Download Modelling the health impacts of tobacco taxation in Ukraine Laura

Modeling the health
impacts of tobacco taxation
in Ukraine
Laura Webber PhD, UK Health Forum
Tatiana Andreeva PhD, Ukraine
alcohol and drug information centre
Renzo Sotomayor MD, World Bank
OUTLINE
•
•
•
•
•
•
Background
Aims
Methods
Results
Conclusions
Acknowledgements
BACKGROUND
• Ukraine has had one of the fastest declines in smoking
prevalence in the world (WHO, 2011)
• Multi-faceted tobacco control legislation from 2005 onwards,
corresponding to the FCTC
• Examples include: media campaigns, smoke-free places, textual
tobacco warning labels, ban on outdoor tobacco advertising,
taxes
• But Ukraine still has one of the highest smoking prevalence
rates in Europe (45% in men, 11% in women) (KIIS, Omnibus
surveys).
AIM
• Model the long term health impacts of increasing tobacco tax
in Ukraine
Specifically, the following two scenarios were run:
• 1. Baseline scenario (2016): Ad valorem (12%) minimum
specific (8.515 UAH/$0.35) and simple specific (6.365
UAH/$0.27)
• 2. Tax increase scenario: Increase Ad valorem tax (15%), and
30% Increase in the minimum specific excise (11.08
UAH/$0.46), and simple specific (8.28 UAH/$0.34)
METHOD
Microsimulation
Risk data
Disease data
Population data
Health economic
data
Intervention
scenarios
Distribution
programme
UKHF Microsimulation© programme
Input datasets
Software programmes
Output datasets
Output data
METHOD
• Smoking prevalence data from 2015 (national data)
• Population data for Ukraine (UN population data)
• Disease data as below (literature)
Incidence
Mortality
Survival
Direct healthcare costs
CHD
GBD 2015
GBD 2015
Converted from incidence
and mortality
I Denisova, P Kuznetsova
2014
Stroke
GBD 2015
GBD 2015
Converted from incidence
and mortality
I Denisova, P Kuznetsova
2014
COPD
GBD 2015
GBD 2015
Converted from incidence
and mortality
I Denisova, P Kuznetsova
2014
Lung
cancer
GBD 2015
GBD 2015
Converted from incidence
and mortality
I Denisova, P Kuznetsova
2014
RESULTS
• WHO TaXSiM model calculated that the tax increase would
result in a reduction in tobacco consumption
• This was used in the model to quantify the long-term health
impact on disease and related health care costs
Summary of scenario
% reduction in
cigarette consumption
Tax scenario
Estimated expected
Estimated expected
reduction in smoking (males) reduction in smoking
(females)
Uptake (%)
Number of
Uptake (%)
Number of
cigarettes
cigarettes
smoked (%)
smoked (%)
5.61
4.59
10.2
0
RESULTS
• The increase in tobacco tax is predicted to result in the
avoidance of ~127,000 new cases of smoking-related
diseases by 2035
78,092
CHD
$47.6m/yr
25,881
COPD
$6.7m/yr
14,725
Lung
Cancer
$6m/yr
8032
Stroke
$4.6m/yr
RESULTS
Economic outputs
Millions UAH Millions US$
Cum. direct healthcare costs avoided
Cumulative premature mortality costs
The exchange rate of 1US$/23.8 UAH is used here
2025
542.2
22.8
2035
1545.8
65.0
2025
3568.4
149.9
2035
16536.4
694.8
Limitations
• A number of data limitations
-No data on non-healthcare costs, e.g. lost productivity due to disease,
were available, though we included lost salary.
-No data were available to explore differences by social groups.
• Only a one-time tax was implemented
• No change in second-hand smoke exposure is modeled.
• The simulation only includes four smoking-related diseases, so results
are likely underestimates of the true effects.
• No in-depth uncertainty analysis was conducted.
• The model does not take account of future changes in policy or
technology.
Conclusions
• Small changes can have important impacts on morbidity,
premature mortality and related costs
• Continuous tobacco control measures are required
• Highlights the importance of gathering good quality
surveillance data to make the most accurate estimates
• Future work could consider additional tax scenarios
ACKNOWLEDGEMENTS
Tatiana I. Andreeva, ADIC-Ukraine
Renzo Sotomayor, World Bank
Abbygail Jaccard, UKHF
Lise Retat, UKHF
Michael Xu, UKHF
Konstantin Krasovsky, Ministry of Health, Ukraine
Joy Townsend, London School of Hygiene and Tropical Medicine
World Bank
Patricio Marquez
Alberto Gónima
Feng Zhao
Olena Doroshenko