Within a smoking-cessation program, what impact does genetic information on lung cancer need to have to demonstrate cost-effectiveness?
© Gordon et al; licensee BioMed Central Ltd. 2010
Received: 20 January 2010
Accepted: 16 September 2010
Published: 16 September 2010
Many smoking-cessation programs and pharmaceutical aids demonstrate substantial health gains for a relatively low allocation of resources. Genetic information represents a type of individualized or personal feedback regarding the risk of developing lung cancer, and hence the potential benefits from stopping smoking, may motivate the person to remain smoke-free. The purpose of this study was to explore what the impact of a genetic test needs to have within a typical smoking-cessation program aimed at heavy smokers in order to be cost-effective.
Two strategies were modelled for a hypothetical cohort of heavy smokers aged 50 years; individuals either received or did not receive a genetic test within the course of a usual smoking-cessation intervention comprising nicotine replacement therapy (NRT) and counselling. A Markov model was constructed using evidence from published randomized controlled trials and meta-analyses for estimates on 12-month quit rates and long-term relapse rates. Epidemiological data were used for estimates on lung cancer risk stratified by time since quitting and smoking patterns. Extensive sensitivity analyses were used to explore parameter uncertainty.
The discounted incremental cost per QALY was AU$34,687 (95% CI $12,483, $87,734) over 35 years. At a willingness-to-pay of AU$20,000 per QALY gained, the genetic testing strategy needs to produce a 12-month quit rate of at least 12.4% or a relapse rate 12% lower than NRT and counselling alone for it to be equally cost-effective. The likelihood that adding a genetic test to the usual smoking-cessation intervention is cost-effective was 20.6% however cost-effectiveness ratios were favourable in certain situations (e.g., applied to men only, a 60 year old cohort).
The findings were sensitive to small changes in critical variables such as the 12-month quit rates and relapse rates. As such, the cost-effectiveness of the genetic testing smoking cessation program is uncertain. Further clinical research on smoking-cessation quit and relapse rates following genetic testing is needed to inform its cost-effectiveness.
Smoking remains a substantial health problem in many countries and is the largest modifiable risk factor for several cancers and a host of chronic diseases. Between 1980 and 2004, smoking prevalence in the Australian population dropped from 40% to 21%  partly due to progressive tobacco control policies such as cigarette taxation, smoke-free workplaces and extensive public education campaigns. However, smokers remain a large proportion of the population (21%) as in other European countries (around 30%). It has been proposed that while system-level public health approaches are effective at reducing aggregate smoking levels, a 'one size fits all' approach may not be effective for all types of smokers.
The pivotal paper by Cromwell J et al. (1997) demonstrated the cost-effectiveness of smoking-cessation programs delivered by a general practitioner (GP). Many subsequent smoking-cessation programs have also demonstrated substantial health gains for a relatively low allocation of resources. However, despite being cost-effective, smoking-cessation programs still suffer from low success rates in terms of numbers of quitters at 12-months. As a general guide, the 12-month quit rates are around 6% for brief GP advice, 9% for proactive counselling, 6-12% for nicotine replacement therapies with counselling, and 12-19% for pharmacotherapies with counselling. The extent of relapse following successful smoking-cessation further erodes their effectiveness. This suggests that many smokers may require other measures, such as targeted or personalised information, to encourage cessation and abstinence.
While tobacco smoking is the largest known risk factor for lung cancer occurring in 85-90% of cases, only 10-15% of smokers develop lung cancer. Recent evidence suggests that this may be partly due to differences in genetic susceptibility to lung cancer[7, 8]. That is, the smoking-gene interaction means that some smokers are at greater risk of developing lung cancer, with several host characteristics (i.e., K-ras, GSTM1, CYP2D6, c-MET, NKX2-1, LKB1, BRAF) implicated in lung cancer onset . Further, other genes are implicated in other chronic diseases linked with smoking, therefore smoking-cessation has wider health benefits and therefore is always beneficial.
