is currently limited. Two recently published studies, in Germany and the UK, demonstrated that various definitions of SSB tax can be effective in decreasing the consumption of sugar and reducing the incidence of caries.13,14 It is difficult to generalise the results of these studies due to the differences in the health and economic systems as well as population factors among countries. Consequently, the aim of our study was to quantitatively explore the implications of SSB tax for oral health outcomes and dental care service utilisation in the Australian setting. Methods Model approach Our cohort model was designed to capture the chain of events triggered by the government imposing a SSB tax, leading to changes in sugar consumption and resources required for dental treatment (figure 1). The broad approach was to establish a baseline sugar intake (added, discretionary and in the form of SSBs) and calculate how it would change under a tax scenario taking into account the consumer response approximated by the price elasticity of consumption. We then used evidence of the causal link between sugar consumption and caries incidence expressed through decayed, missing and filled teeth (DMFT) to model caries avoided as a result of the new, lower level of sugar intake. The final step was to estimate cost savings associated with the introduction of SSB tax using the mean cost of treating an instance of caries. All incremental values were calculated relative to the comparator of no tax on SSB which represented the status quo. Figure 1 View largeDownload slide Model schematic Figure 1 View largeDownload slide Model schematic Population The baseline inputs included the age-gender composition of the population in single year groups15 and corresponding background mortality rates used in the calculation of effects accumulating in future time periods.16 To increase the precision of our predictions mortality was accounted for using half-cycle correction. The population size was 18.7 m in the base case (≥18 years), and 14.6 m (≥30 years) and 22.3 m (≥6 years) explored in sensitivity analyses. Consumption Consumption of added sugar and SSB consumption was based on the Australian Bureau of Statistics (ABS) Australian Health Survey (AHS)17 and modelled in age groups defined correspondingly to the input data, i.e. ages 6–8 years, 9–13, 14–18, 19–30, 31–50, 51–70 and above 71. Consumption was modelled separately for males and females, thus accounting for any systematic differences. Three categories of products (fruit and vegetable juices and drinks, cordials and soft drinks including flavoured mineral waters) added up to 90% of added sugar consumption in non-alcoholic beverages. Furthermore, our analysis included discretionary consumption only. According to ABS AHS data this accounted for 98% and 96% of all added sugar consumption in males and females, respectively. Effect of tax on consumption Three parameters were considered when factoring in the effect of tax on consumption. First, the tax rate, which was assumed 20% in the base (a de facto standard rate used in previous studies)13,18 and the values 10 and 30% tested in sensitivity analyses. Second, the pass-on rate reflected the proportion of price increase that would be passed on to consumers, as opposed to being absorbed by the supply side. In the base model this was assumed to be 100%, i.e. the entire tax amount was added to the price faced by consumers. We also explored the values of 50 and 80%. Third, to account for the consumer response to price increases, we employed the concept of price elasticity of demand defined as a percentage change in the quantity demanded over the percentage chance in price. In the base case we used the most recent SSB-specific point estimate for Australia19 which produced the price elasticity of –0.95, i.e. for every per cent of price increase the amount consumed would decrease by 0.95%. We also explored two alternative values for this parameter: an estimate from another Australian study with a low estimate serving also as a lower bound (0.63)20 and a result of an international literature review serving as an upper bound (1.299).21 Regarding the latter, we noted that a number of international studies published since 201322,23 offered estimates that corroborated this higher value. The scenarios in which we explored the effects of SSB tax on the population of children and teenagers aged 6 and above were performed under the assumption that parents’ preferences, which are instrumental in consumption decisions, can be extrapolated and applied in children. This assumption followed a consideration of a number of mechanisms through which parents influence their offspring’s healthy and unhealthy food consumption patterns and include active guidance, availability and modelling.24 Oral health consequences of sugar consumption Baseline DMFT accumulation occurring for all background reasons was modelled using data reported by the Australian Institute for Health and Welfare.25 Average DMFT increase rates were calculated from DMFT values available in age groups (Tables 2.4 and 2.6 of the report) adjusted for the proportion of males to females in the population. The relationship between the amount of sugar consumed and the occurrence of caries was informed by the study by Bernabé et al.26 who reported a coefficient of 0.1 DMFT per additional 10 g of daily sugar