Prioriser les facteurs de risque pour identifier les interventions préventives à évaluer économiquement
Determinación de la prioridad de los factores de riesgo con el fin de identificar las intervenciones preventivas para la evaluación económica
Nick WilsonI,*; Tony BlakelyI; Rachel H FosterI; David HadornI; Theo VosII
IDepartment of Public Health, University of Otago, Wellington, PO Box 7343, Wellington South, New Zealand
IISchool of Population Health, University of Queensland, Herston, Australia
OBJECTIVE: To explore a risk factor approach for identifying preventive interventions that require more in-depth economic assessment, including cost-effectiveness analyses.
METHODS: A three-step approach was employed to: (i) identify the risk factors that contribute most substantially to disability-adjusted life years (DALYs); (ii) re-rank these risk factors based on the availability of effective preventive interventions warranting further cost-effectiveness analysis (and in some instances on evidence from existing cost-effectiveness analyses); and (iii) re-rank these risk factors in accordance with their relative contribution to health inequalities. Health inequalities between the Māori and non-Māori populations in New Zealand were used by way of illustration.
FINDINGS: Seven of the top 10 risk factors prioritized for research on preventive interventions in New Zealand were also among the 10 risk factors most highly ranked as contributing to DALYs in high-income countries of the World Health Organization's Western Pacific Region. The final list of priority risk factors included tobacco use; alcohol use; high blood pressure; high blood cholesterol; overweight/obesity, and physical inactivity. All of these factors contributed to health inequalities. Effective interventions for preventing all of them are available, and for each risk factor there is at least one documented cost-saving preventive intervention.
CONCLUSION: The straightforward approach to prioritizing risk factors described in this paper may be applicable in many countries, and even in those countries that lack the capacity to perform additional cost-effectiveness analyses, this approach will still make it possible to determine which cost-effective interventions should be implemented in the short run.
OBJECTIF: Étudier une approche fondée sur les facteurs de risque pour identifier les interventions préventives nécessitant une évaluation économique plus approfondie, y compris des analyses de rentabilité.
MÉTHODES: Une approche en trois étapes a été utilisée pour (i) identifier les facteurs de risque contribuant en grande partie aux années de vie corrigées du facteur invalidité (AVCI), (ii) reclasser ces facteurs de risque en fonction de la disponibilité des interventions préventives efficaces justifiant une analyse supplémentaire de rentabilité (et, dans certains cas, sur la base de preuves provenant d'analyses de rentabilité existantes), et (iii) reclasser ces facteurs de risque en fonction de leur contribution relative aux inégalités de santé. Les inégalités de santé entre les populations maories et non maories en Nouvelle-Zélande ont été utilisées à titre d'illustration.
RÉSULTATS: Sept des dix principaux facteurs de risque priorisés pour la recherche sur les interventions préventives en Nouvelle-Zélande figuraient également parmi les dix facteurs de risque contribuant le plus aux AVCI dans les pays à revenu élevé de la région du Pacifique occidental de l'Organisation mondiale de la Santé. La liste définitive des facteurs de risque prioritaires incluait le tabagisme, la consommation d'alcool, l'hypertension artérielle, l'hypercholestérolémie, le surpoids/l'obésité et l'inactivité physique. Tous ces facteurs contribuaient à des inégalités de santé. Des interventions efficaces pour prévenir chacun d'eux sont disponibles, et il existe au moins une intervention préventive plus économique connue pour chaque facteur de risque.
CONCLUSION: L'approche simple de priorisation des facteurs de risque décrite dans cet article peut être applicable dans de nombreux pays. Même dans les pays qui n'ont pas la capacité d'effectuer des analyses de rentabilité complémentaires, cette approche permettra de déterminer quelles interventions rentables devraient être mises en œuvre à court terme.
OBJETIVO: Explorar una estrategia en relación con los factores de riesgo que permita la identificación de las intervenciones preventivas que requieran una evaluación económica de mayor profundidad, incluyendo los análisis de rentabilidad.
