Economic evaluation of tobacco control in Asia
Tuvdendorj, Ariuntuya
DOI:
10.33612/diss.155457815
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2021
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Tuvdendorj, A. (2021). Economic evaluation of tobacco control in Asia: Dynamic population health impact
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Informing policy makers on the efficiency
of population level tobacco control
interventions in Asia:
A systematic review of model-based
economic evaluations
Ariuntuya Tuvdendorj
Yihui Du
Grigory Sidorenkov
Erik Buskens
Geertruida H. de Bock
Talitha Feenstra
ABSTRACT
Economic evaluations of tobacco control interventions support decisions
regarding resource allocation in public health policy. Our systematic review
was aimed at identifying potential bias in decision models used to estimate the
long-term costs and effects of population-based tobacco control interventions
in Asia.
We included studies conducted in Asian countries and using a modelling
technique to evaluate the economic impacts of one or more population-based
tobacco interventions in line with the Framework Convention on Tobacco
Control (FCTC). We assessed the structure, input parameters, and risk of bias
for each model, and performed a narrative synthesis of the included studies.
Nine model-based economic evaluation studies of population-based tobacco
interventions were identified. About 60% of the criteria for reporting quality
were met in all studies, indicating that reporting generally lacked transparency.
The studies were highly heterogeneous in terms of the scope, types, and
structures of their models and the quality of input parameters. One-third of
the models applied in the studies scored a high risk of bias, with problems
mostly falling into the following categories: model type, time horizons, and
smoking transition probabilities.
More data is needed to provide high-quality evidence regarding the
cost-effectiveness of tobacco control policies in Asia. Strong evidence at the
country level hinges on the availability of accurate estimates of the effects of
the interventions, the relative risks of smoking, and the price elasticity of the
demand for tobacco. Simple transfers of models built in Western populations
do not suffice.
4
INTRODUCTION
The Asian continent accounts for the highest production and consumption
of tobacco globally.(1) To reduce tobacco use, the World Health Organization
proposes a package of interventions, the so-called MPOWER interventions,
with proven effectiveness MPOWER includes: Monitor tobacco use and
prevention, protect people from smoke, offer help to quit smoking, warn
about the danger, enforce bans, and raise taxes. (2)(3) Limited resources for
prevention policy may require countries to set priorities. (4)(5)
Health economic decision models are being increasingly used, as part
of health technology assessments (HTAs), within many countries to support
priority setting.(6) These models enable tobacco control interventions to be
evaluated for their long term consequences. The models support extrapolations
from short-term observations and the synthesis of data derived from various
sources. (7) The findings of a previous review showed that the majority of the
models applied in economic evaluation studies were developed and applied
in Western countries. (8)
Specific types of bias may occur in model-based economic evaluations.
When present, such bias hinders the translation of the economic evaluation
results to real life. Previous systematic reviews indicated that all of the examined
model-based economic evaluations of smoking cessation interventions had
missing information in one or more key domains required for full transferability
of these evaluations to a new context. (9) Because these models were often
developed in Western countries, assessments of their reporting quality and
risk of bias are important prerequisites prior to their application in Asian
settings. (10)
Hence our review focussed on applications in Asia. While previous
reviews have examined simulation models used for evaluating tobacco control,
they did not consider their application in Asian contexts or examine potential
model bias. (8) Therefore, our aim was to conduct a systematic review of the
potential bias of decision models used to estimate the costs and effects of
tobacco control interventions in an Asian setting. We produced a systematic
qualitative synthesis of the studies included in our review.
METHODS
This systematic review has been registered in the International Prospective
Register of Systematic Reviews (PROSPERO) under the following number:
CRD42019141679. (11) We adhered to the Preferred Reporting Items for
Systematic Reviews and Meta-Analysis (PRISMA). (12)
Eligibility criteria
Type of population: We reviewed studies conducted in Asian populations,
covering a total of 48 countries categorized as belonging to Asia within the
WHO country classifications. We excluded Australia and New Zealand.
Type of interventions: Primary focus was on the WHO’s ‘MPOWER’ interventions.
Studies were included which evaluated a non-clinical, population-based
intervention. Individual-oriented interventions, such as cessation support,
were excluded from the review, since they can be evaluated by more simple
models than population-based interventions.
Study design: We reviewed full economic evaluation studies. To be included in
the review, studies had to report minimally on intervention costs and health
benefits and ideally on all relevant cost consequences and health outcomes.
We explored the heterogeneity of model types and structures that are
currently being applied within Asian settings, but did require a model-based
economic evaluation, that is, use of a mathematical model that simulated
both intervention effects and costs.
Comparator: There was no restriction on the comparator. The economic
evaluation could compare the results of all feasible options in relation to each
other and/or to current practices.
Information sources
A systematic search was performed to identify all relevant studies that satisfied
our selection criteria within the following databases: Medline, Embase, Web of
Science, and the Cochrane Library. Additionally, we checked the reviews that
we identified for further studies.
4
Search strategy
In consultation with the medical data specialists, four sets of search strings
were used: (1) specific populations/countries classified as Asian, (2) terms
related to smoking and tobacco control, (3) combined terms from studies
in health economic evaluations, and (4) specific terminology for simulation
models. The exact search terms per database are listed in Appendix S1. The
last search was conducted in November 2019.
Selection process
After removing duplicates, two authors (AT, YD) independently screened
the titles and abstracts followed by the full-text articles. Any disagreements
between the two reviewers were resolved through discussions. However, if a
disagreement persisted, it was resolved though consultations involving the
other authors: GS, EB, GHdB, and TF.
Data extraction
Data extraction forms were developed using the consolidated health economic
evaluation reporting standards (CHEERS) checklists.(13) The two reviewers (AT,
YD) independently extracted data using these forms for 30% of the articles
included in the review that were randomly selected to ensure consistency.
Disagreements were resolved through discussions between the two reviewers
(AT, YD), and in cases in which no agreement was reached, a third author
(TF) intervened. During this process, data extraction forms were revised and
checked by each of the co-authors to ensure consistency in interpretation,
and adapt the information extracted from the studies to cover the aspects of
relevance to our research question. The final data extraction form used for the
current study is shown in Appendix S2.
Data items
The following data were extracted from each study: (1) overview of study
characteristics, (2) model structure, and (3) sources of evidence for model
parameters. The data were separately extracted for each intervention type,
sub-model, and individual country.
