Additional Files

Supplementary material Tob Control

Additional Files

Contents Additional file 1. Review of tobacco simulation models applied in Low- and Lower Middle-Income Countries ...... 2 Search strategy ...... 2 Inclusion and exclusion criteria ...... 2 Data extraction ...... 2 Results ...... 2 Search results ...... 2 Additional file 2: Annual percentage increase in excise tax to achieve 70% of retail price being tax by 2025 ...... 10 Additional file 3: Epidemiological data and trends for diseases ...... 10 Additional file 4: Model inputs for annual percent changes in cigarette smoking, and prevalence quit for current users ...... 12 Check for cohort variations ...... 12 Check for model fit for estimating APCs in cigarette smoking ...... 12 Estimating historic (1996 to 2016) proportion quitting by sex by age by year...... 13 Additional file 5 Model calibration ...... 15 Additional file 6: Annual percentage changes for all-cause mortality ...... 18 Additional file 7: Formula for current smoking RR ...... 19 Additional file 8: Coherence checks on epidemiological data from IHME, using DISMOD II ...... 20 Lung cancer ...... 20 Ischemic heart disease ...... 22 Stroke ...... 24 COPD ...... 26 Additional file 9: Uncertainty about starting smoking prevalence ...... 29 Additional file 10: Disease specific relative risks for smokers versus non-smokers according to age group and sex ...... 31

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 1. Review of tobacco simulation models applied in Low- and Lower Middle-Income Countries Search strategy A search was conducted on Medline using PubMed platform on 22 October 2018 to identify relevant studies without any date or language restrictions and using the following search strategy:

(Simulat* [TIAB] OR Markov[TIAB] OR Multistate [TIAB] OR Multi-state [TIAB] OR Levy[au] OR Gartner[au] OR Forecast* [TIAB])

AND

(Smoking [TIAB] OR Tobacco use [TIAB] OR Cigarette [TIAB] OR SimSmoke[TIAB])

AND

(Filters (filter number 2) for low and lower middle-income countries obtained from https://epoc.cochrane.org/lmic-filters) Inclusion and exclusion criteria Studies on populations from low and lower middle-income countries, defined by World Bank classification, that simulated tobacco control policies for reduction in mortality outcomes with and without measures of morbidity such as HALY/QALY/DALY were eligible for inclusion in the review. Multi-country studies that fulfilled the criterion related to simulation of tobacco control policies for reduction in mortality outcomes and included any low and lower middle-income country were included in the review. No language restrictions were put in place during the search. Studies on populations from upper middle-income nations (such as Thailand, Albania, Brazil, China) were excluded from the review. In addition, studies that only modelled effectiveness of tobacco control policies on prevalence of tobacco use and ignored disease burden and mortality were excluded as their findings are not comparable to other included studies. Data extraction To summarise the existing evidence, we recorded the authors’ details, publication year, base year from which projections are made and the time horizon in which interventions’ effectiveness is modelled. We also extracted information on the type of simulation modelling, whether the studies modelled changes in mortality through tobacco-attributable single disease, multiple diseases or whether directly to all-cause mortality. Additionally, we recorded the tobacco control interventions that were modelled in the selected studies. Finally, we extracted key characteristics of applied simulation models including allowing for future trends in tobacco prevalence as well as whether the models incorporated a time lag from quitting tobacco use to disease risk and consequently mortality. Results Search results A total of 429 articles were retrieved on PubMed through our search. Based on the title and abstract screening, and set inclusion and exclusion criteria, a total of 12 studies were deemed suitable for data extraction. The summary characteristics of the selected studies are presented chronologically in Table 1.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Table 1 Summary characteristics of included studies

Study Publ. Base year Country Type of Outcomes Single disease or Modelled through Tobacco control Allowing for future Time lag Year and time modelling all/many tobacco- diseases vs directly policies trends in tobacco from horizon related diseases to all cause modelled prevalence smoking mortality to disease incidence Levy et al.1 2006 Base year Vietnam Markov process Smoking Many Directly to all cause Tax increases, Yes No 2002; Over prevalence mortality clean indoor air a 30 year change and laws, mass period deaths media policies, averted advertising bans/ warning labels and strategies to reduce youth access Higashi et al2 2011 Base year Vietnam Markov process Change in Many Disease models: Excise tax Yes No 1999; 5 (multistate life DALYs & Lung cancer, COPD, increase, graphic year time tables)(BOD associated IHD, CVA, LRTI and warnings, mass horizon models); Separate change in other cancers media campaigns forecast model costs and smoking for BAU smoking bans in prevalence and public/work epidemiological places model for disease and mortality impact

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Study Publ. Base year Country Type of Outcomes Single disease or Modelled through Tobacco control Allowing for future Time lag Year and time modelling all/many tobacco- diseases vs directly policies trends in tobacco from horizon related diseases to all cause modelled prevalence smoking mortality to disease incidence Pichon-Riviere 2011 Base year: Multiple First order Monte Change in Many Disease models: 1) nicotine Yes Yes et al3 Different Latin Carlo, or QALYs Coronary and replacement or years for American probabilistic noncoronary heart behavioral each countries microsimulation disease, interventions); 2) country; of individual cerebrovascular media campaign) Time subjects disease, COPD, and; 3) training horizon: pneumonia, primary care Lifetime of influenza, lung physicians in the cancer, and nine brief counseling population other neoplasms interventions, including pharmacotherap y in benefit plans). Higashi & 2012 Base year: Vietnam Markov process Change in Many Disease models: Physician brief Yes No Barendregt4 2006, (multistate life DALYs & Ischaemic advice; nicotine Lifetime tables)(BOD associated heart disease (IHD); replacement horizon: models); Separate costs cerebrovascular therapy (NRT) until forecast model accident (CVA); patch; NRT gum; extinct and cancer (lung, mouth bupropion; and epidemiological and oropharynx, varenicline. model oesophagus, pancreas, bladder, stomach); chronic obstructive pulmonary disease (COPD); and lower respiratory tract infections