consumption in a sample of Finnish adults aged 30 years and over (n = 1702). The scenarios in which we estimated the effects of SSB tax in two broader groups of the Australian population required an extrapolation of these results. The scenario including ages 18 and above was carried out under the assumption that the effects of sugar consumption were the same in the group aged 18–29 as in the group aged 30 and over. In the scenario that included children and teenagers (ages 6 and more) we extrapolated the causal link based on the study by Rugg-Gunn et al.27 who reported a coefficient similar to that in Bernabé et al. (+1.28 DMFT per 100 g of sugar) in a 2-year study of dietary habits and caries increment in English children and adolescents (n = 405). Treatment cost We assumed a societal perspective for costs, i.e. costs of treatment were considered regardless to whom they accrue. However, only direct costs of treating DMFT were taken into equation. Consequently, potential costs taking place outside of the dentist’s room such as those associated with travel, waiting time and additional pain relief were not considered. The calculation of the expected cost of treating an added lesion was based on the weighted average of dental care in the public28 and private29 settings of restoring one or two anterior or posterior surfaces.13 The proportions of services used in public and private settings were sourced from an AIHW report30 (Table 3.6). The weighted mean cost of treating a carious lesion, used in the base model, was A$161.43. Alternative values we explored were A$142.05 (a lower bound value defined as the public sector cost) and A$165.01 (a higher bound value corresponding to costs as per the private sector only). The dental sector-specific price inflation31 was used to index 2014 DVA costs forward to reflect today’s values. It also served as a basis for indexing dental costs occurring in the future. In the base case, we assumed that in future years dental prices would increase at the same rate as they have between June 2014 and June 2017, an average annual increase of 1.5%. In sensitivity analyses we allowed this parameter to vary between 0% and 5%. Time horizon and discounting To allow comparability with existing studies, in the base case we assumed the time horizon of 10 years. Alternative values of 1 and 20 years were tested in scenario analyses. Future costs and health outcomes were discounted at 5% in the base case and 0% and 3% in sensitivity analyses. Sensitivity analyses Sensitivity and scenario analyses were performed on all key model inputs as discussed above (table 1). In order to further test the stability of results, we ran probabilistic sensitivity analyses on key sugar consumption parameters: the daily intake of added sugars and the proportion of discretionary added sugar intake from SSBs. Here, in each of the 2000 model cycles, the input parameters were drawn from a normal distribution with their means and standard deviations as reported in the ABS AHS.17 Table 1 Summary of parameter values used in the model Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Table 1 Summary of parameter values used in the model Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Results Base case The base model results, indicated in table 2, suggest that imposing a 20% ad valorem tax, compared with maintaining the status quo, would lead to 3.89 million DMFT units averted in the Australian adult population over 10 years (outcomes occurring in the future discounted at 5%). The corresponding cost savings associated with dental care avoided, assuming all DMFTs would get treated, amounted to A$666 million over the 10-year period. In per-person terms, considering only the analysed population, these values were 0.21 DMFT averted and A$35.61 saved in dental care expenditure, respectively. Table 2 Base case results Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 a Millions of DMFT except last column which is reported in units. b A$millions except last column which is reported in A$. Table 2 Base case results Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 a Millions of DMFT except last column which is reported in units. b A$millions except last column which is reported in A$. One-way sensitivity and scenario analyses Considering the tested parameter ranges, the model was most sensitive to the choice of the time horizon. DMFT averted ranged from 0.53 to 5.76m, and costs avoided between A$85m and A$1039m, for the time horizon set at 1 and 20 years, respectively. The choice of the tax rate, tested for values between 10 and 30%, also showed a considerable impact on model outcomes, with DMFT averted spanning between 1.95 and 5.84m, and costs avoided between A$333m and A$1bn. The source of price elasticity of demand for SSBs, and the choice of model population, had comparable impact on the results, with DMFT averted between 2.5 and 5.3m, and cost savings within the range of A$430m and A$911m. Changing the values of the remaining parameter had a smaller impact on the outcomes. A complete list of analysed scenarios and their results is presented in table 3. Table 3 Results of sensitivity and scenario analyses One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 a Millions of DMFT units. b A$ millions. Table 3 Results of sensitivity and scenario analyses One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 a Millions of