Risk of bias in individual studies
Quality of reporting in the selected studies was assessed using the 56 items
Philips checklist. Items are distributed across three components: structure (n =
20), data (n = 32) and consistency (n = 4).(14) The percentages of ‘yes’, ‘no’, ‘not
applicable’, or ‘unclear’ were calculated for each component. (Appendix S3)
Next to this, quality of sources of evidence for model parameters was
assessed according to the hierarchy of evidence for economic evaluations.
(15) The level of evidence for each model parameter was graded as ‘high’,
‘moderate’, ‘low’, ‘not applicable’ or ‘no source’. A full description of the hierarchy
of evidence scales used can be found in Appendix S4.
Finally, the bias in economic evaluations (ECOBIAS) checklist was applied
to assess the risk of bias.(16) This checklist includes eleven types of bias
identified within model-based economic evaluations. Studies were critically
assessed for each type of bias, and then ranked as ‘high risk’, ‘moderate risk’,
and ‘low risk’ for each item. The average risk of bias was then calculated for
each item across all studies. The full checklist is presented in Appendix S5.
Data synthesis
The outcome of this review was a systematic narrative synthesis to present a
critical appraisal of the methodological quality and risk of bias of the selected
modelling studies. Aim was to assess the suitability of available decision
models for the Asian context, using Cochrane guidance.(17)
4
RESULTS
Figure 1 presents the details and process relating to the search for and
selection of studies for the review. A total of 2,567 records were identified
during the initial search; nine modelling studies were finally selected for full
data extraction.
Id en tif ic ation Medline (n=675) Embase (n=861) Web of Science (n=744) Cochrane (n=287)
Article identified in external sources
(n=1)
Articles identified through a systematic database search
(n = 2,567) Scr een in g
Articles remaining after
excluding duplicates (n = 1,413)
Titles and abstracts screened
(n = 1,413) Articles excluded on the basis of titles and abstracts (n = 1,380)
Elig
ib
ility
Articles eligible for full text
screening (n = 33)
Articles excluded because they were - Not the study population (n = 14) - Not population interventions (n = 3) - Not economic evaluations (n = 3) - Not model-based EE (n = 1) - Not about smoking exposure (n = 1) - Not full-text publications (n = 3)
Inc
lude
d Studies included in the
qualitative synthesis (n = 9)
Overview of studies included in the review
Table 1 summarizes the included studies, and Table S2 in the Appendix adds
more details. Most studies (n=7) focused on interventions conducted within a
single country: four studies were conducted in Vietnam, (18-21) two studies
in China, (22)(23) and one was in India (24). Two other studies focused on
multiple countries; seven(25) and nine (26) countries respectively. Three
studies focused solely on the male population.(22)(23)(25) Four studies
applied cost-consequence analyses, (18)(21)(25)(26) three studies applied
effectiveness analyses (19)(20)(24) and two studies an extended
cost-effectiveness analysis (22)(23). A tobacco tax increase (n = 7) was most
often studied. Three studies compared and combined up to four MPOWER
population-based tobacco control interventions,(19)(22)(26) namely mass
media campaigns, tax increases, labelling of tobacco products, bans on
tobacco advertising, graphic warnings, and the promotion of smoke-free
work-places.
Model structure
Table S3 in the Appendix presents a summary of model structures. The most
prevalent model type applied was a static simple compartmental model (18)
(22)(23)(25) that was previously used in international comparison studies. (27)
The static models directly link an initial intervention effect in terms of smoking
prevalence to total life years gained and costs. The following dynamic models
were applied : a state transition model (19); a dynamic life-table model (20);
and a dynamic population model. (21) Dynamic models estimate health gains
over time. Considerable variations existed. For instance, diseases modelled
varied from only one (20)(24) up to 16 different tobacco-related diseases.(26)
All studies performed sensitivity analyses to assess uncertainty associated
with key scenarios and parameters. The most commonly used technique was
a univariate sensitivity analysis (n=5).
4
Table 1
O
ver
view of studies included in the r
eview
C hapt er 4 A ut ho r a nd pu blic at io n ye ar Se tti ng ; B as el in e y ea r Ty pe o f an al ys is In terv en tio n C ho ice of o ut co m es ICE R/ re su lts Pol ic y ad vi ce /c on cl us ion Effe cts Co st s M in h e t a l., 201 8 [1 8] V iet nam ; 2 01 7 C ost -cons eque n ce s tu dy Tax in cr eas e b y 75% –8 5% A vo id ed m or ta lit ie s Sav ed m or tal ity co st s N ot pr es ent ed b ut he al th ga ins and c os t s av in gs w er e f ou nd. The g ove rnm ent sho ul d s upp or t ef fo rts to in cr eas e t he ci gar et te tax in V iet nam . G loba l T oba cc o Eco no m ics C ons or tium , 201 8 [25] Indi a, Ind one si a, B angl ade sh, P hi lip pi ne s, V ie tn am , C hin a, Th aila nd ; 201 5 C ost -cons eque n ce s tu dy On e-tim e i ncr eas e i n t he ret ai l p rice o f ci gar et tes by 50 % G ai ns in li fe y ear s A ver ted tr eat m en tco st s A ddi tiona l t ax re ve nue N ot pr es ent ed b ut he al th ga ins and c os t s av in gs w er e f ou nd. M or e he al th a nd fi na nc ia l ga ins for the po or es t 2 0% tha n f or the ric he st 20 % of the po pu la tio n. V er gu et et al .,2 01 7 [22 ] C hi na ; 201 5 Ex ten ded CE A Ex ci se t ax in cr eas ed b y 75 %; sm oke -f ree w or kp laces A ver ted p rem at ur e deat hs C han ge i n t ax rev en ues ; Ou t-of -poc ke t pa ym ent s av er ted ; Pove rty pr eve nt ion N ot pr es ent ed b ut he al th ga ins and c os t s av in gs w er e f ou nd. Si gn ifi can t h eal th an d eco no m ic be ne fit s f or C hi na ’s p op ul at io n, es pe ci al ly f or the p oor . V er gu et et al ., 201 5 [23] C hi na ; 201 1 Ext en de d CE A On e-tim e t ax in cr eas e b y 50 % G ai ns in li fe y ear s Tax rev en ue g ai ns ; H ous eho ld ex pe ndi tur e on to bacco ; T ob acco -r el at ed di seas e co st s; F in an ci al ris k p ro te ctio n N ot pr es ent ed b ut he al th ga ins and c os t s av in gs w er e f ou nd. Su bs