The genetic link to lung cancer has implications for the design of smoking-cessation programs. Genetic information represents a type of individualized or personal feedback regarding the risk of developing lung cancer, and hence the potential benefits from stopping smoking, may motivate the person to remain smoke-free. Central to this is the potential to address the issue of optimistic bias, the underestimation of one's own risk of a harmful outcome relative to the average smoker. Recent developments in genetics suggests that some people respond well to genetic information about risk of lung cancer [10, 11], are more likely to quit  and perhaps less likely to relapse. Combining a genetic test with a smoking-cessation program might enhance the effectiveness and thus represent a cost-effective intervention.
Several companies now offer genetic testing for lung cancer susceptibility however they offer a single nucleotide polymorphism (SNP) test for lung cancer risk result and no other clinical data is used for their risk assessment. Our author (R.Young) heads a clinical research program at Auckland Hospital, New Zealand, offering patients a SNP-based test involving 20 SNPs and assessment of other clinical variables (family history, COPD, smoking patterns) within usual clinical practice for smoking-cessation. Early results show that intentions to quit smoking among 250 participants based on genetic testing for lung cancer risk were around 88% in those at elevated risk of lung cancer. The economic value of the adopting this new technology into practice is yet to be determined.
To date, no smoking-cessation study has examined the cost-effectiveness of offering genetic tests in the context of disease prevention but other studies have investigated genetic testing to guide the choice of pharmacotherapy among individuals attempting to stop smoking [13, 14]. Genetic testing imposes costs on individuals, doctors and the health system. Thus, if genetic testing is to be offered in addition to a first-line smoking-cessation program, then it must result in enough new quitters (or reduced numbers of relapsers) in order to justify the costs. The purpose of this study was to explore how much of an impact genetic testing information would need to have in order to be a cost-effective addition to a typical smoking-cessation program. Specifically, we assess the net costs, and health benefits of a smoking-cessation program with a genetic test compared with nicotine replacement smoking-cessation treatment.
Markov model structure
Data parameters used in model: description, base case estimate, range tested in one-way sensitivity analyses and sources
Quit rates: 12-month continuous abstinence
a) Genetic Test
b) Usual treatment
Relapse rate after 12-month quit
10% in years 2-6, 4% after1
Lung cancer incidence
Annual from age 40, e.g., 0.0018024 at age 65 years1
Relative risk of lung cancer in heavy smokers compared to general population
Relative risk of lung cancer in ex-smokers compared to general population
Annual from 5-year age group by time since quit e.g, ages 50-55 years RR = 4.751
Survival/mortality rates (background population)
Annual by age e.g, age 65 annual dying rate = 0.009361
ABS Life Tables 2005-072
Survival rates of lung cancer
Annual survival at 1 year 36% to 12% at 5 years
a) early lung cancer
, authors assumption3
b) adv lung cancer
a) Early stage lung cancer (I&II)
b) Adv stage lung cancer (III&IV)
c) No lung cancer
Lung cancer healthcare costs
a) Early lung cancer 1st year (NSCLC only)
b) Adv lung cancer + SCLC 1st year
All ± 30%
c) Ongoing costs (stable disease)
d) Progressive disease
e) Terminal care (final year)
Description of the two strategies
We compared a usual smoking-cessation program with an alternative involving the usual smoking-cessation program and a genetic test some point after (e.g., 6 weeks) completing the program (as per McBride et al. 2002 ). The benefit of this test is to decrease the likelihood that an individual will relapse and begin smoking again as measured by relapse rates at 12 months.
Intervention components and unit costs for usual smoking-cessation (USC) and USC plus genetic test
USC (NRT with telephone counselling)
Standard 5-25 minutes
 MBS item 53
1st step - 21 mg/6 pkts
2nd step - 14 mg/2 pkts
3rd step - 7 mg/2 pkts
Initial + 4 sessions
DVA, $119.75 initial then $83.70/hr
USC + Genetic test
USC as above
Standard 5-25 minutes
MBS online schedule, item 53
Blood sample, transfer to lab and analysis
Explains results of gene test
Assumption - same for quit booklet
Data parameters in the model
The data used to populate the model was based on published literature, national reports and government cancer statistics, however a number of assumptions were also necessary (Additional file 1, Table S1). The key parameters in the model were quit rates in the two arms and, for the genetic test arm, we have assumed that these behaviour changes have occurred regardless of the underlying properties of the genetic test. Systematic reviews and results of meta-analyses were used to inform estimates on 12-month quit rates of NRT  and relapse rates beyond 12 months. Although it is possible to that 'natural' quitters, those needing no assistance to quit smoking, may exist in both groups, we have assumed the natural quit rate is equivalent in both arms. Risk estimates of lung cancer are dependent on gender, time since quitting and smoking frequency and were derived from a cohort study of over 463,000 US men and women. Current epidemiological evidence provided information on background incidence of lung cancer by stage, mortality and survival rates of lung cancer, and all-cause mortality among smokers. To reflect changing estimates as the cohort ages, we accounted for age-dependent variables using tabulated data in our model. Table 1 lists all data estimates and tabled data in the model with their respective sources and ranges tested in the sensitivity analyses.