DMFT units. b A$ millions. Probabilistic sensitivity analyses Probabilistic sensitivity analyses produced approximately normal distributions that, for both DMFT averted and costs avoided, centred around the mean values similar to those reported in the base case. The means were 3.9 m (SD 1.11m) for DMFT and A$669m (SD A$189m) for cost savings. The 95% confidence interval for DMFT was between 6.08 and 1.73m; the corresponding values for cost savings were A$1042m and A$296m. Discussion Excessive consumption of discretionary sugar is a topical problem, both nationally and internationally.2,3 The SSB tax is seen as a public health policy tool key to addressing this issue, and as such it is likely to be considered at some point by most governments. This matter has recently triggered a policy debate in Australia,32 a country that has been at the forefront of advancing public health through fiscal policy, as in the case of tobacco and alcohol.33 Self-regulation and public–private partnerships have been argued to be insufficient responses, therefore government regulation and market intervention have been recommended to curb the harmful effects of processed food and drink.34 In practice, the solution is likely to be a compromise resulting from the political process and stakeholder consultations, as is the case in the UK.35 Existing evaluations of SSB tax have so far been centred on the problems of obesity, diabetes and other chronic conditions.7,8 Despite clear connotations of oral health and health care with sugar consumption, the implications of a SSB tax in this area for have been largely missing from the view. Our study closes this gap by producing a piece of evidence that, other than informing the oral health policy-making itself, enables a more complete account of SSB tax implications for health and health care across the board. Our findings suggest that a change in sugar consumption resulting from a SSB tax leads to a material reduction of dental caries and associated health care costs, in the base case amounting to 3.9 million fewer DMFT units due to decay averted, and cost savings of A$666 million, over 10 years. The actual total societal costs avoided may be higher than the estimates due to some direct (e.g. travel to the point of care), indirect (e.g. lost productivity) and intangible (e.g. pain) costs being excluded from the analysis. On the other hand, the cost savings represent an upper bound estimate due to our model relying on the assumption that all lesions would eventually be treated. In reality, a proportion of the decay may not be treated and instead removed with teeth for other reasons such as trauma and orthodontics. In contrast, dental caries not accumulated or not averted due to mortality was implicitly modelled. The comparability of our results to previous studies is limited due to distinct approaches to defining a SSB tax. Notably, the implications of introducing a 20% ad valorem tax from our model cannot be benchmarked to results of the UK study by Briggs et al.14 who defined the tax as a tiered levy based on sugar content per 100 ml. However, our estimates of 0.21 DMFT per person averted, translating into cost savings of A$35.61 per capita over the 10-year period, validate well against the German study by Schwendicke et al.13 who report that depending on the age-gender group DMFT averted of up to 0.46 units and cost savings up to €14 (A$22) per person are possible. Furthermore, they find that for most groups the cost savings and health benefits are far lower or even negative in the case of older females. Schwendicke et al. attribute the unexpected finding in older females to an increased consumption of other sugar beverages such as juices which results from the lack of age-specific price elasticity estimates. Therefore, they highlight the importance of using age-specific elasticity estimates when modelling the effects of a SSB tax. Such data were unavailable in the Australian setting. Further benefits of a SSB tax are an increased consumption of untaxed beverages, especially plain water, which replaces the previous consumption of SSB.36 Consequently, a positive impact on obesity and associated comorbidities is also likely.37 Importantly, it has been shown that a SSB tax has a greater impact in younger than older adults13 as well as in low-income rather than mid- to high-income households13,23 This is also where the greater disease burden is found.38 Given that additional tax revenue would be generated by means of the tax, it would be advantageous if those funds were earmarked for programmes that further support combating the adverse consequences of sugar consumption. As empirical evidence of the implementation of SSB taxes, as opposed to simulation studies, becomes available, validity can be reassessed and improved. Especially important are possible substitution effects in individual and industry responses such as reformulation, price changes and observed values of the pass-on rate of the price increase.14 These factors have been accounted for in our model at their face value; however, the actual reactions of consumers and the industry remain to be seen. Finally, the effectiveness of SSB taxation in improving health outcomes has to be judged in the context of the market distortion caused by the introduction of a tax. The welfare of many consumers whose sugar consumption is moderate and does not