tan tial h eal th an d f in an ci al be ne fit s f or h ous eh ol ds in C hi na . H ig as hi e t a l., 201 1 [19] V iet nam ; 200 6 CE A Ex ci se t ax in cr eas ed b y 55 % –85 %; G ra phi c w ar ni ngs o n ci gar et te p ack s; M as s m ed ia cam pai gn s; Sm oki ng ba n in pu bl ic or w or k p laces DAL Ys a ve rte d Int er ve nt ion c os ts M ed ian IC ER (VND p er DAL Y a ve rte d) Tax es in cr eas e b y 5 5% –85 % : V N D 2, 90 0; G ra phi c w ar ni ngs : VND 5 00 ; M ass m ed ia ca m pa ign: V N D 7 8, 300; B an i n pub lic pl ac es : V N D 6 7, 900; B an a t w or k: V N D 3 36, 80 0 A ll in te rv en tio ns w er e c os t-ef fect iv e. T he b es t o pt io ns w er e gr ap hi c w ar ni ng s o n ci gar et te pack et s an d t ax in cr eas es . H a et al ., 201 1 [20] V iet nam ; 200 7 CE A H ea lth e duc at io n t hr oug h m as s m ed ia DAL Ys a ve rte d C os t p er y ear AC ER p er DAL Y sa ve d: VND 12, 32 4, 059 V er y co st -ef fect iv e ( < G D P p er cap ita) A m as s m ed ia ca m pai gn o n to bacco co nt ro l w as am on g t he m os t co st -ef fect iv e int er ve nt io ns . D ona lds on e t al ., 20 11 [24 ] Indi a; 200 8 CE A C om pl et e sm oki ng ba n A M I cas es av er ted ; G ai n i n l ife y ear s C os t p er A M I cas e av er ted ; C os t p er li fe y ear s g ai ne d Int er ve nt ion w as c os t s avi ng A c ost -s av in g alte rn ativ e to th e cu rr en t p ar tia l le gis la tio n i n G uj ar at . D or an et al ., 201 0 [21] V iet nam ; 2 00 6 C ost -cons eque n ce s tu dy Ex ci se t ax in cr eas ed b y 65% –9 0% C ha nge in t he num be r of sm oke rs To tal tax rev en ue N ot p res en ted /les s s m ok er s an d ex tra t ax rev en ues Ef fe ct ive po lic y opt io n f or si m ul ta ne ous ly c ur bi ng t oba cc o us e a nd r ai si ng r eve nue . A sar ia et al ., 200 7 [26] B angl ade sh, C hi na , I ndi a, In do nes ia, P ak is tan , Phi lippi ne s, R us si a, Th aila nd , V ie tn am ; 200 6– 20 15 C ost -cons eque n ce s tu dy In cr eas ed tax es o n toba cc o by 43 .2 %; A sm ok e-fr ee w or kp lace; Lab el lin g o f t ob acco as inj ur ious to he al th; B an on t oba cc o D eat hs av er ted Int er ve nt ion c os t per p er so n p er y ear N ot p res en ted Po pu la tio n-ba se d i nt er ve nt ion th at c ou ld su bs ta ntia lly re du ce m or tal ity fr om ch ro ni c di seas es . N ot es : G TE C =G lo bal T ob acco E co no m ics C on so rti um ; C EA = co st -ef fect iv en es s an al ys is ; V N D = V iet nam es e D on g; D A LY = D is ab ilit y-ad ju st ed li fe year s; IC ER = in cr em en tal co st -ef fect iv en es s r at io ; A C ER = av er ag e co st -ef fect iv en es s r at io ; G D P = G ro ss d om es tic p ro du ct s; A M I=acu te m yo car di al in far ct io n Not es: GTEC=Global Tobac co E conomics C onsor tium; CEA = c ost -eff ec tiv eness analy sis; VND = Vietnamese D ong; D AL Y = Disabilit y-adjust ed lif e years; ICER = incr
emen tal cost -eff ec tiv eness r atio; A CER = a ver age c ost -eff ec tiv eness r atio; GDP = Gr oss domestic pr oduc ts; A MI=acut e m yocar dial infar ction
Quality of reporting
The results for the studies’ reporting quality are presented in Figure 2. The
overall score for the Philips checklist was 56%. The scores for model structure
were generally high, with an average of 66%. The score for data was 51% and
for consistency 44%. Reporting quality scores per item per study are shown in
Appendix Table S4.
Figure 2. Reporting quality of the studies using the Philips checklist
0% 20% 40% 60% 80% 100% Minh et al., (2018) Global TEC (2018) Verguet et al., (2017) Verguet et al., (2015) Higashi et al., (2011) Anh Ha et al., (2011) Donaldson et al., (2011) Doran et al., (2010) Asaria et al., (2007) average Minh et al., (2018) Global TEC (2018) Verguet et al., (2017) Verguet et al., (2015) Higashi et al., (2011) Anh Ha et al., (2011) Donaldson et al., (2011) Doran et al., (2010) Asaria et al., (2007) average Minh et al., (2018) Global TEC (2018) Verguet et al., (2017) Verguet et al., (2015) Higashi et al., (2011) Anh Ha et al., (2011) Donaldson et al., (2011) Doran et al., (2010) Asaria et al., (2007) average Overall Model structure Model data Model consistency
4
Model data
Table S5 in the Appendix shows sources of evidence and quality of model
data for the studies. The quality of disease data in the majority of the studies
was assessed to be moderate, as this evidence was obtained from global
comparative studies.(28)(29) The relative risks of death from tobacco-related
diseases were taken from literature (n = 4) and assessed to be moderate
quality. The most commonly used data sources were the American Cancer
Society’s cancer prevention study (CPS-II) (30) and the WHO’s comparative risk
assessment (CRA) (31) that was based on the CPS-II study. Data on intervention
effects were assessed to be of moderate quality, either because of the low
quality of the study designs in local settings or because studies with
high-quality designs were conducted in Western countries. All of the models used
high-quality data on smoking prevalence obtained from country-specific
surveys. Two studies used disability weights in their models to estimate
averted numbers of DALYs.(19)(20) Other studies only reported reductions
in mortality. High-quality cost data derived from published country-specific
findings were used in all studies to estimate unit costs, inpatient costs, and
intervention costs.
Model bias
On average, about one-third of the models used entailed a high risk of bias
(figure 3). Biases were typically related to quality criteria. More than half of the
models did not meet the following quality criteria: selection of appropriate
models, a sufficient time horizon for capturing the effects of the interventions,
and accurate transition probabilities of the baseline data.
About 40% of the models were found to entail a moderate risk of bias
because of their limited scope. At least one of the four uncertainty principles
(methodological, structural, heterogeneity, and parameter) were not addressed
and the synthesis of data on the effects of interventions was not appropriate.
In addition, disability weights (utility data), which are important for estimating
the true effects of interventions, were not applied in the majority (78%) of the
models. The complete results relating to model bias are shown in Appendix
Table S6.