The measures of benefit in the evaluation were the number of quitters and quality-adjusted life-years gained (QALYs) over 35 years. The number of quitters at 12 months is also presented to highlight the shorter-term impact. The level of effectiveness of smoking-cessation enhanced with a genetic test was based on a randomised clinical trial involving 557 participants. The proportion of individuals achieving continued abstinence at 12 months was 11% compared with 5% in the NRT only arm (p = 0.08). This study was chosen as it included the comparison groups most relevant for an Australian setting, that is, NRT plus counselling with or without a genetic test. McBride's study was also randomized, prospective, used an intention-to-treat analytical approach and included largely lower socio-economic smokers. Three other studies assessing the impact of genetic susceptibility on smoking-cessation [19–21] did not investigate relevant comparators including one with no control group, were non-randomized or had earlier-time quit rates. These quit rates ranged from 6-19%. Evidence for the effectiveness of NRT alone was based on a published systematic review of 136 randomized controlled trials, over 40,000 participants and yielding a summary estimate of 6%. In the absence of outcomes of genetic testing on smoking-cessation beyond 12-months, we assumed relapse rates from the literature were equivalent in the two arms.
The QALY is a generic outcome measure preferred for use in economic evaluations combining survival time adjusted for quality of life. A structured literature review was undertaken to locate recent preference-based quality of life scores (or utility weights) for lung cancer. Eleven studies from 1997-2008 were uncovered. The utility weights used in the present study were based on direct utility assessment using standard gamble interviews  and a second study that used the EuroQol 5D questionnaire . These studies were chosen because utilities were available for advanced/early stage and stable/progressive lung cancer, were more likely to reflect current treatment patterns and side-effects  and reported a range of scores to acknowledge uncertainty[22, 24].
Where C = costs, E = effects (QALYs or quitters), GT = genetic testing arm and USC = usual smoking-cessation arm and represent the additional costs per health benefit of the genetic testing component. Our analysis took a payer perspective when measuring and valuing resources used for the two options. This included two payers; the consumers and health providers and the analysis aggregated the costs from both payers. Direct costs borne by the consumers (smokers) included over-the-counter NRT and the genetic test (Table 2). Health providers primarily bear the cost of lung cancer diagnosis, treatment and follow-up care and health care counselling and advice during smoking-cessation programs. Costs and effects were discounted at 5% and brought forward to 2009 Australian dollars using the health component of the Consumer Price Index.
Sensitivity and scenario analyses
Threshold analyses were undertaken to separately determine at what quit and relapse rates the genetic testing arm was cost-effective. To determine if any variables were primarily driving the cost-effectiveness results, one-way sensitivity analyses on all parameters were undertaken (Table 1). Of particular importance is the 12 month quit rate of 11% following a genetic test compared with the 6% base quit rate . The stability of the results to the quit rates was explored by examining quit rates of 7%, 15% and 22% for the genetic test option, and 3%, 9% and 11% for the usual smoking-cessation program. Relapse rates were also halved to explore the optimistic scenario commonly used in previous work [4, 25, 26]. Break-even analysis was used to identify the quit rate required for the genetic test to be cost-effective compared with usual smoking-cessation.