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Study Publ. Base year Country Type of Outcomes Single disease or Modelled through Tobacco control Allowing for future Time lag Year and time modelling all/many tobacco- diseases vs directly policies trends in tobacco from horizon related diseases to all cause modelled prevalence smoking mortality to disease incidence Basu et al.7 2013 Base year: India Discrete-time Deaths Just cardiovascular Disease modelled: (1) smoke-free Yes Yes 2013; Time microsimulation averted disease Myocardial laws, (2) brief horizon: 10 model (also infarctions and cessation advice years modelled stroke by health synergies in providers, (3) effectiveness of mass media interventions) campaigns covering channels of communication such as television, radio, newspapers, billboards, posters, leaflets, or booklets intended to reach large numbers of people, (4) a tobacco advertising ban, and (5) increased cigarette and/or bidi taxes Levy et al.8 2016 Base year: Pakistan and Using estimated Smoking Many Directly to all cause Tax increases, No No 2010; Time Tunisia price elasticities prevalence mortality clean indoor air horizon: 5 and relative risks. change and laws, mass years and (also modelled deaths media policies, 40 years synergies in averted advertising bans/ effects effectiveness of warning labels interventions) and strategies to reduce youth access

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Study Publ. Base year Country Type of Outcomes Single disease or Modelled through Tobacco control Allowing for future Time lag Year and time modelling all/many tobacco- diseases vs directly policies trends in tobacco from horizon related diseases to all cause modelled prevalence smoking mortality to disease incidence Ho et al.9 2017 Base year: Multiple Using estimated Cigarette Many Mortality not Tax No No 2014; Time African price elasticities consumption modelled horizon: countries not clear Ngalesoni et al. 2017 Base year Tanzania Markov process Change in Many Diseases modelled: Tobacco tax Yes No 10 2013; Time (multistate life DALYs and Stroke and IHD increase, mass horizon: 10 tables)(BOD cost media years models); Separate effectiveness campaigns, forecast model package and labelling, epidemiological advertisement model ban, smoke-free public places and smoke-free workplaces Ho et al.11 2018 Base year: Multiple Using estimated Deaths Many Directly to all cause Excise tax No No 2015; Time Asia-Pacific price elasticities averted and mortality increase horizon: countries and relative risks reduction of not clear (Cambodia, number of Nepal, smokers Bangladesh, Myanmar, Laos, Vietnam, Bhutan, Papua New Guinea, Solomon Islands, Indonesia, India Mongolia, Sri Lanka, Vanuatu)

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Study Publ. Base year Country Type of Outcomes Single disease or Modelled through Tobacco control Allowing for future Time lag Year and time modelling all/many tobacco- diseases vs directly policies trends in tobacco from horizon related diseases to all cause modelled prevalence smoking mortality to disease incidence Levy et al.12 2018 40 year 88 countries Using estimated Smoking Many Directly to all cause Tax increases, No No time that price elasticities prevalence mortality clean indoor air horizon implemented and relative risks. change and laws, mass highest deaths media policies, MPOWER averted advertising bans/ measures warning labels and strategies to reduce youth access Publ: Publication; BOD: Burden of Disease; DALYs: Disability Adjusted Life Years; COPD: Chronic Obstructive Pulmonary Disease; IHD: Ischemic Heart Disease; CVA: Cerebrovascular Accident; LRTI: Lower Respiratory Tract Infections MPOWER: Monitoring tobacco use and prevention policies, Protect people from tobacco smoke, Offer help to quit tobacco use, Warn about the dangers of tobacco, Enforce bans on tobacco advertising, promotion and sponsorship, Raise taxes on tobacco; NRT: Nicotine Replacement Therapy

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

References

1. Levy DT, Bales S, Lam NT, Nikolayev L. The role of public policies in reducing smoking and deaths caused by smoking in Vietnam: results from the Vietnam tobacco policy simulation model. Social science & medicine (1982) 2006; 62(7): 1819-30.

2. Higashi H, Truong KD, Barendregt JJ, et al. Cost effectiveness of tobacco control policies in Vietnam: the case of population-level interventions. Applied health economics and health policy 2011; 9(3): 183-96.

3. Pichon-Riviere A, Augustovski F, Bardach A, Colantonio L. Development and validation of a microsimulation economic model to evaluate the disease burden associated with smoking and the cost-effectiveness of tobacco control interventions in Latin America. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 2011; 14(5 Suppl 1): S51- 9.

4. Higashi H, Barendregt JJ. Cost-effectiveness of tobacco control policies in Vietnam: the case of personal smoking cessation support. Addiction (Abingdon, England) 2012; 107(3): 658-70.

5. Lutz MA, Lovato P, Cuesta G. Cost-effectiveness analysis of varenicline versus existing smoking cessation strategies in Central America and the Caribbean using the BENESCO model. Hospital practice (1995) 2012; 40(1): 24-34.

6. Lutz MA, Lovato P, Cuesta G. Cost analysis of varenicline versus bupropion, nicotine replacement therapy, and unaided cessation in Nicaragua. Hospital practice (1995) 2012; 40(1): 35- 43.

7. Basu S, Glantz S, Bitton A, Millett C. The effect of tobacco control measures during a period of rising cardiovascular disease risk in India: a mathematical model of myocardial infarction and stroke. PLoS medicine 2013; 10(7): e1001480.

8. Levy DT, Fouad H, Levy J, Dragomir AD, El Awa F. Application of the Abridged SimSmoke model to four Eastern Mediterranean countries. Tobacco control 2016; 25(4): 413-21.

9. Ho LM, Schafferer C, Lee JM, Yeh CY, Hsieh CJ. The effect of cigarette price increases on cigarette consumption, tax revenue, and smoking-related death in Africa from 1999 to 2013. International journal of public health 2017; 62(8): 899-909.

10. Ngalesoni F, Ruhago G, Mayige M, et al. Cost-effectiveness analysis of population-based tobacco control strategies in the prevention of cardiovascular diseases in Tanzania. PloS one 2017; 12(8): e0182113.

