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Objective When considering proposals to improve diets, it is important to understand how factors like price and income can affect saturated fat (SF) intake and demand. In this study, we examine and estimate the influence of price and income on intake across 160 countries, by age and sex, and derive sensitivity measures (price elasticities) that vary by age, sex and world region.Design We econometrically estimate intake responsiveness to income and prices across countries, accounting for differences by world region, age and sex. Intake data by age, sex and country were obtained from the 2018 Global Dietary Database. These data were then linked to global price data for select food groups from the World Bank International Comparison Programme and income data from the World Development Indicators Databank (World Bank).Results Intake differences due to price were highly significant, with a 1% increase in price associated with a lower SF intake (% energy/d) of about 4.3 percentage points. We also find significant differences across regions. In high-income countries, median (age 40) intake reductions were 1.4, 0.8 and 0.2 percentage points, given a 1% increase in the price of meat, dairy, and oils and fats, respectively. Price elasticities varied with age but not sex. Intake differences due to income were insignificant when regional binary variables were included in the analysis.Conclusion The results of this study show heterogeneous associations among prices and intake within and across countries. Policymakers should consider these heterogeneous effects as they address global nutrition and health challenges.

Abstract Background The United States (US) Food and Drug Administration (FDA), under the 2009 Family Smoking Prevention and Tobacco Control Act, banned characterizing flavors in cigarettes; however, mentholated tobacco products were exempt. Since 2009, over 20 US jurisdictions and numerous countries around the world have extended this restriction to menthol. Currently, the FDA is reconsidering its position on a nation-wide menthol cigarette ban. However, the effects of such a ban remain unclear. We conducted a scoping review to explore the impact of a menthol cigarette ban on individual behaviors (initiation, cessation, and product switching), sales, and compliance. Methods We conducted a search of the international literature using PubMed, EBSCO, and Web of Science (to November 25, 2019). We retrieved articles relevant to the impacts of an implemented or hypothetical menthol ban. We also included studies of flavored tobacco product bans due to their potential relevance in gauging compliance and product substitutability. Results The search identified 493 articles, of which 24 were included. Studies examined the effects of implemented menthol bans (n = 6), hypothetical menthol bans (n = 12) and implemented flavor bans that exclude menthol (n = 6). Menthol bans were found to reduce sales and increase smoking cessation with only partial substitution for non-menthol cigarettes. US smokers’ reactions to a hypothetical ban indicate that about 25–64% would attempt to quit smoking and 11–46% would consider switching to other tobacco products, including 15–30% to e-cigarettes. Flavor ban studies indicate reductions in initiation of 6%. Ban compliance was high, but studies indicate that the tobacco industry and retailers have attempted to circumvent their impact via packaging changes and online sales. Conclusion Our review finds that extending the US cigarette flavor ban to menthol products would promote smoking cessation and reduce initiation. This evidence supports further action by the FDA towards mentholated tobacco products. However, few studies have been conducted in the vaping era.

Objective: To project the prevalence of obesity across the WHO European region and examine whether the WHO target of halting obesity at 2010 levels by 2025 is achievable. Methods: BMI data were collected from online databases and the literature. Past and present BMI trends were extrapolated to 2025 using a non-linear categorical regression model fitted to nationally representative survey data. Where only 1 year of data was available, a flat trend was assumed. Where no data were available, proxy country data was used adjusted for demographics. Results: By 2025, obesity is projected to increase in 44 countries. If present trends continue, 33 of the 53 countries are projected to have an obesity prevalence of 20% or more. The highest prevalence is projected for Ireland (43%, 95% confidence interval (CI): 28-58%). Lithuania, Finland, and the Netherlands were each estimated to have an absolute increase of 2 percentage points in the prevalence of obesity between 2015 and 2025. Discussion: The quality of BMI data across Europe is highly variable, with fewer than 50% of the 53 countries having measured nationally representative data and often not enough data to interpret projections meaningfully. Nevertheless, the prevalence of obesity in the European Region appears to be increasing in most countries and, with it, the health and economic burden of its associated diseases. This paints a concerning picture of the future burden of obesity-related noncommunicable diseases across the region. Greater and continued effort for the implementation of effective preventive policies and interventions is required from governments.