A probabilistic sensitivity analysis was also performed, re-sampling from nominated distributions of data inputs through 10,000 iterations. Beta distributions were assigned to probabilities (e.g., quit and relapse rates, health state transitions) and gamma distributions were assigned to cost variables because these are often right-skewed. The simulated mean ICER (QALYs) with 95% confidence intervals (CI) was generated. Finally, to assess the structural uncertainty of our model, we re-examined the model for men and women separately because it is well known that men are heavier smokers and have higher risks of lung cancer compared to women. We also explored the model for all persons starting at age 30 and 60 years. During our analyses, we assumed a willingness-to-pay ICER threshold of $20,000 per QALY gained to guide the interpretation of the findings, a level in keeping with higher-end cost-effectiveness ratios found in previous evaluations of smoking-cessation programs.
Results of incremental cost-effectiveness ratios (ICER) in base case and probability sensitivity analysis
Short-term (at end of 12-months)
NRT + counselling
NRT + counselling
+ genetic test
Cost for 1000 persons in each arm
Number of quitters @ 12 months
ICER - per quitter @ 12 months
Long-term (at end of 35 years)
Mean cost per person
QALYs gained per person
ICER - QALYs gained per person
Monte Carlo simulated ICERs
Base case ICER
Initial cohort aged 30 years
Initial cohort aged 60 years
Men only (aged 50 years)
Women only (aged 50 years)
These results suggest an ICER above the threshold level of AU$20,000 per QALY gained. We found that the 12-month quit rate would need to be at least 12.4%, or that the long-term relapse rate needed to be 12% lower, for the genetic testing strategy to be as cost-effective as the usual smoking-cessation strategy (Additional file 1, Figures S1 & S2). The predicted proportions of the cohort who quit or relapsed for both strategies by age are highlighted in Additional file 1, Figure S3 and similarly for those with early and advanced lung cancer in Additional file 1, Figure S4.
Over a short-term 12-month period, for every 1000 individuals undertaking smoking-cessation enhanced with a genetic test, an additional cost of $355,600 would result in 50 additional quitters or $7,112 per additional quitter over 12 months compared with usual smoking-cessation (Table 3).
Sensitivity & scenario analyses
The cost-effectiveness ratios were lower than our base case when applied to men only $27,182 per QALY (95%CI $9,200, $70,783) and higher for women $46,408 per QALY (95%CI $17,199, $118,383) (Table 3). When we assessed the model with younger initial cohort of 30 year olds, the cost per QALY ratios increased to $133,409 (95%CI $53,502 $361,376) and for 60 year olds, decreased to $27,601 (95%CI $8,783, $73,948) (Table 3). If it was assumed that the relapse rate is halved in both strategies (i.e., 5% relapse from years 2-6, 2% thereafter), the mean ICER per QALY gained was $18,623 (95%CI $5,897, $49,228). The relapse rate would need to be zero in both arms, and the quit rate for genetic-testing option at least 18%, for the genetic-testing option to have lower costs and higher effects than usual smoking-cessation. Alternatively, keeping the relapse rate at our base level (10% years 2-6, 4% thereafter), the quit rate for the genetic-testing option needs to be at least 29% to dominate the usual smoking-cessation option.
The purpose of this paper was to examine the potential cost-effectiveness of smoking-cessation via NRT enhanced with genetic information on lung cancer risk using a dynamic model and up-to-date data estimates. Our results suggest that using the 12 month quit rate reported in a previous trial,  the genetic testing option is unlikely to be cost-effective at a threshold of $20,000 per QALY gained. The genetic test option would need to achieve a 12-month continuous quit rate of 12.4% or more for it to be a cost-effective addition to NRT and counselling treatment alone. Alternatively, the genetic testing option would need to achieve relapse rates 12% lower than those for usual smoking-cessation. Although our base ICER $34,687 per QALY is higher than the $20,000 threshold, we emphasize that the high volatility in the model estimates means that the genetic test option could easily become cost-effective if further evidence supported mildly more optimistic quit or relapse rates. However, overall we found very small differences in cost between the two options over a period of 35 years and similarly for differences in effects. The model was very sensitive to small changes in critical variables such as the 12-month quit rates and relapse rates after 12-months, hence the results are unstable. Further research on smoking-cessation quit rates following genetic testing is needed to improve the validity of the values used in our model and reduce the uncertainty of our findings.