11. Ho LM, Schafferer C, Lee JM, Yeh CY, Hsieh CJ. Raising cigarette excise tax to reduce consumption in low-and middle-income countries of the Asia-Pacific region:a simulation of the anticipated health and taxation revenues impacts. BMC public health 2018; 18(1): 1187.

12. Levy DT, Yuan Z, Luo Y, Mays D. Seven years of progress in tobacco control: an evaluation of the effect of nations meeting the highest level MPOWER measures between 2007 and 2014. Tobacco control 2018; 27(1): 50-7.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 2: Annual percentage increase in excise tax to achieve 70% of retail price being tax by 2025 We specified the main tax intervention as annual percentage increases (API) in tobacco excise tax from 2017 to 2025 inclusive 1, such that by 2025 70% of the retail price was excise tax plus GST.

GST is 10% in the Solomon Islands, or 10/110 = 9.09% of retail price. Thus the 70% target can be reinterpreted as 60.91%/(1-0.0909) = 66.97% of the non-GST retail price being excise tax.

As of 2016, 19.8% of the retail price was excise tax (Table 1 of main paper), meaning 19.8/90.91 = 21.78% of the non-GST retail price is tobacco excise tax. Let X be the ratio increase in tobacco excise tax needed such that 66.97% of the retail GST exclusive price is excise tax, assuming no real change in manufacturer price and retailer margin. Then: × 21.78 = 66.97% × 21.78 + 78.22 𝑋𝑋 And therefore: 𝑋𝑋 60.91 × 78.12 = = 7.282 21.78 × (100 66.97) 𝑋𝑋 − Meaning one plus the API:

(1 + API) = 7.282( ) = 1.2468 1 9

That is, the API in excise tax have to be just under 25% per year for 9 years. (If one, say, only started the tax increases in 2019, it would be 7.2821/7= 1.328, or 32.8% per annum tax increases.)

As a logic check, the manufacturer’s price and retailer margin in 2016 was 78.22% and excise tax was 21.78% of the cigarette price excluding GST. Assuming no change (in real dollars) in the manufacturers price and retailer margin, then the percentage of the cigarette price that will be 9 9 excise tax in 2025 will be (21.78 × 1.2468 ) / (21.78 × 1.2468 + 78.22) = 66.97%. Including GST, the total tax will amount to 70% of the full retail price.

We modelled 25% per annum increases in the excise tax over 9 years (2017 to 2025 inclusive), rounding up from 24.68%.

Additional file 3: Epidemiological data and trends for diseases

1 The model structure does not easily allow an increase in excise tax to start in the base year. So it has to start in 2017, leaving 9 years including 2025 to increase tax. 10

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Modelled incidence rates, prevalence rates and death rates (per 100,000) of lung cancer, ischemic heart disease, stroke, lower respiratory tract infection and Chronic obstructive pulmonary disease (COPD) from 1990 to 2016 for Solomon Islands by sex in five-year age-bands, were sourced from the GBD results tool http://ghdx.healthdata.org/gbd-results-tool. Input parameters for lung cancer, ischemic heart disease, stroke and COPD in the multistate lifetable models included incidence rates, prevalence rates, case fatality rates, remission rate (lung cancer only) and disability rates. Trends in the form of annual percentage changes (APCs) for each of these parameters except disability rates were also included as input parameters. Details on estimation of each parameter is described below: Incidence rates: Incidence rates (per 100,000) for each of the 5 diseases according to sex in five-year age-bands were reported in GBD study from 1990 to 2016. Values of incidence rates for 2016 were used as inputs to DISMOD II (see Additional file 8). There was no strong evidence of non-linear (on log scale) trends in incidence or case fatality from 1990 to 2016 in the IHME data. Therefore, the APC for incidence rates were calculated by obtaining the coefficients for annual change from fitting Poisson regression models on values of incidence rates by sex: Ln[Incidence Rate] = Year + Age (categorical) Coefficients for ‘year’ were used to calculate the APCs in incidence rates for each of the disease by sex Case fatality rates: Case fatality rates for each of the 5 diseases according to sex in five-year age-bands were estimated using the prevalence rates and disease attributed death rates for each year: Case Fatality Rate in year (x)= Death rate attributed to disease in year (x)/ prevalence of disease in year (x) Case fatality rates estimated for 2016 were used as inputs to DISMOD (see Additional file 2). APCs in case fatality rates were also obtained by estimating the coefficients for annual change from fitting Poisson regression models by sex: Ln[Case Fatality Rate] = Year + Age (categorical) Coefficients for ‘year’ were used as the APCs in case fatality rates for each of the disease by sex. Remission rate: Remission rates for lung cancer were not reported in the Global Burden of Disease study. Alternatively, assuming for a 5% cancer specific five-year survival rate (i.e. 5-year RSR of 5%), separate remission rates for males and females were calculated using the following formula for five year age-groups: Estimated remission rate = Survival rate/(1-Survival rate) × Case fatality rate = 0.05/0.95 × Case fatality rate APCs in remission rates were also obtained by estimating the coefficients for annual change from fitting Poisson regression models by sex: Ln[Remission Rate] = Year + Age (categorical) Coefficients for ‘year’ were used as the APCs in remission rates for Lung cancer by sex.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 4: Model inputs for annual percent changes in cigarette smoking, and prevalence quit for current users Data were provided by the GBD on yearly prevalence of cigarette smoking from 1980 to 2016 according to sex and five-year age-groups. In the New Zealand tobacco model, future tobacco prevalence was estimated by calculating a net cessation rate (by age and sex) and initiation rate (at age 20, by sex), and trends over time in these, that fitted with observed historic trends in smoking prevalence and differential mortality rates by smoking status.1,2 Given the GBD data, and the parsimony of avoiding an additional Markov modelling step to estimate net cessation rates and initiation rates, we elected to forecast future tobacco smoking prevalence directly from the 1980 to 2016 data using logistic regression models on past data to determine APC by year. (We also note that the GBD will soon be making available future forecast estimates of risk factor prevalence as well as epidemiological parameters, to 2040 3, that future intervention simulation models should capitalize on.4)

In addition to allowing for age and period effects, we also checked for possible cohort effects. Check for cohort variations The following variable specification was used.

