METHODS: We analysed sequential Global Adult Tobacco Survey (GATS) data done at least at five years interval in 10 countries namely India, Bangladesh, China, Mexico, Philippines, Russia, Turkey, Ukraine, Uruguay, and Vietnam. We estimated weighted prevalence rates of smoking behaviors namely current smoking (both daily and non-daily), prevalence of hardcore smoking (HCS) among current smokers (HCSs%) and entire surveyed population (HCSp%), quit ratios (QR), and the number of cigarettes smoked per day (CPD). We calculated absolute and relative (%) change in rates between two surveys in each country. Using aggregate data, we correlated relative change in current smoking prevalence with relative change in HCSs% and HCSp% as well as explored the relationship of MPOWER score with relative change in smoking behaviors using Spearman' rank correlation test.
RESULTS: Overall daily smoking has declined in all ten countries lead by a 23% decline in Russia. In India, Bangladesh, and Philippines HCSs% decreased as the smoking rate decreased while HCSs% increased in Turkey (66%), Vietnam (33%) and Ukraine (15%). In most countries, CPD ranged from 15 to 20 sticks except in Mexico (7.8), and India (10.4) where CPD declined by 18 and 22% respectively. MPOWER scores were moderately correlated with HCSs% in both sexes (r = 0.644, p = 0.044) and HCSp% (r = 0.632, p = 0.05) and among women only HCSs% (r = 0.804, p = 0.005) was significantly correlated with MPOWER score.
CONCLUSION: With declining smoking prevalence, HCS had also decreased and quit rates improved. Ecologically, a positive linear relationship between changes in smoking and HCS is a possible evidence against 'hardening'. Continued monitoring of the changes in quitting and hardcore smoking behaviours is required to plan cessation services.
DESIGN, SETTING AND PARTICIPANTS: Ten-year horizon (2016-25) modeling study of opioid addiction epidemic and treatment that accommodated potential peer effects in opioid use initiation and supply-induced treatment demand in three Ukrainian cities: Kyiv, Mykolaiv and Lviv, comprising a simulated population of people at risk of and with OUD.
MEASUREMENTS: Incremental cost per quality-adjusted life-year gained in the simulated population.
FINDINGS: An estimated 12.2-, 2.4- and 13.4-fold OAT capacity increase over 2016 baseline capacity in Kyiv, Mykolaiv and Lviv, respectively, would be cost-effective at a willingness-to-pay of one per-capita gross domestic product (GDP) per quality-adjusted life-year gained. This result is robust to parametric and structural uncertainty. Even under the most ambitious capacity increase, OAT coverage (i.e. the proportion of people with OUD receiving OAT) over a 10-year modeling horizon would be 20, 11 and 17% in Kyiv, Mykolaiv and Lviv, respectively, owing to limited demand.
CONCLUSIONS: It is estimated that a substantial increase in opioid agonist treatment (OAT) capacity in three Ukrainian cities would be cost-effective for a wide range of willingness-to-pay thresholds. Even a very ambitious capacity increase, however, is unlikely to reach internationally recommended coverage levels. Further increases in coverage may be limited by demand and would require addressing existing structural barriers to OAT access.