Our ICER of $34,687 per QALY gained would be considered cost-effective in relation to accepted thresholds for pharmacological health care treatments in Australia . However, given that we are not assessing a pharmaceutical and that other ICERs of smoking cessation options are among the lowest of all health interventions, we used a $20,000 acceptable threshold . Several studies have shown better health outcomes and cost-savings are possible for varenicline,[26, 28] bupropion  and community pharmacy-led  programs. In this context, it would seem that our genetic testing strategy is a relatively poor investment. However, with at least 30 published studies providing evidence that a wide variety of smoking-cessation interventions are cost effective, these findings may be less favourable because most studies have overestimated long-term effectiveness due to assumptions made with smoking relapse rates or evaluation time frames being too short. In our study, the use of improved epidemiological data on the risk of developing lung cancer separating risk estimates by gender, time since quitting and heavy/light smoking patterns  should provide more precise cost-effectiveness estimates.
Traditionally, men are heavier smokers than women and their relative risk of lung cancer is higher. This explains the lower (more favourable) ICERs for men because they have relatively higher numbers of life-years to gain from stopping smoking[5, 25]. However, due to the large uncertainty in the model, differences between men and women were tenuous. The benefits of smoking-cessation can occur at any age of quitting however, the risk of lung cancer among ex-smokers versus non-smokers remains elevated even after more than 40 years of cessation. Our findings are in contrast to other studies where smoking-cessation among younger cohorts has more favourable cost-effectiveness than for older cohorts. Our opposite finding results from the fact that a given percentage of people who quit at 12 months are assumed to relapse each year, meaning that some (younger) people will start smoking again before the benefits of not smoking (avoided cancer) are realized. Additional research is need to identify whether relapse rates for younger smokers would in fact remain low after receiving positive results from a genetic test.
When our model was re-assessed for 30 year olds, the long-term effects were severely eroded due to discounting and relapse rates. Therefore, the overall effectiveness was very small, inflating the cost-effectiveness ratio. This finding would indicate that the genetic testing arm is potentially suitable only in older (at least 50 year olds), long-term smokers or that the NRT and counselling needs to be repeatedly offered in relapsed smokers  and is not cost-effective as a one-off intervention.
Our choice of relapse rates is an important variable in our model both in terms of the values used, which were taken from a meta-analysis, and the 35 year model duration. These have a combined effect of having a cumulative lifetime relapse of 78% (subject to some quitters dying before they are able to relapse), considerably higher than studies using Markov models with lifetime relapse rates of 35%[4, 25]. When the base case relapse rates were halved and closer to those used previously, the cost-effectiveness ratios were substantially lower; $18,623 (95%CI $5,897, $49,228).
While our model was responsive to an ageing cohort and other time-dependent variables, some limitations are apparent and a number of assumptions were necessary. Data estimates are based on those available in published randomized controlled trials and may not reflect real-world practice (e.g., overestimated effects or compliance from experimental trial data). It is acknowledged that many individuals permanently cease smoking on their own accord with no psychological or pharmacological assistance. The present study examines the relative effectiveness of a smoking cessation program compared with a smoking cessation program given in conjunction with a genetic test. Extensive sensitivity analyses explored parameter uncertainty and aspects of the structural uncertainty (e.g., different cohort profiles). We relied on a single, randomized clinical trial by McBride et al. (2002) for a critical estimate, quit rate at 12-months following the genetic test. This study was US-based and involved a largely African-American lower-socioeconomic cohort. Arguably, McBride et al.'s sample of mostly lower-socioeconomic smokers may be a difficult group to intervene in but likely to be relevant and generalisable to other settings like Australia where a higher proportion of disadvantaged people also smoke. Potentially adverse consequences of genetic testing include emotional distress, concerns about discrimination and implications for telling family members positive results. These issues were omitted from our analysis. Our results relate to QALYs gained from preventing lung cancer onset and we did not incorporate improved survival gains due to the potential avoidance of other major diseases linked to smoking (e.g., heart disease, COPD, diabetes). Again, the impact is that our effects may be underestimated and overall ICERs conservative. A further limitation of the study was the omission of the potential implications of interactions between the level of susceptibility, test properties and quit rates that may impact on the cost-effectiveness findings, introducing further uncertainty. Based on McBride's findings, 33% of the participants in the GT arm had a positive genetic test for the missing gene GSTM1 for elevated susceptibility to lung cancer. However, quit rates in these participants were similar to those with negative tests and therefore behavior change was not hindered by the GT results. This finding is supported by our own pilot work with further results on this issue forthcoming.