0% 20% 40% 60% 80% 100%
Structure assumptions No treatment comparator Wrong model bias Limited time horizon
Data identification Baseline data Intervention effects Quality of life weights Non transparent data
Limited scope bias Internal consistency Overall Bias related to structure Bias related to data Bias related to consistency
Low Moderate High Not applicable
Figure 3. Risk of bias (ECOBIAS) in the reviewed models
DISCUSSION
Nine studies that used decision models to assess the long-term costs and
effects of population-based tobacco interventions in Asia were identified. The
studies exhibited a high degree of heterogeneity in terms of how the decision
problems were formulated, the scope of the models, and the modelling
approaches applied. Our results indicated a considerable room for improving
overall levels of transparency in reporting and the quality of the models.
The average score for the reporting quality of all studies was 56%. Although
poor reporting does not necessary lead to model bias, a lack of transparency
undermines assessments of model bias. Notably, we found indications of a
high risk of bias in about 33% of the models, while another 40% of the models
were found to be associated with a moderate risk of bias.
The model type was a primary cause of model bias. A static
compartmental model, entailing the assumption that the introduction of
4
a population-based tobacco intervention would immediately reduce the
number of premature deaths, was applied in several studies. This type of
model ignores demographic changes and prohibits the use of discount rates
for incorporating time preferences within estimates of costs and benefits.
Although these static models were associated with rich outcome measures
for income distribution relating to additional tax revenues, averted treatment
costs, averted out-of-pocket payments, and poverty prevention resulting from
tobacco control interventions, they do not yield any insights into the timing or
delay of intervention effects and savings.
A second source of bias concerned the model input data, as quality of
life effects were ignored in most of the models, with the exception of two
studies. One study obtained quality of life weights from a neighbouring
country and another reported personal communication as a source without
providing a reference. Thus, in most of the studies, the health benefits derived
from tobacco control were underestimated as a result of an exclusive focus on
life years gained.
Moreover, most of the parameters of the intervention effects were
assessed to be of moderate quality, as they were derived from global
comparative studies and/or studies conducted in Western countries. By
contrast, baseline data on smoking prevalence, obtained from country-specific
tobacco surveys, were used in all of the models.
To the best of our knowledge, this is the first Asia-focused review
conducted to investigate the potential risk of bias entailed in models used for
economic evaluations of tobacco control interventions. In a previous review,
existing models applied in tobacco research were categorized by type.(8) In
another review conducted in 2017, models used for economic evaluations of
smoking cessation interventions were assessed for quality, but not for bias or
level of evidence. (9) The study indicated that all of the models lacked one
or more key attributes required for full transferability to a new context. (9)
Moreover, the majority of the models (58 out of 60) originated in Western
countries or in Australia, and only two were applied in Asia. Our review
identified nine different models applied in an Asian setting.
(32) The network of Asian HTA agencies, HTAsiaLink, was formally established
in 2011, yet not all countries have as of yet joined this initiative.(33) Countries
are at different stages to adapt HTA within their unique health care system,
and facing different challenges (34). The efforts are mainly focused on using
HTA in support of medicine pricing and reimbursement decisions, rather than
population level prevention policy.(32)(34)(35) This may partly explain why
relatively little original modeling efforts could be identified.
In addition, direct transfer of models/study results from Western
countries has its limitations, since as indicated in literature Asian countries
are in the early stage of the tobacco epidemic.(36) In particularly, this is
associated with a relatively low level of the relative risk of death from smoking
in Asia, which is a key model parameter in most analyses.(37) Therefore, true
intervention effects could be over-estimated when transferred directly from
models built for use in Western countries.
A strength of this review was that a comprehensive method for assessing
model bias was used, for the three key areas of model bias: structure, data,
and consistency.(16) Also, our review encompassed a broad set of
population-based tobacco control interventions proposed under the WHO’s MPOWER
initiative. (3)
A limitation of our review concerns the search method. The electronic
database searches were restricted to studies authored in English, and we did
not search the grey literature. Apart from practical reasons, the advantage
of this restriction might be that the identified studies could be more likely
to follow reporting guidelines and have a comparable level of information.
However we cannot exclude having missed some good quality studies being
published in a non-English journal, or in grey literature. Furthermore, we
limited our review to population-based tobacco control interventions, which
have been endorsed by the WHO as being effective and efficient, and require
more complex modeling than individual-based interventions.
Our studies span the time frame from 2007 to 2018, which has seen
increasing attention for model quality. Our checklists were published in
2004 and 2005 and hence reflect standards that were available to all studies
reviewed. The models in our review, however, show improvement over time,
4
mostly so since three recent studies. (22)(23)(25) All used a well-established
compartment model developed by the Asian development bank.(38)
It is noteworthy that some studies attempted not only to assess the
impacts of the investigated interventions on health and total costs but they
also examined the contributions of these interventions to the prevention of
poverty and the avoidance of catastrophic health expenditure. Economic
modelling thus served to inform other national goals, including universal
health coverage, the UN sustainable development goals and the WHO-FCTC
objectives.(39)(40)
To satisfy quality standards, future model-based EE studies on tobacco
control in Asia could use preferably a dynamic model which tracks the
population over a longer time-horizon, and presents properly discounted net
present values as outcomes. To include all health benefits, next to mortality,
impact on chronic smoking related diseases should be included in the model.
Preferably it should allow to analyse the dynamics of smoking cessation and
initiation and how these respond to policy by explicitly modeling changes
in tobacco use behavior (e.g., initiation, cessation and relapse), depending
on local data availability. Where possible, the key model parameters need to
be based on country-specific data or else on locally relevant sources. Finally,
transparent reporting practice following commonly used guidelines for the
reporting of economic evaluation studies could minimize the risk of bias in
model-based EE studies in Asia.(13)(14)
Smoking will remain a major public health problem in most Asian
countries over the coming decades.(37) Therefore, in line with global
initiatives, Asian countries should attempt to implement population-based
interventions to end the tobacco epidemic in this region. Towards this goal,
countries may develop economic models to evaluate public health policy as
part of their HTA initiatives, especially in resource-limited settings where
large-scale experiments are not feasible.(41) Clearly, an appropriate methodology
and the availability of reliable local data as well as guidance on how to link
existing local data to international additional data in an effective manner are
prerequisites of high-quality modelling studies.(6)
CONCLUSION
Model-based economic evaluations are an efficient way of informing policy
makers and supporting their decisions regarding the best mix of interventions
at population-level. However, this requires the availability of high-quality
models. Currently, many studies in Asia do not meet this standard and
consequently do not attain their goal of adequately supporting decision
making. While newer models perform better than less recent ones, much
can be gained. Next to this, more local empirical studies would improve the
availability of model input parameters. In addition, model developers should
pay attention to the structure of their models and ensure the consistency of
evidence used to obtain reliable outcomes.