pose a motive for a government intervention would be adversely affected by soft drink price increases. Such a distortion leads to market inefficiency and reduces societal welfare by a measure referred to by economists as the ‘deadweight loss’.39 Because every member of the society is affected by such a tax, its overall costs may be high. Consequently, the desirability of implementing a SSB tax may require a high societal willingness to pay for health gain or a policy consensus that goes beyond cost-benefit considerations.40 Conclusion The implementation of a 20% SSB tax is likely to result in reductions in tooth decay and corresponding cost savings in dental care that are non-negligible from both the national and personal perspectives. The tax implications in the context of oral health should be considered together with its implications for other aspects of health. This will allow for a complete assessment of SSB tax effectiveness vis-à-vis the welfare loss that it introduces. Funding P.M.S. received financial support from the New Staff Research Start-Up Fund, Faculty of Business, Economics and Law, The University of Queensland. Conflicts of interest: None declared. Key points A 20% ad valorem tax on sugar-sweetened beverages would result in an estimated 3.89 million decayed-missing-filled teeth (units) averted over 10 years, with corresponding cost savings of A$666 million. The results were most sensitive to the assumed tax rate, reactions of consumers and responses from the industry. Our findings add to the broader context of the health effects of taxation policies on sugar-sweetened beverages, with implications for numerous chronic conditions previously demonstrated in the literature. Using an ad valorem tax as a policy response has several drawbacks as it affects persons whose intake of added sugar is moderate and leads to market inefficiency. References 1 Amine E , Baba N , Belhadj M , et al. Diet, nutrition and the prevention of chronic diseases. World Health Organization Technical Report Series. 2003 : 916 . 2 World Health Organization . Fiscal Policies for Diet and Prevention of Noncommunicable Diseases . 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Available at: https://ama.com.au/position-statement/nutrition-2018 (10 January 2018, date last accessed). 33 Davoren S , Mills C , Jones A . National and Regional Perspectives of NCD Risk Regulation: Australia. In: Voon T , Mitchell AD , Liberman J , editors. Regulating Tobacco, Alcohol and Unhealthy Foods: The Legal Issues . UK : Routledge , 2014 . 34 Moodie R , Stuckler D , Monteiro C , et al. Profits and pandemics: prevention of harmful eff ects of tobacco, alcohol, and ultra-processed food and drink industries . Lancet 2013 ; 381 : 670 – 9 . Google Scholar CrossRef Search ADS PubMed 35 Jones CM . The UK sugar tax – a healthy start? Br Dent J 2016 ; 221 : 59 – 60 . Google Scholar CrossRef Search ADS PubMed 36 Colchero MA , Guerrero-López CM , Molina M , Rivera JA . Beverages sales in Mexico before and after implementation of a sugar sweetened beverage tax . PLoS One 2016 ; 11 : e0163463 . Google Scholar CrossRef Search ADS PubMed 37 Basu S , McKee M , Galea G , Stuckler D . Relationship of soft drink consumption to global overweight, obesity, and diabetes: a cross-national analysis of 75 countries . Am J Public Health 2013 ; 103 : 2071 – 7 . Google Scholar CrossRef Search ADS PubMed 38 Lalloo R , Jamieson LM , Ha D , Luzzi L . Inequalities in tooth decay in Australian children by neighbourhood characteristics and indigenous status . J Health Care Poor Underserved 2016 ; 27 : 161 – 77 . Google Scholar CrossRef Search ADS 39 Byrnes J , Petrie DJ , Doran CM , Shakeshaft A . The efficiency of a volumetric alcohol tax in Australia . Appl Health Econ Health Policy 2012 ; 10 : 37 – 49 . Google Scholar CrossRef Search ADS PubMed 40 Lusk JL , Schroeter C . When do fat taxes increase consumer welfare? Health Econ 2012 ; 21 : 1367 – 74 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

The impact of a sugar-sweetened beverages tax on oral health and costs of dental care in Australia

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
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1101-1262
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1464-360X
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10.1093/eurpub/cky087
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Abstract

Abstract Background Despite a clear causal link between frequent consumption of sugar-sweetened beverages (SSBs) and dental disease, little is known about the implications of a tax on SSBs in the context of oral health. The aim of our study was to estimate the impacts of a SSB tax on the Australian population in the context of oral health outcomes, dental care utilisation and associated costs. Methods We designed a cohort model that accounted for the consequences of the tax through the mechanisms of consumer response to price increase, the effect on oral health due to change in sugar intake, and the implications for dental care use. Results Our results indicate that in the adult population an ad valorem tax of 20% would lead to a reduction in decayed, missing and filled teeth (DMFT) by 3.9 million units over 10 years, resulting in cost savings of A$666 million. Scenario analyses show that the outcomes are sensitive to the choice of the time horizon, tax rate, price