Lung cancer is the leading cause of cancer death in many developed countries and the prognosis is poor with a 1-year survival of 34% and 5-year survival of 12%. Although the risk of lung cancer is small in individuals with 'at risk' genotypes, lung cancer is a common cancer and therefore those with a genetic susceptibility affects a high absolute number of smokers. Further research on genetic susceptibility and molecular epidemiology in lung cancer alongside overall risk assessments  remains important work before public health approaches of screening, targeted smoking-cessation programs or other preventive measures are adopted. At the same time, commercial availability and consumer interest in genetic testing is increasing and may create added pressure for insurance companies or governments to subsidize their costs. To date, the evidence to support effective smoking-cessation by informing individuals of their own genetic risk of lung cancer is promising but weak[10, 12]. Genetic testing strategies rely on successful doctor-patient communication and must be ethical, results accurately conveyed and understood by patients.
In certain circumstances, specifically, if a smoking-cessation program delivering a genetic test, NRT and counselling produced a 12-month quit rate of at least 12.4% then it would represent a potentially sound health care investment for 50 year old heavy smokers. Overall, our findings showed that a genetic test option in addition to the use of NRT and counselling would produce very similar costs and effects than NRT and counselling alone. Further research on the quit rates at 12 months and beyond following a genetic testing strategy is required to strengthen our findings.
List of abbreviations
Chronic obstructive pulmonary disease
Incremental cost-effectiveness ratio
Nicotine replacement therapy
Quality adjusted life years
Single nucleotide polymorphisms
Usual smoking cessation
L Gordon is funded through a National Health and Medical Research Council Public Health Post-Doctoral Training Fellowship #496714. N Hirst is funded through a National Health and Medical Research Council Program Grant #552429.
- Scollo MM, Winstanley MH, (Eds): Tobacco in Australia: Facts and Issues. Third edition. Melbourne: Cancer Counci Victoria; 2008.Google Scholar
- World Health Organisation Regional Office for Europe: Prevalence of daily smoking by country, adults aged 15 years and over, European Region. WHO; 2005.Google Scholar
- Warner KE, Mackay JL: Smoking cessation treatment in a public-health context. Lancet 2008, 371: 1976–1978. 10.1016/S0140-6736(08)60846-6PubMedView ArticleGoogle Scholar
- Cromwell J, Bartosch WJ, Fiore MC, Baker T, Hasselblad V: Cost effectiveness of the AHCPR guidelines for smoking. Journal of the American Medical Association 1997, 278: 1759–1766. 10.1001/jama.278.21.1759PubMedView ArticleGoogle Scholar
- Gordon LG, Graves N, Hawkes A, Eakin E: A review of the cost-effectiveness of face-to-face behavioural interventions for smoking, physical activity, diet and alcohol. Chronic Illness 2007, 3: 101–129. 10.1177/1742395307081732PubMedView ArticleGoogle Scholar
- Shearer J, Shanahan M: Cost effectiveness analysis of smoking cessation interventions. Aust N Z J Public Health 2006, 30: 428–434. 10.1111/j.1467-842X.2006.tb00458.xPubMedView ArticleGoogle Scholar
- Young RP, Hopkins RJ, Hay BA, Epton MJ, Mills GD, Black PN, Gardner HD, Sullivan R, Gamble GD: Lung cancer susceptibility model based on age, family history and genetic variants. PLoS ONE 2009, 4: e5302. 10.1371/journal.pone.0005302PubMed CentralPubMedView ArticleGoogle Scholar