MÉTODOS: Se aplicó una estrategia en tres etapas con el fin de (i) identificar los factores de riesgo que contribuyen de manera más significativa a los años de vida ajustados en función de la discapacidad (AVAD); (ii) volver a clasificar estos factores de riesgo en base a la disponibilidad de intervenciones preventivas eficaces que justifiquen la realización de análisis de rentabilidad adicionales (y, en algunos casos, en base a las evidencias procedentes de los análisis de rentabilidad existentes); y (iii) volver a clasificar estos factores de riesgo de acuerdo con su contribución relativa a las desigualdades sanitarias. A modo de ilustración, se analizaron las desigualdades sanitarias entre las poblaciones maoríes y no maoríes de Nueva Zelanda.
RESULTADOS: Siete de los 10 factores de riesgo prioritarios para la investigación sobre las intervenciones preventivas en Nueva Zelanda también se encontraron entre los 10 factores de riesgo con mayor calificación en cuanto a su contribución a los AVAD en países de ingresos altos de la Región del Pacífico Occidental de la Organización Mundial de la Salud. En la lista definitiva de los factores de riesgo prioritarios se incluyeron el tabaquismo, el consumo de alcohol, la presión arterial alta, un nivel elevado de colesterol, el sobrepeso/obesidad y el sedentarismo. Todos estos factores contribuyeron a las desigualdades sanitarias. Se han desarrollado intervenciones eficaces para prevenir todos estos factores, y para cada factor de riesgo se ha documentado al menos una intervención preventiva destinada al ahorro de costes.
CONCLUSIÓN: La sencilla estrategia consistente en establecer la prioridad de los factores de riesgo descritos en este documento puede ser aplicable en muchos países, e incluso en aquellos países que carezcan de la capacidad necesaria para llevar a cabo los análisis de rentabilidad adicionales, esta estrategia también permitirá determinar qué intervenciones rentables se han de aplicar a corto plazo.
Most countries seek to improve the health of their populations while reducing health inequalities. They must therefore deploy their health sector resources, which are often scarce, in a manner that maximizes both goals. Preventive interventions are often highly cost-effective1 and they sometimes promote equity.2,3 Recent work in Australia has demonstrated that many preventive interventions are cost-effective, and quite a few are actually cost-saving over the long-term. The Australian Assessing Cost-Effectiveness of Prevention (ACE-Prevention) Project reported 23 preventive interventions as being cost-saving or "dominant"; 20 as being "very cost-effective" and 31 as being simply "cost-effective" (i.e. within the range of 10000 to 50 000 Australian dollars [i.e. 9895 to 49 465 United States dollars (US$)] per disability-adjusted life year [DALY] averted).1
In light of the above and based on the fact that many preventive interventions work through risk factor modification, we sought to develop a systematic approach for identifying those interventions that should be prioritized for more extensive cost-effectiveness analysis (CEA) based on risk factor prioritization. We implicitly sought to identify not only risk factors for which (highly) cost-effective interventions are feasible, but also risk factors that contribute substantially to the burden of disease and whose reduction through effective interventions is therefore more likely to contribute to substantial improvements in health.
We propose a three-step approach: (i) identifying the top priority risk factors, namely those that contribute the greatest number of DALYs; (ii) re-ranking these risk factors based on evidence of the availability of effective interventions that warrant CEA (and, in some instances, on evidence stemming from existing CEAs); and (iii) a final re-ranking based on the extent to which these risk factors contribute to health inequalities. By way of illustration we use health inequalities between Māori and non-Māori population groups in New Zealand.
Disease burden contributed by risk factors
Comparative risk assessment methods make it possible to compare to what extent different risk factors contribute to the disease burden. Briefly, a burden of disease study is performed to quantify the DALYs contributed by all selected disease conditions. The DALY is a composite of years of life lost due to a particular disease or disability and a morbidity component represented by the number of years lived in a state of disability (e.g. if living with stroke has a disability weight of 0.4 and the average number of years lived with stroke is 10, this amounts to a loss of 4 years of life).