Introduction: Public health surveillance of smoking prevalence is essential in gauging the magnitude of the problem, identifying groups most affected, and evaluating polices. However, little attention has focused on how prevalence is measured, particularly in low- and middle-income countries, where smoking patterns may not mirror those in high-income countries and where the burden of tobacco use is rapidly growing. Mexico provides a unique opportunity to gauge how the questions used to define established smokers can affect prevalence estimates. This study assesses how using the 100-cigarette lifetime question to define smoking status affects estimates of smoking prevalence. Methods: We consider data from four nationally representative surveys in Mexico, from 2002 to 2016. These surveys ask about current smoking even for adults who do not indicate having smoked 100 cigarettes in their lifetime. We compare estimates of daily and nondaily smoking prevalence by age and gender with and without the 100-cigarette screen. Results: The relative difference in prevalence estimates with and without the screen was greater for nondaily than daily smoking and for females than males. The difference was especially pronounced for nondaily smokers aged 15–24, where there was a 50%–75% relative difference in estimates, albeit smaller discrepancies (20–49% relative difference) were also found among older smokers. In recent years, the relative difference was greatest at lower levels of educational attainment. Conclusions: With the growth in nondaily smoking, using the 100-cigarettes screen to define smoking status can have important implications regarding size of smoking population. Further research is warranted in other countries.

Abstract Background We estimated the prevalence and mortality risks of preserved ratio impaired spirometry (PRISm) and chronic obstructive pulmonary disease (COPD) in the US adult population. Methods We linked three waves of pre-bronchodilator spirometry data from the US National Health and Nutritional Examination Survey (2007–2012) with the National Death Index. The analytic sample included adults ages 20 to 79 without missing data on age, sex, height, BMI, race/ethnicity, and smoking status. We defined COPD (GOLD 1, 2, and 3–4) and PRISm using FEV1/FVC cut points by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We compared the prevalence of GOLD stages and PRISm by covariates across the three waves. We estimated adjusted all-cause and cause-specific mortality risks by COPD stage and PRISm using all three waves combined. Results Prevalence of COPD and PRISm from 2007–2012 ranged from 13.1%-14.3% and 9.6%-10.2%, respectively. We found significant differences in prevalence by sex, age, smoking status, and race/ethnicity. Males had higher rates of COPD regardless of stage, while females had higher rates of PRISm. COPD prevalence increased with age, but not PRISm, which was highest among middle-aged individuals. Compared to current and never smokers, former smokers showed lower rates of PRISm but higher rates of GOLD 1. COPD prevalence was highest among non-Hispanic White individuals, and PRISm was notably higher among non-Hispanic Black individuals (range 31.4%-37.4%). We found associations between PRISm and all-cause mortality (hazard ratio [HR]: 2.3 95% CI: 1.9—2.9) and various cause-specific deaths (HR ranges: 2.0–5.3). We also found associations between GOLD 2 (HR: 2.1, 95% CI: 1.7–2.6) or higher (HR: 4.2, 95% CI: 2.7–6.5) and all-cause mortality. Cause-specific mortality risk varied within COPD stages but typically increased with higher GOLD stage. Conclusions The prevalence of COPD and PRISm remained stable from 2007–2012. Greater attention should be paid to the potential impacts of PRISm due to its higher prevalence in minority groups and its associations with mortality across various causes including cancer.

Abstract Background Simulation models play an increasingly important role in tobacco control. Models examining the impact of nicotine vaping products (NVPs) and smoking tend to be highly specialized and inaccessible. We present the Smoking and Vaping Model (SAVM),a user-friendly cohort-based simulation model, adaptable to any country, that projects the public health impact of smokers switching to NVPs. Methods SAVM compares two scenarios. The No-NVP scenario projects smoking rates in the absence of NVPs using population projections, deaths rates, life expectancy, and smoking prevalence. The NVP scenario models vaping prevalence and its impact on smoking once NVPs became popular. NVP use impact is estimated as the difference in smoking- and vaping-attributable deaths (SVADs) and life-years lost (LYLs) between the No-NVP and NVP scenarios. We illustrate SAVM’s adaptation to the German adult ages 18+ population, the Germany-SAVM by adjusting the model using population, mortality, smoking and NVP use data. Results Assuming that the excess NVP mortality risk is 5% that of smoking, Germany-SAVM projected 4.7 million LYLs and almost 300,000 SVADs averted associated with NVP use from 2012 to 2060. Increasing the excess NVP mortality risk to 40% with other rates constant resulted in averted 2.8 million LYLs and 200,000 SVADs during the same period. Conclusions SAVM enables non-modelers, policymakers, and other stakeholders to analyze the potential population health effects of NVP use and public health interventions.