• Cohort (continuous) = Age in year (x) + (2016 – x) • Cohort (categorical)= 20/34; 35/44; 45/54; 55/64; 65/74; 75/84; 85/94; 95 and above, using above Cohort (continuous)

Following models were fitted using Stata 15 on data from 1980 to 2016 and ages between 20 and 79.

Model 1: Logit[prev] = int + B1.Age[categorical] +B2.Cohort[categorical]

Model 2: Logit[prev] = int + B1.Age[categorical] +B2.Cohort[categorical] +B3.Age.Cohort.[categorical]

Conclusions from the analysis:

1. No cohort effects seen in model 1 (i.e. no significant B2 coefficient) 2. No cohort or interaction effects seen in Model 2 (i.e. no significant B2 or B3 coefficient in model 2)

Check for model fit for estimating APCs in cigarette smoking To identify appropriate models that allow for age-variations in cigarette smoking in estimation of APCs and prevalence quit, age was modelled as continuous, categorical, quadratic and cubic. Interaction between age and year were also tested.

Model 1: Logit[prev] = int + B1.Age[continuous]

Model 2: Logit[prev]= int + B1.Age[continuous] + B2.Age[quadratic]

Model 3: Logit[prev]= int + B1.Age[continuous] + B2.Age[cubic]

Model 4: Logit[prev] = int + B1.Age[categorical]

Model 5: Logit[prev]= int + B1.Age[continuous] + B2.Age[quadratic] + Year[continuous]

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Model 6: Logit[prev]= int + B1.Age[continuous] + B2.Age[quadratic] + B3.Year[continuous]X Age[continuous]

Conclusion:

Based on the values of Deviance Information Criterion (DIC) and the lack of year-wise variation in cigarette smoking, Model 2 was identified as most appropriate for estimation of APC. Supplementary Figure 5 below shows the output log odds of smoking prevalence, by age. The categorical model – as a saturated model – is considered to the target. For parsimony in the MSLT, a non-categorical specification of age was preferred for mathematical simplicity. The linear age model (not shown) would fail to capture the non-linear decrease in the log odds of smoking with increasing age. The quadratic was, however, a good fit. The cubic option was no better, so for parsimony we selected a model with age and age-squared to predict the log odds of smoking.

Supplementary Figure 1: Log odds of smoking prevalence by age obtained from regressing smoking prevalence on categorical age, age(quadratic) and age (cubic).

0 20 40Age 60 80 100 0.00

-0.50

-1.00

-1.50

-2.00 Log odds of smoking of odds Log -2.50

-3.00 Categorical Quadratic Cubic

Regarding the year variable itself, critical for deriving the APC in the log odds of smoking, there was no strong evidence of any statistical interaction of year with age. Therefore, and parsimoniously, we simply used the year coefficient for modelling future smoking prevalence – which given the log odds scale, translates to somewhat different APC in smoking prevalence in the MSLT.

Estimating historic (1996 to 2016) proportion quitting by sex by age by year Predicted prevalence obtained from Model 2 was also used to estimate proportion quit, by sex, age and year, in the 20 years leading up to 2016. This was required for the MSLT as it has tunnel states for year since quitting to capture the diminishing disease risk with time since quit.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Theses historic estimates of proportion quit were ‘only’ needed in the initiation of the MSLT; as the MSLT simulates out to the future, it takes over the estimation of the quit proportions.

Therefore, and again for parsimony, we simply assumed no differential mortality by smoking status in the past, and used the above logistic model to estimate these initiation proportions. (The MSLT simulating forward does capture differential mortality by smoking status.)

1. Ikeda T, Cobiac L, Wilson N, et al. What will it take to get to under 5% smoking prevalence by 2025? Modelling in a country with a smokefree goal. . Tob Control 2015;24:139-45. doi: 10.1136/tobaccocontrol-2013-051196

2. Gartner CE, Barendregt JJ, Hall WD. Predicting the future prevalence of cigarette smoking in Australia: how low can we go and by when? Tobacco control 2009;18(3):183-89. doi: 10.1136/tc.2008.027615

3. Foreman K, et al. Forecasting life expectancy, years of life lost, all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios 2016-2040 for 195 countries and territories. The Lancet 2018.https://doi.org/10.1016/S0140-6736(18)31694-5.

4. Blakely T. Commentary: GBD makes major strides in forecasting future health. The Lancet 2018. https://doi.org/10.1016/S0140-6736(18)31861-0

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 5: Model calibration Given the rigorous epidemiological nature of most epidemiological input parameters, the model is – to an extent – self calibrated. However, it is important to check that model outputs in business as usual (BAU) are consistent with expected values. For example, small errors in projected trends in incidence and case fatality may interactively cumulate. Also, the model assumes disease independence, so if lung cancer is preferentially occurring among people with coronary heart disease, it may mean an independently modelled coronary heart disease mortality rate is incorrect. (Unless there is a high degree of non-independence, this is unlikely to be problematic until rates are very high (i.e. old age)).

The key epidemiological model inputs are disease incidence rates and case fatality rates – and their trends to 2040. These then work in the model to dynamically determine disease-specific prevalence and mortality rates. Thus, it is important to ‘check’ that the MSLT model outputs of mortality rates are plausible – which requires a plausible comparator or ‘calibration target’. To set these targets, we used Poisson regression on GBD all-cause mortality rates for 1990 to 2016, and then forecast (using the year term) the BAU future mortality rates.