With this information in hand, one can then calculate the disease burden attributable to specific risk factors. For example, in a comparative risk assessment of the burden of disease attributable to tobacco, all diseases that are caused by tobacco smoking are identified, the relative risks for the association between smoking and each disease are assembled, and the population distribution of smoking is determined from surveys. One then posits a counterfactual (but theoretically feasible) distribution for each risk factor. Such a counterfactual distribution would be nil in the case of a dichotomous variable such as smoking, but for a continuous variable such as blood pressure, the counterfactual would be a shifted and compressed distribution with a low mean that is associated with a minimal risk of disease. These data are then combined using population-attributable risk analyses to calculate the percentage of DALYs that a given factor contributes to a particular condition, for instance, the percentage of ischaemic heart disease DALYs contributed by smoking. Finally, one compares the number of DALYs attributable to various risk factors and ranks these factors accordingly.
In previous work in New Zealand, comparative risk assessment methods were used to identify and rank major risk factors for the year 1996,4 but rankings were based on numbers of deaths rather than DALYs. Furthermore, such work is now somewhat outdated, as more recent meta-analyses and syntheses of relative risk data have become available. We therefore used the global burden of disease data published more recently by the World Health Organization (WHO) for high-income countries in the Western Pacific Region: Australia, Brunei Darussalam, Japan, New Zealand, the Republic of Korea and Singapore.5
Risk factor selection criteria
As a starting point we decided that all the risk factors to be initially considered had to be among the top 15 causes of DALYs in the six aforementioned highincome countries.5 That is, we were interested not just in cost-effectiveness, but also in the effect of interventions on population health overall. We then assessed and re-ranked the selected risk factors in terms of: (i) current or predictable future availability of effective preventive interventions targeting the risk factor (and, in some instances, with evidence of cost-effectiveness as well); and (ii) the extent to which the factor contributes to health inequalities. For the first ranking we required the availability of at least one preventive intervention addressing the risk factor for which evidence of cost-effectiveness also existed. For our example of health inequalities drawn from New Zealand, we required that the risk factor contribute substantially to inequalities between the Māori (indigenous) and non-Māori populations (including European, Pacific peoples and Asian people), on the premise that any intervention addressing the risk factor would also reduce these inequalities. Although inequalities exist among other ethnic groups and different socioeconomic strata in New Zealand, the gap between the Māori and non-Māori populations is especially large and of particular concern to health sector policy-makers. In this paper we focus on inequalities between the Māori and non-Māori populations to illustrate how health inequalities can be incorporated into the method we have developed for prioritizing risk factors for future research on the costeffectiveness of preventive interventions.
To inform the above process we searched Medline and Google Scholar to identify articles on interventions targeting all 15 initially selected risk factors. We also searched for reports on the New Zealand Ministry of Health web site (www.moh. govt.nz). These searches also served to identify the role played by each risk factor in the health inequalities between the Māori and non-Māori people (e.g. by comparing the hospitalization rates, the mortality rates or other disease burden estimates from epidemiological studies).
The 15 risk factors initially considered from the work of the WHO on burden of disease at the regional level showed good overlap with the risk factors previously prioritized for study in New Zealand (Table 1). Of the 10 risk factors identified by WHO as being the leading contributors to lost DALYs, seven had been previously identified by the New Zealand Ministry of Health as being among the 10 most important risk factors for death in the country (i.e. albeit using a different metric from DALYs).
The last five risk factors listed in Table 1 are not likely to rank higher than the factors appearing higher on the list in terms of the potential benefits of interventions to prevent them. This is because these factors were also ranked lower in the previous burden of disease study conducted in New Zealand, and the difference between them and the top seven in terms of their contribution to DALYs is too large to be plausibly attributable to error. In light of this, we focused on the top 10 risk factors.
The preventive interventions targeting each risk factor are listed in Table 2. Cost-effective preventive interventions (some of which have also been reported as cost-saving)1 were identified for each factor. We dropped the "occupational risk" category from further consideration because it involved a multitude of interventions specific to certain occupational settings.