Abstract Objective To synthesize the outcomes of policy evaluations of flavoured electronic nicotine delivery systems (ENDS) restrictions. Data sources PubMed, Scopus, Embase and Web of Science before May 3, 2022. Study selection Studies that report sales, behaviour, or compliance outcomes related to implemented or hypothetical ENDS flavour restrictions. Data extraction Restriction details, whether implemented or hypothetical, whether additional products were restricted, jurisdictional level, study locations, and outcomes classified by sales, behaviour, and compliance. Data synthesis We included 30 studies. Of those, 26 were conducted exclusively in the US, two in India, and two surveyed respondents in multiple countries, including the US. Twenty-one evaluated implemented restrictions, while nine considered hypothetical restrictions. Five studies evaluated product sales, 17 evaluated behaviour, and 10 evaluated compliance, with two studies reporting multiple outcomes. Two studies reported an increase and one a reduction in cigarette sales following restrictions, while three reported reductions in ENDS sales. Behavioural studies presented a mixed view of the impacts of regulations on ENDS and cigarette use. However, the use of disparate outcomes limits the comparability of studies. Studies of hypothetical restrictions suggest decreased ENDS use, increased cigarette use, and increased use of illicit markets. Studies of compliance with flavoured product restrictions that included ENDS found that 6–39% of stores sold restricted flavoured products post-restrictions. Online stores remain a potential source of restricted products. Conclusion Our findings highlight the need for additional research on the impacts of ENDS restrictions. Research should further evaluate the impact of restrictions on youth and adult use of nicotine and tobacco products in addition to the effects of restrictions in countries beyond the US to enable a robust consideration of the harm-benefit trade-off of restrictions.

BackgroundSmokeless tobacco (SLT) prevalence was decreasing in Kentucky before 2007, but has since increased. This study examines the impact of policies on cigarette and SLT use by applying the SimSmoke tobacco control policy simulation model. MethodsUsing data from the large-scale Tobacco Use Supplement of the Current Population Survey (TUS-CPS) and information on state-specific tobacco policies, Kentucky SimSmoke is updated and extended to incorporate exclusive SLT and dual cigarette and SLT use. The model is validated using survey data through 2017. The model was used to estimate the impact on smoking and SLT prevalence and attributable deaths of policies implemented between 1993 and 2018 and the impact of stronger future policies implemented in 2018 and maintained through 2060. ResultsSimSmoke generally reflects trends in exclusive cigarette use from the TUS-CPS and the Behavioral Risk Factor Surveillance System (BRFSS), but underestimated the increase in SLT prevalence in recent years. SimSmoke projects that policies implemented between 1993 and 2018 reduced male and female cigarette use by 23.7% and 23.0%, and male and female SLT use by 4.9% by 2018, averting 9018 tobacco-attributable deaths by 2018, increasing to 89 547 by 2060. The largest reductions in cigarette and SLT use were attributed to cigarette price increases. Strengthening tobacco control policies could reduce smoking prevalence by 41% and 40%, and reduce SLT prevalence by 33% and 25% for males and females by 2060. ConclusionOur results suggest that cigarette-oriented policies were effective in reducing SLT use but have been less successful in recent years. Future use rates can be further reduced through more restrictive statewide policies, which also target non-combustible nicotine products.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Abstract Background With the increasing changes in tobacco use patterns, “current use” definition and the survey used may have important implications for monitoring population use trends. Methods Using three US surveys (2014/15 TUS-CPS, NHIS and PATH), we compared the adult (age 18+) prevalence of four product groups (cigarettes, other combustibles, smokeless tobacco, and e-cigarettes) based on three past 30-day frequency of use thresholds: 1+, 10+, and 25+ days. We also examined mutually exclusive single, dual, and polytobacco users as a percentage of total users for each product group. Results Regardless of threshold or product, the prevalence was higher in PATH followed by NHIS and TUS-CPS, in some cases by large percentages. The differences in cigarette and smokeless tobacco use prevalence in going from the 1+ to 10+ days and to the 25+ days threshold were minimal. Applying different frequency thresholds had the largest impact on other combustibles prevalence, with a 60% reduction with the 10+ days threshold and a 80% reduction with the 25+ days threshold, compared to the 1+ days threshold, followed by e-cigarettes with 40 and 60% reductions, respectively. The proportion of dual and polytobacco users decreased considerably when using the 10+ vs. the 1+ days threshold and polytobacco use was almost non-existent with the 25+ days threshold. Conclusion The estimated prevalence of each tobacco product use depends largely on the survey and frequency of use threshold adopted. The choice of survey and frequency threshold merits serious consideration when monitoring patterns of tobacco use.

