Data for planning equitable and cost effective health services: An approach from NZ

1. Data for planning equitable and cost effective health services: An approach from NZ Burden of Disease Epidemiology, Equity & Cost-Effectiveness Programme (BODE3) Directors: Tony Blakely, Nick Wilson, Diana Sarfati Named Investigators: Hadorn, O’Dea, Tobias, McLeod, Costilla, Soeberg, Atkinson, Simpson, Vos, Barendregt, Cobiac, Foster, Richardson, Sloane, Kvizhinadze, Nghiem, Collinson tony.blakley@otago.ac.nz

2. What I think I was meant to talk about NZ Census-Mortality Study (NZCMS) and CancerTrends • Nearly 25 years of mortality & cancer data linked to censuses • Three examples of findings: • Large undercounting of Māori and Pacific deaths and cancers in 1980s/90s, causing 20% to 35% underestimates of rates… • … which when corrected for disclosed opening ethnic gaps in life expectancy in the 1980s and 1990s [a time of structural reforms] • Varying trends in cancer incidence over time, e.g. cervical cancer – major public health success story!

3. What is the next problem? Policy making without synthesising evidence • The 100 manila folder problem • “Please sit on this committee, and advise us what to do next.”

4. What are the opportunities? Leverage existing data and methods • Unique ID linked health data • Census-mortality and census-cancer linked data • ACE methodology from Australia • Burden of disease studies – comparable disease envelope and parameters • Increasing computer power • Data-banks of systematic reviews and meta-analyses

5. What is the idea? Build infrastructure for rapid cost effectiveness analysis • Rather than respond to need for cost effectiveness analyses one-by-one…. • … first, build the data and modelling infrastructure that can respond more rapidly and with greater comparability between interventions to (just about) anything you ask • Capitalise on New Zealand’s rich data by ethnicity and socioeconomic position for equity analyses • Build capacity

6. Focus on economic decision models Which is just one input into the decision making process • Cost, effect (population change in health) and cost effectiveness • Equity • Strength of the evidence base. • Acceptability to stakeholders, especially public • Feasibility of implementation • Sustainability (Budget, workforce, political, other) • Other consequences (side effects, spin-offs) • Politics • Social values • Rule of rescue

7. Vision of BODE3 HRC-funded programme 2010-15; Ministry collaboration • “To build capacity and academic rigour in New Zealand in the estimation of • disease burden, cost-effectiveness and equity impacts of proposed interventions, • and undertake a range of such assessments.” Burden of Disease Epidemiology, Equity & Cost-Effectiveness Programme (BODE3) uow.otago.ac.nz/BODE3-info.html

8. Yes, we will mainly use DALYs… …. but in cost-effectiveness little different from QALYs • There are two types of DALYs: • For burden of disease studies where ‘external’ model lifetable used [but no age weights a là 1990s GBD studies] • For economic evaluations, where the population’s own lifetable is used to determine background mortality rates • Can talk in terms of ‘DALYs averted’, or ‘HALYs gained’ • Thus the only conceptual difference is the use of disability weights vis à vis utilities

9. Presentation Structure of presentation to you today • Objectives and methodologies for BODE3 • ABC-CBA • NZACE-Prevention • Building capacity and academic rigour • Data inputs to infrastructure • Interventions to assess • Example of “link models”

10. 2010 to 2015 objectives of BODE3 • To estimate the impact and cost-effectiveness of cancer control interventions • Markov time dependent macrosimulation models, and discrete event simulation models • Aotearoa Burden of Cancer and Comparative Benefit Assessment study; ABC-CBA • To estimate the impact and cost-effectiveness of preventive interventions: • multistate lifetables • NZ-Assessing Cost-Effectiveness: Prevention; NZACE-Prevention. • To build capacity and academic rigour in • epidemiological and economic modelling • equity analyses • incorporation of uncertainty • skills and workforce

11. Burden of Disease Cancer Injury Diabetes CVD Palliative care Supportive care, rehabilitation ABC-CBA Treatment Screening Risk Factors NZ-ACE Prevention Integration of BODE3 ABC-CBA and NZACE-Prevention deliberately overlap

12. Objective 1: ABC-CBA Capitalises on data strengths in New Zealand INPUTS OUTPUTS MODELLING Cancer model Averted disability adjusted life years (gained HALYs) Intervention-specific modelling of change in core model parameters (incidence, survival, stage, DW or utility) Systematic review of literature Core disease models - Markov time dependent macrosimulation - Discrete event simulation Specify uncertainty distribution about each input variable Expert opinion HealthTracker (NHI linked data) NZ data Cost effectiveness Attribution of cancer Vote:Health cost over Markov states DRG cost estimates Direct costing of intervention Total change in cost Other cost data Societal costing (if appropriate) Questions and Answers

13. Objective 1: ABC-CBA Data used to build the baseline model • Current and future cancer incidence, by merging: • Ministry of Health projections by sex by 5-year age group, with • Linked census-cancer registration data (i.e. CancerTrends) generated rate ratios of cancer Questions and Answers

14. Breast cancer trends by ethnicity Incidence c.f. mortality trends – census-linked data Incidence from CancerTrends Mortality from NZCMS Index