It became apparent that most (8/9) of the risk factors in our revised list contributed to health inequalities between Māori and non-Māori people (Table 3). Data on DALYs were not available from previous New Zealand studies, but data on each risk factor's contribution to years-of-life-lost (YLL) in the Māori population were available for six out of nine risk factors (albeit from 1996-1997 and hence somewhat outdated). So we used these data to rank "high blood cholesterol" above "physical inactivity" in our final revisions to the ranking (Table 4). We excluded air pollution from further investigation because of lack of clarity around the importance of environmental interventions for reducing health inequalities between Māori and non-Māori populations.
Table 4 shows additional factors that we considered in the final priority ranking of the eight selected risk factors. Certain areas were assigned lower priority particularly because the strength of the evidence around intervention effectiveness and cost-effectiveness was uncertain.
Inequalities other than those between Māori and non-Māori populations should ideally be included in the risk factor priority-setting process, although not explicitly considered part of it in this paper. The six risk factors we identified as having the highest priority (Table 4) are also relevant for reducing health inequalities affecting the Pacific peoples of New Zealand, and disease burden by age group (i.e. in children or youth and older adults).32 Furthermore, four of the six risk factors are relevant to reducing health inequalities affecting socioeconomically deprived New Zealanders, given that this population group has more adverse risk factor profiles in terms of smoking, alcohol abuse, physical inactivity and overweight or obesity.32
Interpretation of major findings
Our study results, based on WHO regional data on DALYs by risk factor for the New Zealand setting, were fairly consistent with the findings of past work on risk factor prioritization in this country.4
The evidence from cost-effectiveness analyses in the literature resulted in changes in the priority ranking of some of the risk factors, but not the top three (tobacco, alcohol use and high blood pressure). From the perspective of health inequalities between the Māori and non-Māori populations, this process produced only modest changes in the rankings, since eight of the nine risk factors being considered in the revised list were found to contribute to inequality. This does not obviate the importance of explicitly considering inequalities in the prioritization process. Rather, it suggests that in New Zealand important health inequalities are present in the overall burden of disease, as measured by DALYs. Our final six top priority risk factors for New Zealand were all among the top seven contributors to DALYs as identified by WHO (Table 1).
Can the methods described in this paper be successfully applied in other countries that need to prioritize risk factors for identifying preventive interventions meriting CEA? We believe that they probably can, but this is a research question in its own right. Since global burden of disease data are available at the country level for all WHO regions and are routinely reported by country income level, there should be a reasonable choice of relevant DALY and comparative risk factor assessment data to draw upon for most countries. Also, the global burden of disease study currently in progress will draw on many additional systematic reviews and will further update burden of disease data. Admittedly, however, the quality of the data pertaining to health inequalities varies enormously across countries and will be largely country-specific.
We focused mainly on identifying those risk factors on which to concentrate for more in-depth CEA of interventions. However, a lack of capacity to undertake country-specific CEA or lack of political will may prevent CEAs from being undertaken in some countries. Nonetheless, we still believe in the usefulness of our approach, since the priority-setting exercise itself and the supporting evidence obtained through literature searches will identify those interventions that are highly likely to be cost-effective in a given country. In fact, Beaglehole et al. have essentially used this approach to recommend five priority actions - tobacco control, salt reduction, improved diets and physical activity, reduction in hazardous alcohol intake and development of essential drugs and technologies - to substantially reduce the global burden of non-communicable diseases in a cost-effective manner.44
Strengths and limitations
One strength of our approach is its strong reliance on the DALY metric, which captures both morbidity and mortality. The use of WHO regional data has also allowed us to improve on the more limited and somewhat outdated work on disease burden formerly conducted in New Zealand and to develop an approach that other countries can potentially use in the absence of their own national burden of disease studies. Our approach is also relatively simple and hence more likely to be transparent and acceptable to policy-makers in the health sector.