- Kiyohara C, Otsu A, Shirakawa T, Fukuda S, Hopkin JM: Genetic polymorphisms and lung cancer susceptibility: a review. Lung Cancer 2002, 37: 241–256. 10.1016/S0169-5002(02)00107-1PubMedView ArticleGoogle Scholar
- Alberg AJ, Ford JG, Samet JM: Epidemiology of lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). Chest 2007, 132: 29S-55S. 10.1378/chest.07-1347PubMedView ArticleGoogle Scholar
- Bize R, Burnand B, Mueller Y, Rege Walther M, Cornuz J: Biomedical risk assessment as an aid for smoking cessation. Cochrane Database Syst Rev 2009, 15: CD004705.Google Scholar
- Cameron LD, Sherman KA, Marteau TM, Brown PM: Impact of genetic risk information and type of disease on perceived risk, anticipated affect, and expected consequences of genetic tests. Health Psychol 2009, 28: 307–316. 10.1037/a0013947PubMedView ArticleGoogle Scholar
- McBride CM, Bepler G, Lipkus IM, Lyna P, Samsa G, Albright J, Datta S, Rimer BK: Incorporating genetic susceptibility feedback into a smoking cessation program for African-American smokers with low income. Cancer Epidemiol Biomarkers Prev 2002, 11: 521–528.PubMedGoogle Scholar
- Heitjan DF, Asch DA, Ray R, Rukstalis M, Patterson F, Lerman C: Cost-effectiveness of pharmacogenetic testing to tailor smoking-cessation treatment. Pharmacogenomics J 2008, 8: 391–399. 10.1038/sj.tpj.6500492PubMedView ArticleGoogle Scholar
- Welton NJ, Johnstone EC, David SP, Munafo MR: A cost-effectiveness analysis of genetic testing of the DRD2 Taq1A polymorphism to aid treatment choice for smoking cessation. Nicotine Tob Res 2008, 10: 231–240. 10.1080/14622200701767761PubMed CentralPubMedView ArticleGoogle Scholar
- Hughes JR, Peters EN, Naud S: Relapse to smoking after 1 year of abstinence: a meta-analysis. Addict Behav 2008, 33: 1516–1520. 10.1016/j.addbeh.2008.05.012PubMed CentralPubMedView ArticleGoogle Scholar
- Briggs A, Claxton K, Sculpher M: Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press; 2006.Google Scholar
- Stead LF, Perera R, Bullen C, Mant D, Lancaster T: Nicotine replacement therapy for smoking cessation. Cochrane Database Syst Rev 2008, 23: CD000146.Google Scholar
- Freedman ND, Leitzmann MF, Hollenbeck AR, Schatzkin A, Abnet CC: Cigarette smoking and subsequent risk of lung cancer in men and women: analysis of a prospective cohort study. Lancet Oncol 2008, 9: 649–656. 10.1016/S1470-2045(08)70154-2PubMed CentralPubMedView ArticleGoogle Scholar
- Audrain J, Boyd NR, Roth J, Main D, Caporaso NF, Lerman C: Genetic susceptibility testing in smoking-cessation treatment: one-year outcomes of a randomized trial. Addict Behav 1997, 22: 741–751. 10.1016/S0306-4603(97)00060-9PubMedView ArticleGoogle Scholar
- Carpenter MJ, Strange C, Jones Y, Dickson MR, Carter C, Moseley MA, Gilbert GE: Does genetic testing result in behavioral health change? Changes in smoking behavior following testing for alpha-1 antitrypsin deficiency. Ann Behav Med 2007, 33: 22–28. 10.1207/s15324796abm3301_3PubMedView ArticleGoogle Scholar
- Ito H, Matsuo K, Wakai K, Saito T, Kumimoto H, Okuma K, Tajima K, Hamajima N: An intervention study of smoking cessation with feedback on genetic cancer susceptibility in Japan. Prev Med 2006, 42: 102–108. 10.1016/j.ypmed.2005.10.006PubMedView ArticleGoogle Scholar
- Nafees B, Stafford M, Gavriel S, Bhalla S, Watkins J: Health state utilities for non small cell lung cancer. Health Qual Life Outcomes 2008, 6: 84. 10.1186/1477-7525-6-84PubMed CentralPubMedView ArticleGoogle Scholar