Abstract Background Nicotine vaping products (NVPs) are increasingly popular worldwide. They may provide public health benefits if used as a substitute for smoking, but may create public health harms if used as a gateway to smoking or to discourage smoking cessation. This paper presents the Smoking and Vaping Model (SAVM), a user-friendly model which estimates the public health implications of NVPs in the USA. Methods SAVM adopts a cohort approach. We derive public health implications by comparing smoking- and NVP-attributable deaths and life-years lost under a No-NVP and an NVP Scenario. The No-NVP Scenario projects current, former, and never smoking rates via smoking initiation and cessation rates, with their respective mortality rates. The NVP Scenario allows for smoking- and NVP-specific mortality rates, switching from cigarette to NVP use, separate NVP and smoking initiation rates, and separate NVP and smoking cessation rates. After validating the model against recent US survey data, we present the base model with extensive sensitivity analyses. Results The SAVM projects that under current patterns of US NVP use and substitution, NVP use will translate into 1.8 million premature smoking- and vaping-attributable deaths avoided and 38.9 million life-years gained between 2013 and 2060. When the NVP relative risk is set to 5%, the results are sensitive to the level of switching and smoking cessation rates and to a lesser extent smoking initiation rates. When the NVP relative risk is raised to 40%, the public health gains in terms of averted deaths and LYL are reduced by 42% in the base case, and the results become much more sensitive to variations in the base case parameters. Discussion Policymakers, researchers, and other public health stakeholders can apply the SAVM to estimate the potential public health impact of NVPs in their country or region using their own data sources. In developing new simulation models involving NVPs, it will be important to conduct extensive sensitivity analysis and continually update and validate with new data. Conclusion The SAVM indicates the potential benefits of NVP use. However, given the uncertainty surrounding model parameters, extensive sensitivity analysis becomes particularly important.

Abstract Background Although high consumption of soft drinks has been associated with excess of type 2 diabetes risk, the strength of this association in the Mexican population, where a type 2 diabetes genetic susceptibility has been well established, has been scarcely studied. This study aimed to estimate the risk of type 2 diabetes due to soft drinks consumption in a cohort of Mexicans. Methods We used data on 1445 participants from the Health Workers Cohort Study, a prospective cohort conducted in Cuernavaca, Mexico. Soft drinks consumption was assessed with a semi-quantitative 116-item food frequency questionnaire. Incident type 2 diabetes was defined as self-report of physician-diagnosed type 2 diabetes, fasting glucose > 126 mg/dl, or hypoglycemic medication at any examination. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazard models. Results With a total of 9526.2 person-years of follow-up, 109 incident cases of type 2 diabetes were observed. Type 2 diabetes incidence rate was 7.6, 11.0, and 17.1 per 1000 person-years across levels of soft drinks consumption of

Abstract Background While soft drink intake is positively associated with weight gain, no previous study has investigated whether leisure-time physical activity modifies this association. We estimated the association between soft drink intake and body weight, and explored if this association differed by levels of leisure-time physical activity. Methods We used data from the health workers cohort study, a prospective study of Mexican adults (20 to 85y old), including 1268 health workers and their families, who were assessed at baseline (2004–2006) and follow-up (2010–2012). We assessed soft drink intake (cola and flavored soda) using a validated food frequency questionnaire. We measured leisure-time physical activity using a self-report questionnaire, and categorized according to the 2010 World Health Organization (WHO) recommendations. Body weight was measured by trained personnel. The association between changes in soft drink intake and weight change, and if such association varied by levels of physical activity was estimated through fixed-effect models. Results An increase in one serving per day of soft drink was associated with 0.10 kg (95% CI 0.00, 0.19) increase in weight per year. This association was not modified by leisure-time physical activity, as demonstrated by the magnitude of the coefficient of the interaction between soft drink, leisure-time physical activity, and time (− 0.03 kg, 95% CI − 0.27 to 0.21); people who complied with the WHO physical activity recommendations gained 0.36 kg/year per serving of soft drink, compared to 0.48 kg/year for people without sufficient physical activity. Conclusions Soft drink intake was associated with weight gain. Leisure-time physical activity did not modify the association between soft drink intake and weight gain. This finding challenges the idea that leisure-time physical activity is sufficient to counterbalance weight gain associated to soft drink intake.