However, the approach has important limitations. The WHO data on which it relies applies to the regional rather than the country level. Thus, high blood cholesterol is probably a more important risk factor in New Zealand than in other high-income countries in the Western Pacific Region because New Zealanders consume relatively large quantities of dairy products and meat.45 Conversely, the urban air pollution is likely to be a less important risk factor than average in New Zealand, where population density and industrialization are low and where the winds are relatively strong. Furthermore, WHO data on risk factors fail to capture the potential contribution of potential upstream determinants (such as poor education, lack of employment or low socioeconomic status), to risk factors such as smoking or alcohol misuse.
Our risk factor prioritization process is further limited by the fact that only one aspect of health inequality in New Zealand was examined, namely, health inequalities between Māori and non-Māori populations. However, the distribution of risk factors in this country is such that a focus on the six top risk factors will undoubtedly benefit Pacific peoples in New Zealand as well as children and youth, older adults and socioeconomically deprived New Zealanders. Furthermore, our analysis did not take into account the potential non-health benefits of preventive interventions, which could enhance their cost-effectiveness from a societal perspective. For example, interventions targeting tobacco and alcohol use could reduce absenteeism and premature death in the workforce, and interventions targeting physical inactivity, such as walking and cycling as commuting options and reduced use of private vehicles, could reduce greenhouse gas emissions.19 Similarly, dietary interventions to reduce dairy product and meat consumption (to reduce blood cholesterol levels) would lower greenhouse gas emissions from ruminant-based agriculture (especially the greenhouse gas methane).20
Our approach has tended to prioritize those risk factors having a relatively high impact on health and equity. However, other criteria should also be considered in the final process of selecting risk factors and the interventions designed to prevent them. We offer two examples. First, initial scoping of an intervention that is being actively considered for immediate implementation by policy-makers for social or political reasons may suggest that its impact and/or cost-effectiveness will be low. If so, the intervention should be prioritized for a CEA before implementation is decided. Second, policy-makers should ideally get a better sense of the tradeoffs through access to information on a wide range of interventions ranked by cost-effectiveness and by their impact on DALYs.
Implications for further work
The process of prioritizing risk factors to select preventive interventions for further CEA should include consultation with stakeholders. We have already started consulting with representatives of major health agencies, local health authorities, the primary care sector and experts in Māori health in New Zealand. We also plan to apply criteria other than the ones described herein for selecting preventive interventions for CEA, as detailed by ACE-Prevention workers in Australia.17
Additional research on public attitudes surrounding the importance of risk factors and their preventive interventions should be conducted. There is already evidence that New Zealanders support enhanced tobacco control interventions,46 yet support can be fairly nuanced. For instance, most smokers in New Zealand support raising tobacco taxes only if the tax revenue is destined specifically to providing support for smokers wishing to quit and for health promotion.47
In countries where important health inequalities exist, such as New Zealand, preventive interventions should also be assessed in terms of their ability to reduce health inequalities in a cost-effective way, since there may be trade-offs between achieving overall gains in population health and gains among specific population groups more heavily affected by certain risk factors. The following are possible ways of incorporating inequalities in health between the general population and ethnic minorities when conducting CEA of preventive interventions:
Presenting the health gains attributable to an intervention by population groups, including ethnic minorities (i.e. DALYs averted [per capita] and cost per DALY averted).
Presenting the additional resources and intervention coverage required to reduce by a given amount the gap in the number of DALYs between population groups.
Weighting of the benefits of the intervention in terms of equity by using methods such as the rank-dependent quality-adjusted life year model, which assigns more weight to the health gains attained among those that are worst off.48
Although some trade-offs in the benefits afforded by a few preventive interventions are likely to occur, some interventions will result in benefits for all. For instance, higher tobacco prices tend to protect the health of all citizens as well as to reduce health inequalities.3
In summary, our risk factor approach to identifying preventive interventions for further CEA may seem somewhat simplistic. However, it is relatively straightforward and transparent and can be applied in both developed and developing countries. We encourage further research on the use of this approach internationally.
We thank NZACE-Prevention and BODE3 team colleagues, particularly Diana Sarfati, for their comments during early versions of this work.
Funding: This project receives funding support from the Health Research Council of New Zealand (Project number 10/248).
Competing interests: None declared.