elasticity of demand for SSBs, and the definition of target population. Conclusion We found that the total and per-person consequences of SSB tax were considerable, both in terms of dental caries (tooth decay) averted and dental care avoided. These results have to be compounded with the implications of SSB tax for other aspects of health and health care, especially in the context of chronic diseases. On the other hand, the improved outcomes have to be weighted against a welfare loss associated with introducing a tax. Introduction Frequent and excessive consumption of sugar-sweetened beverages (SSBs), due to their sugar and calorie content, constitutes a major risk factor for numerous chronic conditions including obesity and diabetes.1 In an attempt to address the increasingly pressing issue, the WHO has recently encouraged the use of a sugar tax, urging governments to use their fiscal policies to manage the overconsumption of such drinks. A number of countries including Hungary, Mexico and selected localities in the United States have already imposed a form of SSB tax.2 Further governments are considering its introduction, notably South Africa and the UK. The biological mechanisms linking sugar consumption to a range of non-communicable diseases are well established.3 The research of SSB tax implications for sugar consumption and health outcomes has so far focussed on obesity,4 diabetes,5 stroke6 and broad health and health sector impacts,7,8 also taking into consideration the socio-economic status of the persons affected.9 Sugar consumption has also been demonstrated to contribute to dental problems: tooth decay10 and tooth loss11 which in turn can affect overall health status and quality of life.12 In spite of this, the evidence of SSB tax effectiveness in this area is currently limited. Two recently published studies, in Germany and the UK, demonstrated that various definitions of SSB tax can be effective in decreasing the consumption of sugar and reducing the incidence of caries.13,14 It is difficult to generalise the results of these studies due to the differences in the health and economic systems as well as population factors among countries. Consequently, the aim of our study was to quantitatively explore the implications of SSB tax for oral health outcomes and dental care service utilisation in the Australian setting. Methods Model approach Our cohort model was designed to capture the chain of events triggered by the government imposing a SSB tax, leading to changes in sugar consumption and resources required for dental treatment (figure 1). The broad approach was to establish a baseline sugar intake (added, discretionary and in the form of SSBs) and calculate how it would change under a tax scenario taking into account the consumer response approximated by the price elasticity of consumption. We then used evidence of the causal link between sugar consumption and caries incidence expressed through decayed, missing and filled teeth (DMFT) to model caries avoided as a result of the new, lower level of sugar intake. The final step was to estimate cost savings associated with the introduction of SSB tax using the mean cost of treating an instance of caries. All incremental values were calculated relative to the comparator of no tax on SSB which represented the status quo. Figure 1 View largeDownload slide Model schematic Figure 1 View largeDownload slide Model schematic Population The baseline inputs included the age-gender composition of the population in single year groups15 and corresponding background mortality rates used in the calculation of effects accumulating in future time periods.16 To increase the precision of our predictions mortality was accounted for using half-cycle correction. The population size was 18.7 m in the base case (≥18 years), and 14.6 m (≥30 years) and 22.3 m (≥6 years) explored in sensitivity analyses. Consumption Consumption of added sugar and SSB consumption was based on the Australian Bureau of Statistics (ABS) Australian Health Survey (AHS)17 and modelled in age groups defined correspondingly to the input data, i.e. ages 6–8 years, 9–13, 14–18, 19–30, 31–50, 51–70 and above 71. Consumption was modelled separately for males and females, thus accounting for any systematic differences. Three categories of products (fruit and vegetable juices and drinks, cordials and soft drinks including flavoured mineral waters) added up to 90% of added sugar consumption in non-alcoholic beverages. Furthermore, our analysis included discretionary consumption only. According to ABS AHS data this accounted for 98% and 96% of all added sugar consumption in males and females, respectively. Effect of tax on consumption Three parameters were considered when factoring in the effect of tax on consumption. First, the tax rate, which was assumed 20% in the base (a de facto standard rate used in previous studies)13,18 and the values 10 and 30% tested in sensitivity analyses. Second, the pass-on rate reflected the proportion of price increase that would be passed on to consumers, as opposed to being absorbed by the supply side. In the base model this was assumed to be 100%, i.e. the entire tax amount was added to the price faced by consumers. We also explored the values of 50 and 80%. Third, to account for the consumer response to price increases, we employed the concept of price elasticity of demand