We then extracted equivalent mortality rates from the MSLT, with a prior level of tolerance between the MSLT and Poisson regression estimates set at +/- 10% agreement. We compared these model outputs, for selected ages, at 5, 10 and 20 years after the base-year (i.e. 2016). These comparisons are shown in Supplementary Table 1, below. Predicted values of disease specific mortality rates by age and sex from the multistage life table model are mostly within 10% range of those obtained from independent prediction through DISMOD II and Poisson regression models. The only exceptions were for COPD in 2031 and 2036. Overall, a high degree of coherence was noted between the estimates from multistage life table model and DISMOD II and Poisson regression models.

Of note, when we initially undertook this exercise with the MSLT parameterized with IHME estimates of incidence and case fatality (IHME mortality rate divided by IHME prevalence; IHME estimates forecast forward using year coefficients from Poisson regression), calibration was poor. It was this that alerted us to the problem described below in Additional file 7, namely of an apparent lack of coherence of some of the Solomon Island disease incidence and mortality rates. The calibration outlined in this section is using the DISMOD II outputs described in Additional file 7.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Supplementary Table 2: Calibration targets (‘Poisson’, from a regression model on 1990 to 2016 GBD data predicting future rates and thence counts), and initial MSLT model outputs: mortality rates per 100,000 per year

2016 2021 2026 2031 2036 Sex Age group Poisson Model Ratio Poisson Model Ratio Poisson Model Ratio Poisson Model Ratio Poisson Model Ratio Coronary heart disease Male 40-44 153 145 0.95 159 158 0.99 164 161 0.98 170 162 0.95 176 161 0.91 55-59 455 408 0.90 471 457 0.97 488 475 0.97 505 480 0.95 523 476 0.91 70-74 1077 1017 0.94 1115 1100 0.99 1155 1142 0.99 1195 1159 0.97 1238 1150 0.93 85-89 3328 3119 0.94 3445 3402 0.99 3567 3481 0.98 3692 3497 0.95 3823 3467 0.91 Females 40-44 76 77 1.01 79 82 1.03 82 84 1.03 85 86 1.01 88 86 0.98 55-59 254 242 0.95 263 264 1.00 273 275 1.01 283 280 0.99 293 279 0.95 70-74 965 901 0.93 1000 1009 1.01 1036 1050 1.01 1073 1068 1.00 1111 1065 0.96 85-89 4122 3627 0.88 4270 4247 0.99 4423 4382 0.99 4581 4430 0.97 4745 4413 0.93 Stroke Male 40-44 70 63 0.90 68 69 1.01 66 68 1.03 65 67 1.03 63 66 1.06 55-59 323 301 0.93 314 315 1.00 306 314 1.03 298 308 1.04 290 306 1.05 70-74 1067 1012 0.95 1039 1050 1.01 1011 1042 1.03 984 1024 1.04 958 1017 1.06 85-89 2497 2174 1.25 2430 2377 0.98 2366 2362 1.00 2302 2319 1.01 2241 2306 1.03 Females 40-44 73 67 1.06 73 73 1.01 73 74 1.02 72 74 1.02 72 73 1.02 55-59 310 291 0.78 302 308 1.02 294 313 1.07 293 312 1.07 292 310 1.06 70-74 1077 1012 0.94 1048 1084 1.03 1020 1090 1.07 1017 1083 1.06 1015 1077 1.06 85-89 2889 2383 1.26 2812 2835 1.01 2737 2842 1.04 2729 2811 1.03 2722 2798 1.03 Lung cancer Male 40-44 15 15 0.98 15 15 0.99 16 15 0.99 16 16 0.98 16 16 0.96 55-59 63 64 1.01 64 64 0.99 66 64 0.98 67 65 0.98 68 65 0.96 70-74 142 143 1.01 145 144 0.99 148 146 0.99 151 148 0.98 154 149 0.96 85-89 199 198 1.00 203 201 0.99 207 204 0.99 212 207 0.98 216 207 0.96 16

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

2016 2021 2026 2031 2036 Sex Age group Poisson Model Ratio Poisson Model Ratio Poisson Model Ratio Poisson Model Ratio Poisson Model Ratio Females 40-44 4 4 1.04 4 4 1.00 4 4 1.00 4 4 0.99 5 4 0.96 55-59 20 21 1.05 21 21 1.02 22 22 1.02 22 23 1.01 23 23 0.98 70-74 60 61 1.02 62 62 1.00 65 64 0.99 67 66 0.99 70 66 0.95 85-89 62 61 0.98 64 63 0.98 67 65 0.97 69 67 0.97 72 67 0.93 COPD Male 40-44 35 35 1.00 33 33 1.01 31 31 1.02 29 30 1.04 27 30 1.12 55-59 133 136 1.02 124 128 1.03 116 120 1.03 109 115 1.06 102 116 1.14 70-74 609 638 1.05 569 603 1.06 532 569 1.07 498 545 1.09 466 549 1.18 85-89 1840 1838 1.00 1721 1763 1.02 1609 1684 1.05 1504 1629 1.08 1407 1654 1.18 Females 40-44 27 26 0.98 26 26 0.99 25 25 1.00 24 25 1.02 23 25 1.07 55-59 69 72 1.05 67 70 1.05 64 68 1.06 62 67 1.07 60 67 1.13 70-74 354 364 1.03 342 351 1.03 330 340 1.03 318 334 1.05 307 338 1.10 85-89 788 807 1.02 761 798 1.05 734 800 1.09 709 826 1.17 684 840 1.23

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 6: Annual percentage changes for all-cause mortality Data on total numbers of death and death rates for Solomon Islands according to age and sex were extracted from GBD estimates. Annual percentage changes (APCs) by sex were calculated based on Poisson regression coefficients obtained from regressing values of death rates on year. To allow for age-variations in all cause mortality, interactions between wider age groups and year were fitted, for models separately by sex.

Ln[all-cause mortality rate]= intercept + B1.Age[5yrcat] + B2.Year + B3.Age[1-19].Year + B4.Age[20- 34].Year + B5.Age[35-49].Year + B6.Age[50-79].Year + B7.Age[80+].Year

Where:

B1, B2, …, B7 are the model coefficients, with B1 actually being a vector of coefficients for all five- year age categories.