BackgroundNondaily smoking has been on the rise, especially in Mexico. While Mexico has strengthened its tobacco control policies, their effects on nondaily smokers have gone largely unexamined. We developed a simulation model to estimate the impact of tobacco control policies on daily and nondaily smoking in Mexico.MethodsA previously validated Mexico SimSmoke model that estimated overall trends in smoking prevalence from 2002 through 2013 was extended to 2018 and adapted to distinguish daily and nondaily smoking prevalence. The model was then validated using data from Mexican surveys through 2016. To gauge the potential effects of policies, we compared the trends in smoking under current policies with trends from policies kept at their 2002 levels.ResultsBetween 2002 and 2016, Mexico SimSmoke underestimated the reduction in male and female daily smoking rates. For nondaily smoking, SimSmoke predicted a decline among both males and females, while survey rates showed increasing rates in both genders, primarily among ages 15-44. Of the total reduction in smoking rates predicted by the model by 2018, tax policies account for more than 55%, followed by health warnings, cessation treatment, smoke-free air laws, and tobacco control spending.ConclusionsAlthough Mexico SimSmoke did not successfully explain trends in daily and nondaily smoking, it helps to identify gaps in surveillance and policy evaluation for nondaily smokers. Future research should consider appropriate measures of nondaily smoking prevalence, trajectories between daily and nondaily smoking, and the separate impact of tobacco control policies on each group.

In 2014 the Mexican government implemented taxes to reduce the consumption of sugar-sweetened beverages and nonessential energy-dense foods. In this manuscript, we analyze the scientific evidence linking the consumption of these products to chronic diseases, and summarize the studies that have evaluated the observed and expected impact of food taxes in Mexico. The implementation of taxes to unhealthy foods has reduced purchases of sugar-sweetened beverages in 7.6% and nonessential energy-dense foods in 7.4%. A reduction in consumption could decrease obesity prevalence by 2.5%; prevent 20 000 cardiovascular disease events; 189 000 diabetes cases; and 20 000 cardiovascular deaths. Additionally, this impact could save nearly 1 173 million dollars from healthcare costs. Taxes to unhealthy foods should be strengthen up and remain as an integral part of the national strategy to reduce obesity and chronic diseases in Mexico.

Rates of diabetes in Mexico are among the highest worldwide. In 2014, Mexico instituted a nationwide tax on sugar-sweetened beverages (SSBs) in order to reduce the high level of SSB consumption, a preventable cause of diabetes and cardiovascular disease (CVD). We used an established computer simulation model of CVD and country-specific data on demographics, epidemiology, SSB consumption, and short-term changes in consumption following the SSB tax in order to project potential long-range health and economic impacts of SSB taxation in Mexico.We used the Cardiovascular Disease Policy Model-Mexico, a state transition model of Mexican adults aged 35-94 y, to project the potential future effects of reduced SSB intake on diabetes incidence, CVD events, direct diabetes healthcare costs, and mortality over 10 y. Model inputs included short-term changes in SSB consumption in response to taxation (price elasticity) and data from government and market research surveys and public healthcare institutions. Two main scenarios were modeled: a 10% reduction in SSB consumption (corresponding to the reduction observed after tax implementation) and a 20% reduction in SSB consumption (possible with increases in taxation levels and/or additional measures to curb consumption). Given uncertainty about the degree to which Mexicans will replace calories from SSBs with calories from other sources, we evaluated a range of values for calorie compensation. We projected that a 10% reduction in SSB consumption with 39% calorie compensation among Mexican adults would result in about 189,300 (95% uncertainty interval [UI] 155,400-218,100) fewer incident type 2 diabetes cases, 20,400 fewer incident strokes and myocardial infarctions, and 18,900 fewer deaths occurring from 2013 to 2022. This scenario predicts that the SSB tax could save Mexico 983 million international dollars (95% UI $769 million-$1,173 million). The largest relative and absolute reductions in diabetes and CVD events occurred in the youngest age group modeled (35-44 y). This study's strengths include the use of an established mathematical model of CVD and use of contemporary Mexican vital statistics, data from health surveys, healthcare costs, and SSB price elasticity estimates as well as probabilistic and deterministic sensitivity analyses to account for uncertainty. The limitations of the study include reliance on US-based studies for certain inputs where Mexico-specific data were lacking (specifically the associations between risk factors and CVD outcomes [from the Framingham Heart Study] and SSB calorie compensation assumptions), limited data on healthcare costs other than those related to diabetes, and lack of information on long-term SSB price elasticity that is specific to geographic and economic subgroups.Mexico's high diabetes prevalence represents a public health crisis. While the long-term impact of Mexico's SSB tax is not yet known, these projections, based on observed consumption reductions, suggest that Mexico's SSB tax may substantially decrease morbidity and mortality from diabetes and CVD while reducing healthcare costs.