defined as a percentage change in the quantity demanded over the percentage chance in price. In the base case we used the most recent SSB-specific point estimate for Australia19 which produced the price elasticity of –0.95, i.e. for every per cent of price increase the amount consumed would decrease by 0.95%. We also explored two alternative values for this parameter: an estimate from another Australian study with a low estimate serving also as a lower bound (0.63)20 and a result of an international literature review serving as an upper bound (1.299).21 Regarding the latter, we noted that a number of international studies published since 201322,23 offered estimates that corroborated this higher value. The scenarios in which we explored the effects of SSB tax on the population of children and teenagers aged 6 and above were performed under the assumption that parents’ preferences, which are instrumental in consumption decisions, can be extrapolated and applied in children. This assumption followed a consideration of a number of mechanisms through which parents influence their offspring’s healthy and unhealthy food consumption patterns and include active guidance, availability and modelling.24 Oral health consequences of sugar consumption Baseline DMFT accumulation occurring for all background reasons was modelled using data reported by the Australian Institute for Health and Welfare.25 Average DMFT increase rates were calculated from DMFT values available in age groups (Tables 2.4 and 2.6 of the report) adjusted for the proportion of males to females in the population. The relationship between the amount of sugar consumed and the occurrence of caries was informed by the study by Bernabé et al.26 who reported a coefficient of 0.1 DMFT per additional 10 g of daily sugar consumption in a sample of Finnish adults aged 30 years and over (n = 1702). The scenarios in which we estimated the effects of SSB tax in two broader groups of the Australian population required an extrapolation of these results. The scenario including ages 18 and above was carried out under the assumption that the effects of sugar consumption were the same in the group aged 18–29 as in the group aged 30 and over. In the scenario that included children and teenagers (ages 6 and more) we extrapolated the causal link based on the study by Rugg-Gunn et al.27 who reported a coefficient similar to that in Bernabé et al. (+1.28 DMFT per 100 g of sugar) in a 2-year study of dietary habits and caries increment in English children and adolescents (n = 405). Treatment cost We assumed a societal perspective for costs, i.e. costs of treatment were considered regardless to whom they accrue. However, only direct costs of treating DMFT were taken into equation. Consequently, potential costs taking place outside of the dentist’s room such as those associated with travel, waiting time and additional pain relief were not considered. The calculation of the expected cost of treating an added lesion was based on the weighted average of dental care in the public28 and private29 settings of restoring one or two anterior or posterior surfaces.13 The proportions of services used in public and private settings were sourced from an AIHW report30 (Table 3.6). The weighted mean cost of treating a carious lesion, used in the base model, was A$161.43. Alternative values we explored were A$142.05 (a lower bound value defined as the public sector cost) and A$165.01 (a higher bound value corresponding to costs as per the private sector only). The dental sector-specific price inflation31 was used to index 2014 DVA costs forward to reflect today’s values. It also served as a basis for indexing dental costs occurring in the future. In the base case, we assumed that in future years dental prices would increase at the same rate as they have between June 2014 and June 2017, an average annual increase of 1.5%. In sensitivity analyses we allowed this parameter to vary between 0% and 5%. Time horizon and discounting To allow comparability with existing studies, in the base case we assumed the time horizon of 10 years. Alternative values of 1 and 20 years were tested in scenario analyses. Future costs and health outcomes were discounted at 5% in the base case and 0% and 3% in sensitivity analyses. Sensitivity analyses Sensitivity and scenario analyses were performed on all key model inputs as discussed above (table 1). In order to further test the stability of results, we ran probabilistic sensitivity analyses on key sugar consumption parameters: the daily intake of added sugars and the proportion of discretionary added sugar intake from SSBs. Here, in each of the 2000 model cycles, the input parameters were drawn from a normal distribution with their means and standard deviations as reported in the ABS AHS.17 Table 1 Summary of parameter values used in the model Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Table 1 Summary of parameter values used in the model Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Parameter Base case Sensitivity analyses Tax rate 20% 10%, 30% Time horizon, years 10 1, 20 Price elasticity −0.95 −0.63, −1.299 Pass-on rate 100% 50%, 80% Discount rate 5% 0%, 3% Dental care price inflation 1.5% 0%, 5% Population, years of age ≥18 ≥6, ≥30 Cost of treatment per 1 unit of DMFT $161.43 $142.05, $165.01 Results Base case The base model results, indicated in table 