- Trippoli S, Vaiani M, Lucioni C, Messori A: Quality of life and utility in patients with non-small cell lung cancer. Quality-of-life Study Group of the Master 2 Project in Pharmacoeconomics. Pharmacoeconomics 2001, 19: 855–863. 10.2165/00019053-200119080-00007PubMedView ArticleGoogle Scholar
- Earle CC, Chapman RH, Baker CS, Bell CM, Stone PW, Sandberg EA, Neumann PJ: Systematic overview of cost-utility assessments in oncology. J Clin Oncol 2000, 18: 3302–3317.PubMedGoogle Scholar
- Cornuz J, Gilbert A, Pinget C, McDonald P, Slama K, Salto E, Paccaud F: Cost-effectiveness of pharmacotherapies for nicotine dependence in primary care settings: a multinational comparison. Tob Control 2006, 15: 152–159. 10.1136/tc.2005.011551PubMed CentralPubMedView ArticleGoogle Scholar
- Hoogendoorn M, Welsing P, Rutten-van Molken MP: Cost-effectiveness of varenicline compared with bupropion, NRT, and nortriptyline for smoking cessation in the Netherlands. Curr Med Res Opin 2008, 24: 51–61.PubMedView ArticleGoogle Scholar
- George B, Harris A, Mitchell A: Cost-effectiveness analysis and the consistency of decision making: evidence from pharmaceutical reimbursement in australia (1991 to 1996). Pharmacoeconomics 2001, 19: 1103–1109. 10.2165/00019053-200119110-00004PubMedView ArticleGoogle Scholar
- Jackson KC, Nahoopii R, Said Q, Dirani R, Brixner D: An employer-based cost-benefit analysis of a novel pharmacotherapy agent for smoking cessation. J Occup Environ Med 2nd edition. 2007, 49: 453–460. 10.1097/JOM.0b013e3180459ff2PubMedView ArticleGoogle Scholar
- Thavorn K, Chaiyakunapruk N: A cost-effectiveness analysis of a community pharmacist-based smoking cessation programme in Thailand. Tob Control 2008, 17: 177–182. 10.1136/tc.2007.022368PubMedView ArticleGoogle Scholar
- Etter JF, Stapleton JA: Nicotine replacement therapy for long-term smoking cessation: a meta-analysis. Tob Control 2006, 15: 280–285. 10.1136/tc.2005.015487PubMed CentralPubMedView ArticleGoogle Scholar
- Hoogenveen RT, van Baal PH, Boshuizen HC, Feenstra TL: Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation. Cost Eff Resour Alloc 2008, 6: 1. 10.1186/1478-7547-6-1PubMed CentralPubMedView ArticleGoogle Scholar
- Australian Institute of Health and Welfare (AIHW): ACIM (Australian Cancer Incidence and Mortality) Books. Canberra: Australian Institute of Health and Welfare; 2009.Google Scholar
- Ries LAG, Melbert D, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, Mariotto A, Miller BA, Feuer EJ, Altekruse SF, et al.: SEER Cancer Statistics Review, 1975–2005. Bethesda: National Cancer Institute; 2008.Google Scholar
- Mahadevia PJ, Fleisher LA, Frick KD, Eng J, Goodman SN, Powe NR: Lung cancer screening with helical computed tomography in older adult smokers: a decision and cost-effectiveness analysis. Jama 2003, 289: 313–322. 10.1001/jama.289.3.313PubMedView ArticleGoogle Scholar
- Department of Health & Ageing: National Hospital Cost Data Collection, Cost weights for AR-DRG v.5.1 (Round 11, 2006–07). Canberra: Commonwealth of Australia; 2008.Google Scholar
- Manser R, Dalton A, Carter R, Byrnes G, Elwood M, Campbell DA: Cost-effectiveness analysis of screening for lung cancer with low dose spiral CT (computed tomography) in the Australian setting. Lung Cancer 2005, 48: 171–185. 10.1016/j.lungcan.2004.11.001PubMedView ArticleGoogle Scholar
- Hurley SF, Matthews JP: The Quit Benefits Model: a Markov model for assessing the health benefits and health care cost savings of quitting smoking. Cost Eff Resour Alloc 2007, 5: 2. 10.1186/1478-7547-5-2PubMed CentralPubMedView ArticleGoogle Scholar
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