1. Vos T, Carter R, Barendregt J, Mihalopoulos C, Veerman J, Magnus A, et al. Assessing Cost-Effectiveness in Prevention (ACE-Prevention): final report. Brisbane & Melbourne: University of Queensland & Deakin University; 2010. Available from: www.sph.uq.edu.au/bodce-ace-prevention [accessed 1 October 2011]
2. Capewell S, Graham H. Will cardiovascular disease prevention widen health inequalities? PLoS Med 2010;7:e1000320. doi:10.1371/journal.pmed.1000320 PMID:20811492
3. Main C, Thomas S, Ogilvie D, Stirk L, Petticrew M, Whitehead M et al. Population tobacco control interventions and their effects on social inequalities in smoking: placing an equity lens on existing systematic reviews. BMC Public Health 2008;8:178. doi:10.1186/1471-2458-8-178 PMID:18505545
4. Looking upstream: causes of death cross-classified by risk and condition, New Zealand 1997. Wellington: Ministry of Health; 2004.
5. Global health risks: mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization; 2009.
6. Kahende JW, Loomis BR, Adhikari B, Marshall L. A review of economic evaluations of tobacco control programs. Int J Environ Res Public Health 2009;6:51-68. doi:10.3390/ijerph6010051 PMID:19440269
7. Carter R, Vos T, Moodie M, Haby M, Magnus A, Mihalopoulos C. Priority setting in health: origins, description and application of the Australian Assessing Cost-Effectiveness Initiative. Expert Rev Pharmacoecon Outcomes Res 2008;8:593-617. doi:10.1586/14737188.8.131.523 PMID:20528370
8. Cobiac L, Vos T, Doran C, Wallace A. Cost-effectiveness of interventions to prevent alcohol-related disease and injury in Australia. Addiction 2009;104:1646-55. doi:10.1111/j.1360-0443.2009.02708.x PMID:21265906
9. Chisholm D, Rehm J, Van Ommeren M, Monteiro M. Reducing the global burden of hazardous alcohol use: a comparative cost-effectiveness analysis. J Stud Alcohol 2004;65:782-93. PMID:15700517
10. Anderson P, Chisholm D, Fuhr DC. Effectiveness and cost-effectiveness of policies and programmes to reduce the harm caused by alcohol. Lancet 2009;373:2234-46. doi:10.1016/S0140-6736(09)60744-3 PMID:19560605
11. Evidence for the effectiveness and cost-effectiveness of interventions to reduce alcohol-related harm. Copenhagen: Regional Office for Europe of the World Health Organization: 2009. Available from: http://www.euro.who.int/document/E92823.pdf [accessed 1October 2011]
12. Asaria P, Chisholm D, Mathers C, Ezzati M, Beaglehole R. Chronic disease prevention: health effects and financial costs of strategies to reduce salt intake and control tobacco use. Lancet 2007;370:2044-53. doi:10.1016/S0140-6736(07)61698-5 PMID:18063027
13. Bibbins-Domingo K, Chertow G, Coxson P, Moran A, Lightwood J, Pletcher M et al. Projected effect of dietary salt reductions on future cardiovascular disease. N Engl J Med 2010;362:590-9. doi:10.1056/NEJMoa0907355 PMID:20089957
14. Joffres MR, Campbell NR, Manns B, Tu K. Estimate of the benefits of a population-based reduction in dietary sodium additives on hypertension and its related health care costs in Canada. Can J Cardiol 2007;23:437-43. doi:10.1016/S0828-282X(07)70780-8 PMID:17487286
15. Cobiac LJ, Vos T, Veerman JL. Cost-effectiveness of interventions to reduce dietary salt intake. Heart 2010;96:1920-5. doi:10.1136/hrt.2010.199240 PMID:21041840