2, suggest that imposing a 20% ad valorem tax, compared with maintaining the status quo, would lead to 3.89 million DMFT units averted in the Australian adult population over 10 years (outcomes occurring in the future discounted at 5%). The corresponding cost savings associated with dental care avoided, assuming all DMFTs would get treated, amounted to A$666 million over the 10-year period. In per-person terms, considering only the analysed population, these values were 0.21 DMFT averted and A$35.61 saved in dental care expenditure, respectively. Table 2 Base case results Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 a Millions of DMFT except last column which is reported in units. b A$millions except last column which is reported in A$. Table 2 Base case results Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 Outcome 20% tax No tax Δ Δ/person DMFT units accumulateda 54.04 57.93 –3.89 –0.21 Cost of treatmentb $9281 $9947 –$666 –$35.61 a Millions of DMFT except last column which is reported in units. b A$millions except last column which is reported in A$. One-way sensitivity and scenario analyses Considering the tested parameter ranges, the model was most sensitive to the choice of the time horizon. DMFT averted ranged from 0.53 to 5.76m, and costs avoided between A$85m and A$1039m, for the time horizon set at 1 and 20 years, respectively. The choice of the tax rate, tested for values between 10 and 30%, also showed a considerable impact on model outcomes, with DMFT averted spanning between 1.95 and 5.84m, and costs avoided between A$333m and A$1bn. The source of price elasticity of demand for SSBs, and the choice of model population, had comparable impact on the results, with DMFT averted between 2.5 and 5.3m, and cost savings within the range of A$430m and A$911m. Changing the values of the remaining parameter had a smaller impact on the outcomes. A complete list of analysed scenarios and their results is presented in table 3. Table 3 Results of sensitivity and scenario analyses One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 a Millions of DMFT units. b A$ millions. Table 3 Results of sensitivity and scenario analyses One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 One-way sensitivity analyses Parameter Value range DMFTa Costb Low value High value Range Low value High value Range Time horizon 1–20 years –0.53 –5.76 5.24 –$85 –$1039 $953 Tax rate 10–30% –1.95 –5.84 3.89 –$333 –$1000 $666 Price elasticity 0.63–1.299 –2.58 –5.32 2.74 –$442 –$911 $469 Population ≥6 to ≥30 years –5.20 –2.51 2.69 –$891 –$430 $461 Pass-on rate 50–% –1.95 –3.11 1.17 –$333 –$533 $200 Dental care price inflation 0–5% –3.89 –3.89 0.00 –$628 –$768 $139 Discount rate 0–3% –4.75 –4.20 0.55 –$819 –$721 $98 Cost of treatment $142.05–$165.01 –3.89 –3.89 0.00 –$586 –$681 $95 Probabilistic sensitivity analyses Outcome Median Mean SD Low High LCI 95% HCI 95% Δ DMFTa –3.91 –3.90 1.11 –7.76 –0.21 –6.08 –1.73 Δ Costb –$669 –$669 $189 –$1329 –$35 –$1042 –$296 a Millions of DMFT units. b A$ millions. Probabilistic sensitivity analyses Probabilistic sensitivity analyses produced approximately normal distributions that, for both DMFT averted and costs avoided, centred around the mean values similar to those reported in the base case. The means were 3.9 m (SD 1.11m) for DMFT and A$669m (SD A$189m) for cost savings. The 95% confidence interval for DMFT was between 6.08 and 1.73m; the corresponding values for cost savings were A$1042m and A$296m. Discussion Excessive consumption of discretionary sugar is a topical problem, both nationally and internationally.2,3 The SSB tax is seen as a public health policy tool key to addressing this issue, and as such it is likely to be considered at some point by most governments. This matter has recently triggered a policy debate in Australia,32 a country that has been at the forefront of advancing public health through fiscal policy, as in the case of tobacco and alcohol.33 Self-regulation and public–private partnerships have been argued to be insufficient responses, therefore government regulation and market intervention have been recommended to curb the harmful effects of processed food and drink.34 In practice, the solution is likely to be a compromise resulting from the political process and stakeholder consultations, as is the case in the UK.35 Existing evaluations of SSB tax have so far been centred on the problems of obesity, diabetes and other chronic conditions.7,8 Despite clear connotations of oral health and health care with sugar consumption, the implications of a SSB tax in this area for have been largely missing from the view. Our study closes this gap by producing a piece of evidence that, other than informing the oral health policy-making itself, enables a more complete account of SSB tax implications for health and health care across the board. Our findings suggest that a change in sugar consumption resulting from a SSB tax leads to a material reduction of dental caries and associated health care costs, in the base case amounting to 3.9 million fewer DMFT units due to decay averted, and cost savings of A$666 million, over 10 years. The actual total societal costs avoided may be higher than the estimates due to some direct (e.g. travel to the point of care), indirect (e.g. lost productivity) and intangible (e.g. pain) costs being excluded from the analysis. On the other hand, the