ACKNOWLEDGEMENTS
The authors would like to acknowledge Sjoukje van der Werf, a medical
information specialist at the Central Medical Library of University Medical
Center Groningen, for her assistance in designing the search strategy. The
autors also thank anonymous reviewers for their worthwhile comments and
suggestions.
4
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4
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SUPPLEMENTARY MATERIAL
S1. Search terms
PubMed
(“Asia”[Mesh] OR “Far East”[Mesh] OR “Thailand”[Mesh] OR asia*[tiab] OR Banglades*[tiab]
OR Bhutan*[tiab] OR India*[tiab] OR Indonesia*[tiab] OR Myanmar*[tiab] OR Nepal*[tiab]
OR Sri Lanka*[tiab] OR Thai*[tiab] OR Cambodia*[tiab] OR China[tiab] OR chinese[tiab] OR
Hong Kong[tiab] OR hongkong[tiab] OR Japan*[tiab] OR Laos[tiab] OR Malaysia*[tiab] OR
Mongolia*[tiab] OR Philippin*[tiab] OR Korea*[tiab] OR Singapor*[tiab] OR Viet Nam*[tiab]
OR vietnam*[tiab] OR ((low income*[tiab] OR middle income[tiab]) AND countr*[tiab])
OR (low resource[tiab] AND (setting*[tiab] OR countr*[tiab])) OR Maldiv*[tiab] OR Cook
island*[tiab] OR Fiji*[tiab] Or Kiribati*[tiab] OR Nauru*[tiab] OR Niue*[tiab] OR Palau*[tiab]
OR Guinea*[tiab] OR Samoa*[tiab] OR Tonga*[tiab] OR Tuvalu*[tiab] OR Vanuatu*[tiab])
AND
(“Tobacco”[Mesh] OR “Smoking”[Mesh] OR “Tobacco Use”[Mesh] OR “Tobacco
Products”[Mesh] OR “Smokers”[Mesh] OR “Smoke-Free Policy”[Mesh] OR “Nicotine”[Mesh]
OR “Tobacco Use Disorder”[Mesh] OR “Smoking Prevention”[Mesh] OR “Smoking
Cessation”[Mesh] OR “Tobacco Industry”[Mesh] OR tobac*[tiab] OR cigar*[tiab] OR
smok*[tiab] OR antismok*[tiab] OR nicotine*[tiab])
AND
(“Economics”[Mesh] OR econom*[tiab] OR cost[tiab] OR costs[tiab] OR costl*[tiab]
OR pharmacoeconomic[tiab] OR costing[tiab] OR budget[tiab] OR financ*[tiab] OR
expenditur*[tiab])
AND
(“Models, Economic” [Mesh] OR “Models, Theoretical”[Mesh:NoExp] OR “Decision Support
Techniques”[MeSH] OR “Computer Simulation”[Mesh] OR “Markov Chains”[Mesh] OR
model*[tiab] OR econometric*[tiab] OR markov[tiab] OR decision tree*[tiab] OR discrete
event*[tiab] OR analytic method*[tiab] OR simulat*[tiab])
4
S2. Eligibility criteria
Inclusion criteria for this review
1. Population: study population must be consists of at least one of coun-tries in Asia.
o Selected countries
o General population aged over 15+
Pregnant women, workers, children, second hand smokers are excluded.
YES NO Unclear
2. Intervention: population-based national and international tobacco control interventions must be assessed.
o At least one population-based intervention must be assessed. o Protect people from tobacco smoke
o Warn about the danger of tobacco
o Enforce bans on tobacco advertising, promotion o Raises taxes on tobacco
Face-to face intervention: cessation treatments, counsels, nicotine re-placements treatments, quit line are excluded.
YES NO Unclear
3. Study design: full economic evaluation study must be conducted. o Cost-effectiveness analysis
o Cost benefit analysis o Cost utility analysis o Cost minimization analysis
Observational, trail-based economic evaluation study, qualitative and case studies, randomized controlled trials, experimental studies, valida-tion studies, adverse drug event studies, (systematic) reviews, editorials, letters, dissertations, books, commentaries and meeting abstracts are excluded.
YES NO Unclear
4. Analytic approach: model-based economic evaluation must be ap-plied.
o Markov model o Decision tree o Monte Carlo o Dynamic/static
Trial-based economic evaluations are excluded.
YES NO Unclear
5. Exposure:
o Active smoking exposure
Second hand smoking exposure, air pollution will be excluded.
YES NO Unclear
6. Research article must be: A. In English B. Peer-reviewed C. A full-text publication
S3. Quality of reporting checklists
The full checklist is provided [1].
# Questions to considerStructure
S1 Is there a clear statement of the decision problem?
S1 Is the objective of the evaluation and model specified and consistent with the stated decision problem? S1 Is the primary decision-maker specified?
S2 Is the perspective of the model stated clearly?
S2 Are the model inputs consistent with the stated perspective? S2 Has the scope of the model been stated and justified?
S2 Are the outcomes of the model consistent with the perspective, scope and overall objective of the model? S3 Is the structure of the model consistent with a coherent theory of the health condition under evaluation? S3 Are the sources of data used to develop the structure of the model specified?
S3 Are the causal relationships described by the model structure justified appropriately? S4 Are the structural assumptions transparent and justified?
S4 Are the structural assumptions reasonable given the overall objective, perspective and scope of the model?
S5 Is there a clear definition of the options under evaluation? S5 Have all feasible and practical options been evaluated? S5 Is there justification for the exclusion of feasible options?
S6 Is the chosen model type appropriate given the decision problem and specified causal relationships within the model?
S7 Is the time horizon of the model sufficient to reflect all important differences between options? S7 Are the time horizon of the model, the duration of treatment and the duration of treatment effect
de-scribed and justified?
S8 Do the disease states (state transition model) or the pathways (decision tree model) reflect the underlying biological process of the disease in question and the impact of interventions?
S9 Is the cycle length defined and justified in terms of the natural history of disease? Data
D1 Are the data identification methods transparent and appropriate given the objectives of the model? D1 Where choices have been made between data sources, are these justified appropriately?
D1 Has particular attention been paid to identifying data for the important parameters in the model? D1 Has the quality of the data been assessed appropriately?
D1 Where expert opinion has been used, are the methods described and justified?