15. Objective 1: ABC-CBA Data used to build the baseline model • Current and future cancer incidence, by merging: • Ministry of Health projections by sex by 5-year age group, with • Linked census-cancer registration data (i.e. CancerTrends) generated rate ratios of cancer • Excess mortality rates (i.e. relative survival) from CancerTrends • Māori nearly always higher excess mortality (= lower relative survival) • A modest deprivation difference • Cost data from HealthTracker – Vote:Health costs assigned to individuals (will also be used in NZACE-Prevention) • Vote:Health expenditure allocated across all individuals, by year, accounting for up to 80% of Vote:Health budget • Use tabulations and regressions to generate ‘usual’ costs for a person with: • Given disease, or stage of cancer • Within year of death, within 6 months of diagnosis, etc… • These costs become the cost-offsets in economic decision models Questions and Answers

16. Diagnosis & Treatment Remission Cure After 5 years Pre-terminal Died of other causes Terminal Death Objective 1: ABC-CBA Option 1: Time dependent Markov model: Subpopulation Cervical Cancer Maori Women age 45 in 2006 DW=0.25; 3 months DW=0.20; variable time DW=0.75; 5 months DW=0.93; 1 month Questions and Answers

17. Objective 1: ABC-CBA Actual modelling of interventions • Specify intervention • Parameterise in terms of: • Change in incidence rate • Change in survival • Change in stage distribution • Change in quality of life (be that DW or utility) • Change in direct costs (and possible ‘intervention-specific’ cost-offsets downstream) • … often using ‘link models’ such as: • Care co-ordinators (or patient navigators) may hasten receipt of treatment, which requires searching for literature on the impact of treatment ‘X’ weeks earlier on survival chances, estimating ‘X’ for actual intervention, and determining ‘change in survival’ (with uncertainty) • Event pathways for costing • Etc. Questions and Answers

18. Objective 1: ABC-CBA Early set of interventions to model • Selected with stakeholder advisory group; balance of relevance, evidence, academic considerations • Initial set (biased to those with comparators, and equity interest): • Single versus multiple fraction radiotherapy for bone metastases • Docetaxel and paclitaxel for node positive breast cancer • Trastuzumab • Care co-ordinators (or patient navigators) for stage III colon cancer: • Diagnosis to surgery • Surgery chemotherapy • Adherence • Range of tobacco interventions (e.g. doubling calls to quitline) • Aspirin chemoprevention • CT screening for lung cancer • ? Colorectal cancer screening programme Questions and Answers

19. Objective 2: NZACE-Prevention Assessing Cost-Effectiveness of Prevention • Overall aim: • To use an academically rigorous approach to “estimate the disease burden impact and cost-effectiveness of preventive interventions, for the population overall and by ethnicity and socio-economic position”. • Uses multistate lifetables • Builds on ACE-Prevention Australia: • Utilises existing and academically rigorous method • … but will extend this work: context; interventions; methods. • Will use forthcoming New Zealand 2006 burden of disease study parameters (from Ministry of Health)

20. Objective 2: NZACE-Prevention Existing method; selecting of interventions • Focusing on six major risk factors (covering 38% of lost DALYs, all relevant to inequalities) & have initially selected 91 interventions. Main initial focus, combining in absolute risk approach, looking at fiscal policies (i.e. taxes and subsidies)

21. Cost-effectiveness of alcohol interventions ACE-Prevention (Australia), Cobiac et al Questions and Answers

22. Obj. 3: Capacity and academic rigour Methodological research • Equity analysis options – leverage off ‘heterogeneity’ of data. • Separate modelling by social group • Presenting DALYs-averted (HALYs-gained) by: • social group • targetted interventions • We will trial measures of cost expressed per unit change in absolute difference in per capita DALYs averted (HALYs gained) • Equity-weighted benefit measures (e.g. equity weighted HALYs) • Uncertainty analyses: • Parameter uncertainty routinely uses confidence intervals • But systematic error often more important – we will develop frameworks for incorporating systematic error • Need for scenario analyses – not just mechanical PSA • Comparing DALYs & QALYs. • Assessing the difference for an intervention that impacts on disability/quality of life

23. BODE3: Current developments Price elasticities as a complex example of ‘link models’ • Fiscal policies on food gaining momentum, e.g.: • Danish fat tax • Differential VAT by food type in Australia, and removing GST on healthy food in New Zealand • Requires ‘link models’: • Tax/subsidy pass through rate: • Wide range in literature; uncertainty • Own-price elasticity: • E.g. 1% increase in price of fruit leads to 0.6% decrease in consumption (with uncertainty 0.3% to 1.0%) • Cross-price elasticity: • E.g. 1% increase in price of fruit leads to 0.1% increase in consumption of (fatty, salty) potato crisps (with uncertainty …) • Merging change in purchasing data with change in nutrient intake • Specifying the change in nutrients with change in disease

24. BODE3: Current developments Using expert knowledge • All modelling requires ‘judgement’ or expert knowledge in the specification of model structure • Much modelling also requires expert knowledge in the specification of input parameters (e.g. number of weeks a care coordinator can hasten treatment by). There are formal processes for this, e.g.: • Expert panels • Providing what information is known to panel members • Asking them to estimate the most likely value and likely range (e.g. interquartile) for true parameter Leal et al. Eliciting Expert Opinion for Economic Models. Value in Health 2007;10(3):195-203. O’Hagan A. Uncertain judgements. John Wiley and Sons, 2006

25. Data for planning equitable and cost effective health services: An approach from NZ Burden of Disease Epidemiology, Equity & Cost-Effectiveness Programme (BODE3) tony.blakley@otago.ac.nz uow.otago.ac.nz/BODE3-info.html uow.otago.ac.nz/cancertrends-info.html uow.otago.ac.nz/nzcms-info.html