cost savings represent an upper bound estimate due to our model relying on the assumption that all lesions would eventually be treated. In reality, a proportion of the decay may not be treated and instead removed with teeth for other reasons such as trauma and orthodontics. In contrast, dental caries not accumulated or not averted due to mortality was implicitly modelled. The comparability of our results to previous studies is limited due to distinct approaches to defining a SSB tax. Notably, the implications of introducing a 20% ad valorem tax from our model cannot be benchmarked to results of the UK study by Briggs et al.14 who defined the tax as a tiered levy based on sugar content per 100 ml. However, our estimates of 0.21 DMFT per person averted, translating into cost savings of A$35.61 per capita over the 10-year period, validate well against the German study by Schwendicke et al.13 who report that depending on the age-gender group DMFT averted of up to 0.46 units and cost savings up to €14 (A$22) per person are possible. Furthermore, they find that for most groups the cost savings and health benefits are far lower or even negative in the case of older females. Schwendicke et al. attribute the unexpected finding in older females to an increased consumption of other sugar beverages such as juices which results from the lack of age-specific price elasticity estimates. Therefore, they highlight the importance of using age-specific elasticity estimates when modelling the effects of a SSB tax. Such data were unavailable in the Australian setting. Further benefits of a SSB tax are an increased consumption of untaxed beverages, especially plain water, which replaces the previous consumption of SSB.36 Consequently, a positive impact on obesity and associated comorbidities is also likely.37 Importantly, it has been shown that a SSB tax has a greater impact in younger than older adults13 as well as in low-income rather than mid- to high-income households13,23 This is also where the greater disease burden is found.38 Given that additional tax revenue would be generated by means of the tax, it would be advantageous if those funds were earmarked for programmes that further support combating the adverse consequences of sugar consumption. As empirical evidence of the implementation of SSB taxes, as opposed to simulation studies, becomes available, validity can be reassessed and improved. Especially important are possible substitution effects in individual and industry responses such as reformulation, price changes and observed values of the pass-on rate of the price increase.14 These factors have been accounted for in our model at their face value; however, the actual reactions of consumers and the industry remain to be seen. Finally, the effectiveness of SSB taxation in improving health outcomes has to be judged in the context of the market distortion caused by the introduction of a tax. The welfare of many consumers whose sugar consumption is moderate and does not pose a motive for a government intervention would be adversely affected by soft drink price increases. Such a distortion leads to market inefficiency and reduces societal welfare by a measure referred to by economists as the ‘deadweight loss’.39 Because every member of the society is affected by such a tax, its overall costs may be high. Consequently, the desirability of implementing a SSB tax may require a high societal willingness to pay for health gain or a policy consensus that goes beyond cost-benefit considerations.40 Conclusion The implementation of a 20% SSB tax is likely to result in reductions in tooth decay and corresponding cost savings in dental care that are non-negligible from both the national and personal perspectives. The tax implications in the context of oral health should be considered together with its implications for other aspects of health. This will allow for a complete assessment of SSB tax effectiveness vis-à-vis the welfare loss that it introduces. Funding P.M.S. received financial support from the New Staff Research Start-Up Fund, Faculty of Business, Economics and Law, The University of Queensland. Conflicts of interest: None declared. Key points A 20% ad valorem tax on sugar-sweetened beverages would result in an estimated 3.89 million decayed-missing-filled teeth (units) averted over 10 years, with corresponding cost savings of A$666 million. The results were most sensitive to the assumed tax rate, reactions of consumers and responses from the industry. Our findings add to the broader context of the health effects of taxation policies on sugar-sweetened beverages, with implications for numerous chronic conditions previously demonstrated in the literature. Using an ad valorem tax as a policy response has several drawbacks as it affects persons whose intake of added sugar is moderate and leads to market inefficiency. References 1 Amine E , Baba N , Belhadj M , et al. Diet, nutrition and the prevention of chronic diseases. World Health Organization Technical Report Series. 2003 : 916 . 2 World Health Organization . Fiscal Policies for Diet and Prevention of Noncommunicable Diseases . 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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The European Journal of Public HealthOxford University Press

Published: May 22, 2018

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