D2 Is the data modelling methodology based on justifiable statistical and epidemiological techniques? D2a Is the choice of baseline data described and justified?
D2a Are transition probabilities calculated appropriately?
D2a Has a half-cycle correction been applied to both cost and outcome? D2a If not, has this omission been justified?
4
S3. (continued) Quality of reporting checklists
# Questions to consider Data
D2b If relative treatment effects have been derived from trial data, have they been synthesised using appropriate techniques?
D2b Have the methods and assumptions used to extrapolate short term results to final outcomes been documented and justified?
D2b Have alternative assumptions been explored through sensitivity analysis?
D2b Have assumptions regarding the continuing effect of treatment once treatment is complete been documented and justified?
D2c Are the costs incorporated into the model justified? D2c Has the source for all costs been described?
D2c Have discount rates been described and justified given the target decision-maker? D2d Are the utilities incorporated into the model appropriate?
D2d Is the source for the utility weights referenced?
D2d Are the methods of derivation for the utility weights justified?
D3 Have all data incorporated into the model been described and referenced in sufficient detail? D3 Has the use of mutually inconsistent data been justified (i.e. are assumptions and choices
appropri-ate)?
D3 Is the process of data incorporation transparent?
D3 If data have been incorporated as distributions, has the choice of distribution for each parameter been described and justified?
D3 If data have been incorporated as distributions, is it clear that second order uncertainty is reflected? D4 Have the four principal types of uncertainty been addressed?
D4 If not, has the omission of particular forms of uncertainty been justified?
D4a Have methodological uncertainties been addressed by running alternative versions of the model with different methodological assumptions?
D4b Is there evidence that structural uncertainties have been addressed via sensitivity analysis? D4c Has heterogeneity been dealt with by running the model separately for different subgroups? D4d Are the methods of assessment of parameter uncertainty appropriate?
D4d If data are incorporated as point estimates, are the ranges used for sensitivity analysis stated clearly and justified?
Consistency
C1 Is there evidence that the mathematical logic of the model has been tested thoroughly before use? C2 Are any counterintuitive results from the model explained and justified?
C2 If the model has been calibrated against independent data, have any differences been explained and justified?
C2 Have the results of the model been compared with those of previous models and any differences in results explained?
S4. Quality of sources of evidence
The full checklist is provided [2].
Reference Level of quality of evidence used proposed by Cooper “Use of
evidence in decision models” *** Decision
Demographic data
Registration -official sources –from same jurisdiction High Census - from same jurisdiction High Revised projection Moderate Recently published evidence ( economic evaluation, report, data synthesis) Moderate Unsourced previous evidences Low Expert opinion ( author assumption ) Low
Smoking prevalence data
Periodic surveys conducted using the standardized survey methods -– for
country of interests High Observational studies (Surveys, cross-sectional studies ) – same jurisdiction High Recently published previous evidences (economic valuation, report, – same
jurisdiction Moderate
Recently published observational studies – different jurisdiction Moderate Unsourced data from previous observational studies – Low Expert opinion ( assumption, approximation) Low
Relative risks data
Meta-analysis of cohort studies - same jurisdiction. High Single cohort study – same jurisdiction High Meta-analysis of cohort studies –different jurisdiction Moderate Single cohort study – different jurisdiction Moderate Recently published evidence - (economic evaluation, quality evidence, data
synthesis studies) Low
Expert opinion Low
Diseases data
1 Case series or analysis of reliable administrative databases specifically con-ducted for the study covering patients solely from the jurisdiction of interest High 2 Recent case series or analysis of reliable administrative databases covering patients solely from the jurisdiction of interest High 3 Recent case series or analysis of reliable administrative databases covering
patients solely from another jurisdiction Moderate 4 Old case series or analysis of reliable administrative databases. Moderate 5 Estimates from previously published economic analyses: unsourced Low
6 Expert opinion Low
Costs data
1 Cost calculations based on reliable databases or data sources conducted for specific study – same jurisdiction High 2 Recently published cost calculations based on reliable databases or data sources – same jurisdiction High 3 Unsourced data from previous economic evaluation – same jurisdiction Moderate 4 Recently published cost calculations based on reliable databases or data
sources – different jurisdiction Moderate 5 Unsourced data from previous economic evaluation – different jurisdiction Low
4
S4. (continued) Quality of sources of evidence
Reference Level of quality of evidence used proposed by Cooper “Use of evidence in decision models” *** Decision
Intervention effect data
1+ Meta-analysis of RCTs with direct comparison between comparator therapies, measuring
final outcomes. High
1 Single RCT with direct comparison between comparator therapies, measuring final
out-comes High
2+ Meta-analysis of RCTs with direct comparison between comparator therapies, measuring
surrogate outcomes High
Meta-analysis of placebo-controlled RCTs with similar trial populations, measuring final
outcomes for each individual therapy High 2 Single RCT with direct comparison between comparator therapies, measuring surrogate
outcomes High
Single placebo-controlled RCTs with similar trial populations, measuring final outcomes for
each individual therapy High
3+ Meta-analysis of placebo-controlled RCTs with similar trial populations, measuring
surro-gate outcomes Moderate
3 Single placebo-controlled RCTs with similar trial populations, measuring surrogate
out-comes for each individual therapy Moderate 4 Case-control or cohort studies Moderate 5 Non-analytic studies, for example, case reports, case series Low
6 Expert opinion Low
Utility weights data (DALY, QALY, Life years)
1 Direct utility assessment for the specific study from a sample: a) of the general population
b) with knowledge of the disease(s) of interest c) of patients with the disease(s) of interest
High 1 Indirect utility assessment from specific study from a patient sample with disease(s) of
interest: using a tool validated for the patient population
High 2 Indirect utility assessment from specific study from a patient sample with disease(s) of
interest using tool not validated for the patient population 3 Direct utility assessment from a previous study from a sample either:
a) of the general population
b) with knowledge of the disease(s) of interest c) of patients with the disease(s) of interest
3 Indirect utility assessment from previous study from patient sample with disease(s) of interest: using tool validated for the patient population
4 Indirect utility assessment from previous study from patient sample with disease(s) of
interest: using tool not validated for the patient population or method of elicitation unknown Moderate 5 Patient preference values obtained from a visual analogue scale Low 6 Delphi panels, expert opinion Low Resource use
data
Prospective data collection or analysis of reliable administrative data for specific study High Recently published results of prospective data collection or recent analysis of reliable admin-istrative data: same jurisdiction High Unsourced data from previous economic evaluations: same jurisdiction Moderate Recently published results of prospective data collection or recent analysis of reliable
admin-istrative data: different jurisdiction Moderate Data source not known: different jurisdiction Low
S5. ECOBIAS checklist for bias in economic evaluation
The full checklist is provided [3].