Age[5yrcat] is the categorical variable for 5 year age groups

Year is calendar year as a continuous variable

Age[1-19], Age[20-34], …, Age[80+] are dummy variables for these age groups, used in interactions with calendar year.

The coefficients B2 to B7 were then converted into APCs in mortality by age, and served as inputs to the model.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 7: Formula for current smoking RR There is no published relative risk for current compared to never smokers relative risk of all-cause mortality for the Solomon Islands. However, the GBD gives smoking attributable mortality rates, by sex and five year age group. We used this to back-estimate relative risks of all-cause mortality by smoking.

Let, within any sex by age group (not subscripted):

RRCS = the relative risk of all-cause mortality for current versus never smokers

RREX = the relative risk of all-cause mortality for smokers quit within the last 20 years (labelled as ‘ex’ for simplicity). Moreover, assume that the mortality harm for ex versus

current smokers is half, namely that RREX = 1+ 05 × (RRCS – 1).

PCS = the prevalence of current smoking

PEX = the prevalence of people who have quit within the last 20 years

PN = the prevalence of never smoking

SAM = smoking attributable mortality, as a percentage of all-cause mortality.

Then:

( 1) + ( 1) = + + 𝑅𝑅𝑅𝑅𝐸𝐸𝐸𝐸 − 𝑃𝑃𝐸𝐸𝐸𝐸 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶 − 𝑃𝑃𝐶𝐶𝐶𝐶 𝑆𝑆𝐴𝐴𝑀𝑀 𝑃𝑃𝑁𝑁 𝑅𝑅𝑅𝑅𝐸𝐸𝐸𝐸𝑃𝑃𝐸𝐸𝐸𝐸 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝐶𝐶

This can be rearranged to solve for RRCS, including the substitution of 1+ 05 × (RRCS – 1) for RREX:

(0.5 + ) 0.5 = 𝑃𝑃𝐸𝐸𝐸𝐸 𝑃𝑃𝐶𝐶𝐶𝐶 0.5 + −0.5 + − 𝑃𝑃𝑁𝑁 − 𝑃𝑃𝐸𝐸𝐸𝐸 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶 𝑆𝑆𝐴𝐴𝑀𝑀 𝑃𝑃𝐸𝐸𝐸𝐸 𝑃𝑃𝐶𝐶𝐶𝐶 𝐸𝐸𝐸𝐸 𝐶𝐶𝐶𝐶 𝑃𝑃 𝑃𝑃 − 𝑆𝑆𝐴𝐴𝑀𝑀

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 8: Coherence checks on epidemiological data from IHME, using DISMOD II The inputs to our model are outputs from IHME metaregression using DisMod MR2.1.1 Of particular concern, disease incidence rates were often higher than disease mortality rates, especially for ischemic heart disease (IHD). Such a pattern is possible with strong downward trends in incidence rates over time, but unlikely to be seen at all ages. Therefore, as a validity check, we put the IHME outputs as inputs to an earlier version of DISMOD, namely DISMOD II.2 These checks are reported here, using graphs, for four of the five included conditions (lung cancer, IHD), stroke and chronic obstructive pulmonary disease (COPD)). Coherence checks were not included for lower respiratory tract infection (LRTI) due to its episodic nature with short duration. Weights were applied for each of the input parameters to DISMOD II (incidence rates, prevalence rates, case fatality rates, mortality rates and remission rates (only for lung cancer)). Annual percentage changes (APCs) in incidence rates, case fatality rates (CFR) and remission rates (for lung cancer) were also applied to account for trends based on GBD 1990-2016 data for Solomon Islands. Lung cancer Weights for input parameters in DISMOD II based on available information from GBD (Lowest, low, medium, high, highest):

• Incidence: High • Prevalence: Medium • Remission*: Low • Case fatality: Low • Mortality: High Trends input for incidence rate and case fatality rate estimated from data from 1990-2016.

Males:

• Incidence: 0.29% • Case fatality: 0.16%

Females:

• Incidence: 0.63% • Case fatality: 0.29%

The IHME outputs (used as inputs here) concur well with DISMOD II outputs.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Ischemic heart disease Weights for input parameters in DISMOD II based on available information from GBD (Lowest, low, medium, high, highest):

• Incidence: High • Prevalence: Medium • Remission: Highest • Case fatality: Low • Mortality: High

APC trends input for incidence rate and case fatality rate estimated from data from 1990-2016.

Males -

• Incidence: 0.06% • Case fatality: 0.67%

Females:

• Incidence: 0.20% • Case fatality: 0.64%

The graphs are mostly plausible. However, the input incidence rates were higher than input mortality from about age 55 to 75 for both males and females (just discernible in the graphs). This is unlikely in reality. Moreover, we found that CHD death counts in initial calibration of the MSLT using just IHME inputs was poor. Thus, there were two ‘clues’ that the relativities of the IHME incidence and mortality rates were not quite correct.

Indeed, the DISMOD II outputs shifted the mortality rate down notably (allowing for the log scale; Supplementary Figure 2). The DISMODII prevalence was lower at most ages, and the DISOMD II output CFR higher at most ages.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Stroke Weights for input parameters in DISMOD II based on available information from GBD (Lowest, low, medium, high, highest):

• Incidence: High • Prevalence: Medium • Remission: Highest • Case fatality: Low • Mortality: High

Trends input for incidence rate and case fatality rate estimated from data from 1990-2016.