16. PILL Collaborative Group. An international randomised placebo-controlled trial of a four-component combination pill ("polypill") in people with raised cardiovascular risk. PLoS ONE 2011;6:e19857. doi:10.1371/journal.pone.0019857 PMID:21647425
17. Carter R, Moodie M, Markwick A, Magnus A, Vos T, Swinburn B et al. Assessing cost-effectiveness in obesity (ACE-obesity): an overview of the ACE approach, economic methods and cost results. BMC Public Health 2009;9:419. doi:10.1186/1471-2458-9-419 PMID:19922625
18. Cobiac LJ, Vos T, Barendregt JJ. Cost-effectiveness of interventions to promote physical activity: a modelling study. PLoS Med 2009;6:e1000110. doi:10.1371/journal.pmed.1000110 PMID:19597537
19. Woodcock J, Edwards P, Tonne C, Armstrong BG, Ashiru O, Banister D et al. Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport. Lancet 2009;374:1930-43. doi:10.1016/S01406736(09)61714-1 PMID:19942277
20. Friel S, Dangour AD, Garnett T, Lock K, Chalabi Z, Roberts I et al. Public health benefits of strategies to reduce greenhouse-gas emissions: food and agriculture. Lancet 2009;374:2016-25. doi:10.1016/S0140-6736(09)61753-0 PMID:19942280
21. Shih ST, Carter R, Sinclair C, Mihalopoulos C, Vos T. Economic evaluation of skin cancer prevention in Australia. Prev Med 2009;49:449-53. doi:10.1016/j.ypmed.2009.09.008 PMID:19747936
22. Ni Mhurchu C, Blakely T, Jiang Y, Eyles HC, Rodgers A. Effects of price discounts and tailored nutrition education on supermarket purchases: a randomized controlled trial. Am J Clin Nutr 2010;91:736-47. doi:10.3945/ajcn.2009.28742 PMID:20042528
23. Chestnut LG, Mills DM. A fresh look at the benefits and costs of the US acid rain program. J Environ Manage 2005;77:252-66. doi:10.1016/j.jenvman.2005.05.014 PMID:16171931
24. Ellerman A, Convery F, de Perthuis C, Alberola E, Buchner B, Delbosc A, et al. Pricing carbon: the European Union Emissions Trading Scheme. New York: Cambridge University Press; 2010.
25. Tobacco trends 2008: a brief update of tobacco use in New Zealand. Wellington: Ministry of Health, 2009. Available from: http://www.moh.govt.nz/moh.nsf/pagesmh/9081/$File/tobacco-trends-2008.pdf [accessed 1 October 2011]
26. Blakely T, Fawcett J, Hunt D, Wilson N. What is the contribution of smoking and socioeconomic position to ethnic inequalities in mortality in New Zealand? Lancet 2006;368:44-52. doi:10.1016/S0140-6736(06)68813-2 PMID:16815379
27. Carter KN, Blakely T, Soeberg M. Trends in survival and life expectancy by ethnicity, income and smoking in New Zealand: 1980s to 2000s. N Z Med J 2010;123:13-24. PMID:20720599
28. Blakely T, Carter K, Wilson N, Edwards R, Woodward A, Thomson G et al. If nobody smoked tobacco in New Zealand from 2020 onwards, what effect would this have on ethnic inequalities in life expectancy? N Z Med J 2010;123:26-36. PMID:20720600
29. Mortality: 2005 (final) and 2006-2008 (provisional). Wellington: Ministry of Health; 2010. Available from: http://www.moh.govt.nz/moh.nsf/Files/mortdemo/$file/mort05to08-mar09.pdf [accessed 1 October 2011]
30. Blakely T, Tobias M, Atkinson J, Yeh L-C, Huang K. Tracking disparity: trends in ethnic and socioeconomic inequalities in mortality, 1981-2004. Wellington: Ministry of Health; 2007.