Type of bias Issues addressed (question to consider)
Bias related to structure
1 Structural as-sumptions bias Is the model structure in line with coherent theory? Do treatment pathways reflect the nature of disease?
2 No treatment comparator bias Is there an adequate comparator, i.e. care as usual?
3 Wrong model bias Is the model chosen adequate regarding the decision problem?
4 Limited time horizon bias Was a lifetime horizon chosen? Were shorter time horizons adequately justified?
Bias related to data
5 Bias related to data
identifica-tion
Are the methods of data identification transparent? Are all choices justified adequately? Do the input parameters come from high quality and well-designed studies?
6 Bias related to baseline data Are probabilities, for example, based on natural history data? Is trans-formation of rates into transition probabilities done accurately?
7 Bias related to treatment effects
Are relative treatment effects synthesized using appropriate meta analytic techniques? Are extrapolations documented and well justified? Are alternative assumptions explored regarding extrapolation?
8 Bias related to quality-of-life
weights (utilities)
Are the utilities incorporated appropriate for the specific decision problem?
9 Non-transparent data
incorpora-tion bias
Is the process of data incorporation transparent? Are all data and their sources described in detail?
10 Limited scope bias Have the four principles of uncertainty (methodological, structural, heterogeneity, parameter) been considered?
Bias related to consistency
11 Bias related to internal
consis-tency
Has internal consistency in terms of mathematical logic been evaluated?
4
Table S2.
O
ver
view of studies included in the r
eview
A uthor , public ation year Tar get popula tionSetting Baseline year Study design Persp
ec tiv e Compar at or In ter ven tion Choic e of out comes ICER/r esults Polic y advic e/ conclusion Eff ec ts Costs M inh et al ., 2018[4] Vietnam 2017 Gener al popula tion Cost consequenc e study ; Not repor ted NA Cigar ett e tax es incr ease b y: 75%- 85% Number of mor talit y Sa ved mor talit y cost .
Price increased to 5.7%; SADs=63,339 osts=577 million U$ Price increased to 10.5% SADs=116,678 Costs=1063 million U$ Price increased to 20.9% SADs=232,244 Costs=2117 million U$ Price increased to 52.3% SADs=581,165 Costs=5296 million U$
Incr
easing the cigar
ett e tax c ould r educ e the substan tial health impac t of t obac co use , and fur ther r esult in sig nifican t financial sa vings acr oss societ y. GTEC., 2018 [5]
India Indonesia Bangladesh Philippines Vietnam China Thailand 2015 Male smokers
Cost -consequenc e study ; Not repor ted W ithout in ter ven tion One -time 50% incr ease in the r etail pr ic e of cigar ett es Lif e y ear gains Av er ted tr ea tmen t
costs Additional tax rev
enue
Total life year gained (in million): India: 44.7; Indonesia: 56.8 Bangladesh: 17.2; Philippines:14.7 Vietnam: 14.3; China: 241; Thailand: 13 Disease cost averted (adjusted for $ PPP, in million) India: 3488; Indonesia: 13350 Bangladesh: 507; Philippines: 1964 Vietnam: 919; China: 114180 Thailand: 2575 Additional tax revenues (adjusted for $ PPP, in billion) India: 10.4; Indonesia: 16.4; Bangladesh: 2.6; Philippines: 1.5; Vietnam: 2.4; China: 66.3; Thailand: 3.6
H igher pr ic es of cigar ett es pr ovide mor e
health and financial gains t
o the poor est 20% than t o the r ichest 20% of the popula tion. H igher e xcise tax es suppor t the tar gets
of the sustainable dev
elopmen t goals on non-communicable diseases and po ver ty , and pr ovide financial pr ot ec tion against illness . Ver guet et al ., 2017 [6]
China 2015 Male popula
tion Ex tended cost -eff ec tiv eness analy sis; Consumer perspec tiv e W ithout in ter ven tion In t 1: Ex cise tax incr ease: retail pr ic e of cigar ett es b y 75% Int 2: Smoke -fr ee w or kplac es Av er ted pr ema tur e dea ths
Change in tax rev
enue; Av er ted out of pocket paymen t Pr ev en ted po ver ty cases; Pr ev en ted ca tastr ophic expenditur e
Int 1: Avert 24 million premature deaths; Additional US$ 47 billion revenues gains ; Prevent 9 million poverty case; Averted OOP US55$ billion; Prevented 16 million cases of catastrophic expenditure Int 2:Avert 12 million premature deaths; Decrease tax revenue by US$ 7 billion; Prevent 4 million poverty cases
Incr eased e xcise tax es on t obac co pr oduc ts and w or kplac e smok ing bans can pr ocur e lar ge health and ec onomic benefits t o the Chinese popula tion, especially
among the poor
Table S2.
(c
on
tinued) O
ver
view of studies included in the r
eview
A uthor , public ation year Tar get popula tionSetting Baseline year Study design Persp
ec tiv e Compar at or In ter ven tion Choic e of out comes ICER/r esults Polic y advic e/ c onclusion Eff ec ts Costs Ver guet et al ., 2015 [7]
China 2011 Male popula
tion Ex tended cost -eff ec tiv eness analy sis Consumer perspec tiv e No pr ic e incr ease
One time tax incr
ease b y 50% Lif e y ear gains Tax r ev enue
gains; Household expenditur
e on t obac co; Tobac co -rela ted diseas c osts; Financial r isk pr ot ec tion Av er
ted mean (95% UR)
Years of lif
e gained:
231 million (194-268); Additional tax r
ev enues: $703 billion (616-781) Total e xpenditur e on t obac co: $376 billion (232-505) Decr eased t obac co -r ela ted disease c ost: $24 billion (17-26) Financial pr ot ec tion: $1.8 billion (1.2-2.3) Incr eased t obac co taxa tion can be a pr o-poor polic y instrumen t tha t br ings substan tial health
and financial benefits t
o households in China H igashi et al ., 2011[8] Vietnam 2006 Gener al popula tion CEA Gov er nmen t Sta tus quo sc enar io In t 1: Ex cise tax incr ease fr om 55% t o 85%; In t 2: Gr aphic w ar ning labels on cigar ett es pack ; In t 3: M ass media campaig ns; In t 4: Smok ing ban in public (wor k) plac e D ea th av er ted In ter ven tion costs
ICER median (95% UI): (VND per D
AL Y a ver ted) Gr aphic ban: 500 (300- 1200) Tax es : 55%-85% 2900(1100-6700) Tax es:55%-75% 4200(1700-9900) Tax es: 55%-65% 8600(3400-20100) Smok
ing ban public: 67900 (28200- 33200)
M
ass media campaig
n:78300 (43700-176300) Smok ing ban w or k: 336800 (169300-822900) A ll v alues w er e nega tiv e ICERs , which indica te tha t the in ter ven tions ar e all c ost sa ving . T he go ver nmen t ma y initially consider g raphic w ar ning
labels and tax incr
ease , follo w ed b y other in ter ven tions . Ha et al ., 2011 [9] Vietnam 2007 Gener al popula tion CEA Societal W ithout in ter ven tion Health educa tion thr ough the mass media D ea th av er ted
Cost per year
Costs per y ear : VND 89 billions D AL Ys a ver ted per y ear : 7250 ACER per D AL Y sa ved: VND 12 324 059 Ver y c ost -eff ec tiv e ( < GDP per capita ) Health educa tion pr og ram to r educ e salt in take and a c ombined mass media on salt , t obac co and cholest er ol ar e the most c ost -eff ec tiv e in ter ven
tions and should
pur
4
Table S2.