Males -

• Incidence: -0.50% • Case fatality: -0.21%

Females-

• Incidence: -0.25%

The graphs are mostly plausible (Supplementary Figure 3), but with a further indication that the IHME mortality rate may have been too high – not as much so as for CHD, but nevertheless present.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Supplementary Figure 3: Stroke IHME inputs (i) and DISMOD II outputs (case fatality on right y- axis, other variables plotted on left y-axis)

Stroke males

0.1

0.4

0.01

0.2 0.001 Rates Rates (Log Scale) Case fatality rate fatality Case

0.0001 0.1 25 35 45 55Age 65 75 85 95

Incidence (i) Prevalence (i) Mortality (i) Incidence (o) Prevalence(o) Mortality (o) Case fatality (i) (right axis) Case fatality (o) (right axis)

Stroke females

0.1 0.8

0.01 0.4

0.001 Rates Rates (Log Scale) 0.2 rates fatality Case

0.0001 0.1 25 35 45 55 65 75 85 95 Age

Incidence (i) Prevalence (i) Mortality (i) Incidence (o) Prevalence(o) Mortality (o) Case fatality (i) (right axis) Case fatality (o) (right axis)

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

COPD Weights for input parameters in DISMOD II based on available information from GBD (Lowest, low, medium, high, highest):

• Incidence: High • Prevalence: Medium • Remission: Highest • Case fatality: Low • Mortality: High

Trends input for incidence rate and case fatality rate estimated from data from 1990-2016.

Males -

• Incidence: -0.06% • Case fatality: -1.40%

Females-

• Incidence: 0.18% • Case fatality: -0.87%

The graphs are coherent, in that IHME inputs and DISMOD II outputs are very similar.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Supplementary Figure 4: COPD IHME inputs (i) and DISMOD II outputs (case fatality on right y-axis, other variables plotted on left y-axis)

COPD males 1

0.064 0.1

0.032 0.01 0.016 Rate (Log scale) Case fatality rate fatality Case 0.001 0.008

0.0001 0.004 25 35 45 55 65 75 85 95 Age

Incidence (i) Prevalence (i) Mortality (i) Incidence (o) Prevalence(o) Mortality (o) Case fatality (i) (right axis) Case fatality (o) (right axis)

COPD females 1 0.096

0.1

0.048 0.01

0.024 0.001 Rate (Log scale) Case fatality rate fatality Case

0.012 0.0001

0.00001 0.006 25 35 45 55 65 75 85 95 Age

Incidence (i) Prevalence (i) Mortality (i) Incidence (o) Prevalence(o) Mortality (o) Case fatality (i) (right axis) Case fatality (o) (right axis)

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

1. Flaxman AD, Vos T, Murray C, editors. An Integrative Metaregression Framework for Descriptive Epidemiology. Seattle, WA: Institute for Health Metrics and Evaluation; 2015.

2. Barendregt J, Oortmarssen GJ, Vos T, Murray CJL. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metr 2003; 1(1): 4.

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 9: Uncertainty about starting smoking prevalence In the New Zealand model1 that this Solomon Islands model was adapted from, baseline year smoking prevalence was known with high precision – so no uncertainty was included. However, for the GBD estimates of Solomon Islands cigarette smoking prevalence, there was considerable uncertainty. Supplementary Table 1 (next page) shows the uncertainty in 2016 smoking prevalence, with conversions to the logit scale. The back-estimated standard deviation (SD) of the mean estimate on the logit scale was very similar across age groups, being about 0.35 for males and 0.40 for females. We simply used these SDs for males and females to specify uncertainty about the starting (2016) smoking prevalence (using a logistic distribution to convert logit to prevalence). We specified a 100% correlation of the random draws across sex and age groups within each iteration of the model (i.e. if a high value was drawn, it was high within the range for all age groups).

This uncertainty in the starting prevalence flows through to variation in the number of people quitting. However, it did not have an impact on the starting distribution of former smokers who have quit within 20 years, by years since quit, in the base-year. This will not have a great impact on the MSLT model outputs, and in the interests of model parsimony it was deemed not necessary to include uncertainty about starting proportions by age and by year since quit.

1. Blakely T, Cobiac LJ, Cleghorn CL, et al. Health, Health Inequality, and Cost Impacts of Annual Increases in Tobacco Tax: Multistate Life Table Modeling in New Zealand. PLoS medicine 2015;12(7):e1001856. doi: 10.1371/journal.pmed.1001856 [published Online First: 2015/07/29]

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Supplementary Table 1: GBD predicted smoking prevalence in 2016

Lower Upper SD of mean Sex Age Prev. Lower CL Upper CL Logit CL logit CL logit on logit scale