31. Tobias M, Blakely T, Matheson D, Rasanathan K, Atkinson J. Changing trends in indigenous inequalities in mortality: lessons from New Zealand. Int J Epidemiol 2009;38:1711-22. doi:10.1093/ije/dyp156 PMID:19332501
32. A portrait of health: key results of the 2006-07 New Zealand Health Survey. Wellington: Ministry of Health; 2008. Available from: http://www.moh.govt.nz/moh.nsf/indexmh/portrait-of-health [accessed 1October 2011]
33. Metcalf PA, Scragg RR, Schaaf D, Dyall L, Black PN, Jackson R. Dietary intakes of European, Maori, Pacific and Asian adults living in Auckland: the Diabetes, Heart and Health Study. Aust N Z J Public Health 2008;32:454-60. doi:10.1111/j.1753-6405.2008.00279.x PMID:18959550
34. Sargent M, Begg D, Broughton J, Stephenson S, Wright C, Baxter J. Motor vehicle traffic crashes involving Maori. N Z Med J 2004;117:U746. PMID:14999305
35. Tobacco smoke and involuntary smoking, vol. 83. Lyon: International Agency for Research on Cancer; 2004. Available from: http://monographs.iarc.fr/ENG/Monographs/vol83/index.php [accessed 1 October 2011]
36. Kahler CW, Borland R, Hyland A, McKee SA, Thompson ME, Cummings KM. Alcohol consumption and quitting smoking in the International Tobacco Control (ITC) Four Country Survey. Drug Alcohol Depend 2009;100:214-20. doi:10.1016/j.drugalcdep.2008.10.006 PMID:19056188
37. Lawes C, Stefanogiannis N, Tobias M, Paki Paki N, Ni Mhurchu C, Turley M et al. Ethnic disparities in nutrition-related mortality in New Zealand: 1997-2011. N Z Med J 2006;119:U2122. PMID:16924273
38. Carter P, Gray LJ, Troughton J, Khunti K, Davies MJ. Fruit and vegetable intake and incidence of type 2 diabetes mellitus: systematic review and meta-analysis. BMJ 2010;341:c4229. doi:10.1136/bmj.c4229 PMID:20724400
39. Hales S, Blakely T, Woodward A. Air pollution and mortality in New Zealand: cohort study. J Epidemiol Community Health 2010. E-Pub 21 October
40. Our health, our future: the health of New Zealanders 1999. Wellington: Ministry of Health; 1999.
41. Rehm J, Mathers C, Popova S, Thavorncharoensap M. Teerawattananon Y, Patra J. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 2009;373:2223-33. doi:10.1016/S0140-6736(09)60746-7 PMID:19560604
42. Boffetta P, Couto E, Wichmann J, Ferrari P, Trichopoulos D, Bueno-de-Mesquita HB et al. Fruit and vegetable intake and overall cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst 2010;102:529-37. doi:10.1093/jnci/djq072 PMID:20371762
43. Carter P, Gray LJ, Troughton J, Khunti K, Davies MJ. Fruit and vegetable intake and incidence of type 2 diabetes mellitus: systematic review and meta-analysis. BMJ 2010;341:c4229. doi:10.1136/bmj.c4229 PMID:20724400
44. Beaglehole R, Bonita R, Horton R, Adams C, Alleyne G, Asaria P et al. Priority actions for the non-communicable disease crisis. Lancet 2011;377:1438-47. doi:10.1016/S0140-6736(11)60393-0 PMID:21474174
45. Laugesen M, Swinburn B. The New Zealand food supply and diet - trends 1961-95 and comparison with other OECD countries. N Z Med J 2000;113:311-5. PMID:10972311
46. Edwards R, Wilson N, Thomson G, Weerasekera D, Blakely T. Majority support by Māori and non-Māori smokers for many aspects of increased tobacco control regulation: national survey data. N Z Med J 2009;122:115-8. PMID:20148054
47. Wilson N, Weerasekera D, Edwards R, Thomson G, Devlin M, Gifford H. Characteristics of smoker support for increasing a dedicated tobacco tax: national survey data from New Zealand. Nicotine Tob Res 2010;12:168-73. doi:10.1093/ntr/ntp178 PMID:20018943
48. Bleichrodt H, Diecidue E, Quiggin J. Equity weights in the allocation of health care: the rank-dependent QALY model. J Health Econ 2004;23:157-71. doi:10.1016/j.jhealeco.2003.08.002 PMID:15154692
Submitted: 1 June 2011
Revised version received: 21 September 2011
Accepted: 26 September 2011
Published online: 14 October 2011