(c
on
tinued) O
ver
view of studies included in the r
eview
A uthor , public ation year Tar get popula tionSetting Baseline year Study design Persp
ec tiv e Compar at or In ter ven tion Choic e of out comes ICER/r esults Polic y advic e/ c onclusion Eff ec ts Costs D onaldson et al ., 2011[10] Gujar at gener al popula tion (aged > 20) India 2008 CEA ; Societal Par tial smok ing ban Complet e smok ing ban cases aver ted Lif e y ears sa ved Cost per L Y gains
Base case (optimistic
-w orst); Av er t A MI cases: 17,478 (53, 361-13,109); Lif e y ear gains:
437,589(89,1945-45,268) ICER per lif
e y
ear gained w/out medical
tr ea tmen t: US$ 9.13 (2.24-112) ; C ost per A MI case a ver ted: US$229 (37-387) Implemen ting a c omplet e smok ing ban w ould be a c ost sa ving alt er na tiv e to the cur ren t par tial leg isla tion in t er ms of r educing t obac co -attr ibutable disease in Gujar at . D or an et al ., 2010 [11] G ener al popula tion Vietnam, 2006 Cost consequenc e study Gov er ;nmen t
Business as usual Excise tax
es lev
el
modeled t
o
65%, 75% and 90% Change in number of smokers
Total tax
es
rev
enue
including excise tax rev
enue and
VAT
Number of smokers: 12.3 million in 2006 t
o 13.9 million in 2016 Total tax es r ev enue: NPV , USD billion;
Base case=USD 5.97 Excise tax r
at
e= 55% US$10.35-US$10.95
Ex
cise tax r
at
e=75% US$10.42- US$11.69
Ex
cise tax r
at
e=90% US$10.33- US$12.76
Taxa tion incr eases ar e an eff ec tiv e polic y option tha t can be used b y Vietnam go ver nmen t to simultaneously cur b tobac co use and r aise rev enue . A sar ia et al ., 2007 [12]
Bangladesh China India Indonesia Pak
istan
Philippines Russia Thailand Vietnam; 2006-2015
Cost consequenc e study ; Ref er enc e popula tion
= SIR method Int 1: Incr
eased tax es on tobac co; In t 2: smoke -fr ee w or kplac e; In t 3: Labelling of tobac co; In t 4: Ban on to ba cco D ea th av er ted In ter ven tion
cost per person per y
ear
D
ea
th a
ver
ted: China: 4.5 million;
India: 3.1 million; Combined c
ost of smok ing in ter ven tions f or
cost per person per y
ear
:
Bangladesh: 0.11USD; China: 0.14USD India: 0.16 USD; Indonesia: 0.12USD; Pak
istan:0.23 USD;
Philippines: 0.13USD; Russia:0.49USD; Thailand:017USD; Vietnam : 0.11USD
Popula tion-based in ter ven tion str at eg ies could be substan tially reduc es mor talit y fr om chr onic diseases , and
makes a major (and affor
dable) c on tr ibution to w ar ds achiev emen t of
the global goal t
o pr ev en t and c on tr ol chr onic diseases .
Table S3.
Char
ac
ter
istics and struc
tur
e of the models
A uthor , public ation year M odel name Ref er enc e M odel t yp e M odel assumption Smok ing ca tegories Tr ansition ra te s Smok ing rela ted diseas Rela tiv e risk of smok ing Sub -analy sis Time hori -zo n D isc oun t ra te Sensitivit y analy sis M inh et al ., 2018[4] ADB framew or k [13] Sta tic model Av er age PE= 0.25. Initia tion PE=0.15 (0.65- 0.15) M or talit y r at es: 30% t o 50% The cost per dea
th: US$9560.8 Cur ren t smokers Nev er smokers Quit r at e Initia tion r at e A ll-cause mor talit y NA Age group NA NA USA Quit r at e and mor talit y attr ibutable to smok ing GTEC., 2018 [5] ADB framew or k [6] [14] [13] Simple sta tic model Av er age PE -0.4 (-0.2 t o -0.6 ) fr om HIC t o LMIC PE w as t wic e as lar ge in
young PE=-1.27 (15-24) PE=-0.24 (25+) Half of cur
ren
t smokers
and futur
e smokers will die
Smokers lose on a ver age 10 years R isk r educ tion b y age acr oss inc ome g roups Cur ren t smoker TR=NA COPD , Str oke , IHD disease , lung canc er (dea ths) NA
Age roup; income quin
tile CS NA USA Pric e elasticit y Ver guet et al ., 2017 [6] Based on #4. Sta ted tha t valida ted
model and refer
red t o pr evious study . Simple sta tic model In t 1: P ric e elasticit y w as -0.38. It w as t wic e as lar ge in
younger smokers (15-24 and older) Int 2: One
-time r educ tion in smok ing pr ev alenc e b y 9% Cur ren t smokers TR=NA COPD , Str oke , Hear t disease , Neoplasm (dea ths) RR f or pr ema -tur e mor talit y by age a t quitting
Age group; Income quin
tile CS NA USA Pric e elas -ticit y Br and swith -ing Change in prev alenc e Po ver ty thr eshold ADB: A sian dev elopmen t bank ; PE: pr ic e elasticit y; USA: univ ar ia te sensitivit y analy sis; TR: tr ansition r at
e; NA: not applicable; HIC: high inc
ome c oun tr ies; LMIC: lo w -middle inc ome c oun tr ies ; CS: c ohor t simula tion