Males 10 to 14 1.6% 0.8% 2.9% -4.15 -4.88 -3.51 0.35

15 to 19 12.1% 6.5% 20.3% -1.98 -2.66 -1.37 0.33

20 to 24 34.1% 20.7% 49.5% -0.66 -1.34 -0.02 0.34

25 to 29 43.1% 27.1% 59.5% -0.28 -0.99 0.39 0.35

30 to 34 42.1% 26.3% 59.3% -0.32 -1.03 0.38 0.36

35 to 39 38.0% 23.7% 54.2% -0.49 -1.17 0.17 0.34

40 to 44 35.7% 22.4% 53.0% -0.59 -1.24 0.12 0.35

45 to 49 36.7% 22.8% 52.5% -0.54 -1.22 0.10 0.34

50 to 54 34.9% 20.6% 51.2% -0.62 -1.35 0.05 0.36

55 to 59 32.3% 18.5% 48.7% -0.74 -1.48 -0.05 0.37

60 to 64 28.2% 15.9% 43.1% -0.94 -1.66 -0.28 0.35

65 to 69 24.5% 13.2% 38.9% -1.13 -1.88 -0.45 0.36

70 to 74 19.1% 10.3% 31.2% -1.44 -2.16 -0.79 0.35

75 to 79 15.5% 7.9% 25.6% -1.69 -2.45 -1.07 0.35

80 to 84 11.0% 5.6% 19.0% -2.09 -2.83 -1.45 0.35

85 to 89 11.0% 5.6% 19.0% -2.09 -2.83 -1.45 0.35

90 to 94 11.0% 5.6% 19.0% -2.09 -2.83 -1.45 0.35

95 plus 11.0% 5.6% 19.0% -2.09 -2.83 -1.45 0.35

Females 10 to 14 0.7% 0.3% 1.4% -4.93 -5.75 -4.24 0.39

15 to 19 4.5% 2.0% 8.7% -3.06 -3.89 -2.36 0.39

20 to 24 10.1% 4.6% 18.5% -2.18 -3.04 -1.48 0.40

25 to 29 12.2% 5.9% 22.2% -1.97 -2.77 -1.25 0.39

30 to 34 12.5% 6.1% 22.4% -1.95 -2.73 -1.24 0.38

35 to 39 12.8% 6.2% 22.9% -1.91 -2.72 -1.21 0.38

40 to 44 12.2% 5.8% 21.1% -1.98 -2.79 -1.32 0.38

45 to 49 13.2% 6.3% 23.9% -1.88 -2.69 -1.16 0.39

50 to 54 13.7% 5.9% 24.6% -1.84 -2.76 -1.12 0.42

55 to 59 13.0% 5.7% 25.4% -1.90 -2.80 -1.07 0.44

60 to 64 12.0% 5.3% 23.4% -1.99 -2.88 -1.19 0.43

65 to 69 9.7% 4.4% 18.3% -2.23 -3.09 -1.50 0.41

70 to 74 7.5% 3.1% 14.5% -2.52 -3.44 -1.78 0.42

75 to 79 5.7% 2.4% 11.2% -2.81 -3.70 -2.07 0.41

80 to 84 3.8% 1.7% 7.5% -3.23 -4.05 -2.51 0.39

85 to 89 3.8% 1.7% 7.5% -3.23 -4.05 -2.51 0.39

90 to 94 3.8% 1.7% 7.5% -3.23 -4.05 -2.51 0.39

95 plus 3.8% 1.7% 7.5% -3.23 -4.05 -2.51 0.39

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861 Supplementary material Tob Control

Additional file 10: Disease specific relative risks for smokers versus non-smokers by age group and sex

Age Disease 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80 & above Ischemic Heart 4.316 3.924 3.569 3.246 2.952 2.685 2.443 2.223 2.023 1.841 1.598 Disease (Males) (3.129, (2.907, (2.701, (2.509, (2.331, (2.166, (2.012, (1.869, (1.737, (1.613, (1.445, 5.802) 5.18) 4.624) 4.128) 3.686) 3.29) 2.938) 2.623) 2.341) 2.09) 1.773) Ischemic Heart 6.145 5.464 4.859 4.321 3.843 3.417 3.039 2.703 2.404 2.139 1.794 Disease (Females) (5.066, (4.563, (4.109, (3.701, (3.333, (3.002, (2.703, (2.345, (2.193, (1.975, (1.688, 7.41) 6.511) 5.722) 5.029) 4.419) 3.883) 3.413) 2.999) 2.636) 2.316) 1.908) COPD (Males) 11.546 11.546 11.546 11.546 11.546 11.546 11.546 11.546 11.546 11.546 11.546 (8.921, (8.921, (8.921, (8.921, (8.921, (8.921, (8.921, (8.921, (8.921, (8.921, (8.921, 14.93) 14.93) 14.93) 14.93) 14.93) 14.93) 14.93) 14.93) 14.93) 14.93) 14.93) COPD (Females) 15.257 15.257 15.257 15.257 15.257 15.257 15.257 15.257 15.257 15.257 15.257 (13.638, (13.638, (13.638, (13.638, (13.638, (13.638, (13.638, (13.638, (13.638, (13.638, (13.638, 17.149) 17.149) 17.149) 17.149) 17.149) 17.149) 17.149) 17.149) 17.149) 17.149) 17.149) Stroke (Males) 4.175 3.805 3.468 3.161 2.882 2.627 2.395 2.184 1.992 1.816 1.582 (3.167, (2.94, 4.88) (2.73, (2.534, (2.352, (2.184, (2.027, (1.882, (1.747, (1.622, (1.45, 5.444) 4.375) 3.922) 3.516) 3.152) 2.825) 2.533) 2.27) 2.035) 1.727) Stroke (Females) 6.02 5.357 4.767 4.243 3.777 3.363 2.994 2.666 2.375 2.115 1.778 (4.253, (3.874, (3.529, (3.214, (2.927, (2.666, (2.429, (2.212, (2.015, (1.835, (1.595, 8.384) 7.309) 6.372) 5.555) 4.843) 4.222) 3.681) 3.209) 2.798) 2.439) 1.986) LRTI (Both) 3.484 3.484 3.484 3.484 3.484 3.484 3.484 3.484 3.484 3.484 3.484 (2.802, (2.802, (2.802, (2.802, (2.802, (2.802, (2.802, (2.802, (2.802, (2.802, (2.802, 4.299) 4.299) 4.299) 4.299) 4.299) 4.299) 4.299) 4.299) 4.299) 4.299) 4.299) TBL Cancer (Males) 22.511 22.511 22.511 22.511 22.511 22.511 22.511 22.511 22.511 22.511 22.511 (19.065, (19.065, (19.065, (19.065, (19.065, (19.065, (19.065, (19.065, (19.065, (19.065, (19.065, 26.704) 26.704) 26.704) 26.704) 26.704) 26.704) 26.704) 26.704) 26.704) 26.704) 26.704) TBL Cancer 14.095 14.095 14.095 14.095 14.095 14.095 14.095 14.095 14.095 14.095 14.095 (Females) (13.049, (13.049, (13.049, (13.049, (13.049, (13.049, (13.049, (13.049, (13.049, (13.049, (13.049, 15.352) 15.352) 15.352) 15.352) 15.352) 15.352) 15.352) 15.352) 15.352) 15.352) 15.352)

*Sourced from Supplement to: GBD 2015 Tobacco Collaborators. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from

the Global Burden of Disease Study 2015. Lancet 2017; published online April 5. http://dx.doi.org/10.1016/S0140-6736(17)30819-X

Singh A, et al. Tob Control 2020; 29:388–397. doi: 10.1136/tobaccocontrol-2018-054861