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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

What I think I was meant to talk about

• 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!

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NZ Census-Mortality Study (NZCMS) and CancerTrends

What is the next problem?

• The 100 manila folder problem• “Please sit on this committee, and advise us what to

do next.”

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Policy making without synthesising evidence

What are the opportunities?

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

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Leverage existing data and methods

What is the idea?

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

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Build infrastructure for rapid cost effectiveness analysis

Focus on economic decision models

• 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 6

Which is just one input into the decision making process

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Vision of BODE3

“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.”

HRC-funded programme 2010-15; Ministry collaboration

Burden of Disease Epidemiology, Equity & Cost-Effectiveness Programme (BODE3)

uow.otago.ac.nz/BODE3-info.html

Yes, we will mainly use DALYs…

• 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

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…. but in cost-effectiveness little different from QALYs

Presentation

• 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”

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Structure of presentation to you today

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2010 to 2015 objectives of BODE3

1. 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

2. To estimate the impact and cost-effectiveness of preventive interventions:– multistate lifetables

– NZ-Assessing Cost-Effectiveness: Prevention; NZACE-Prevention.

3. To build capacity and academic rigour in – epidemiological and economic modelling

– equity analyses

– incorporation of uncertainty

– skills and workforce

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Integration of BODE3

ABC-CBA and NZACE-Prevention deliberately overlap

Palliative care

Supportive care, rehabilitation

Treatment

Burden of Disease

Risk FactorsNZ-ACE Prevention

AB

C-C

BA

Screening

Injury

CVDDiabetes Cancer

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Objective 1: ABC-CBA

Core disease models

- Markov time dependent

macrosimulation

- Discrete event simulation

Averted disability

adjusted life years (gained

HALYs)

Total change in

cost

Cost effectiveness

Intervention-specific

modelling of change in core

model parameters (incidence,

survival, stage, DW or utility)

Direct costing of intervention

Expert opinion

Systematic review of literature

HealthTracker (NHI linked data)

NZ data

DRG cost estimates

INPUTS OUTPUTS

Other cost data

Spe

cify uncertainty distribution about

each input variable

Attribution of cancer Vote:Health cost over Markov

states

Cancer model

Societal costing (if

appropriate)

MODELLING

Capitalises on data strengths in New Zealand

Questions and Answers

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Objective 1: ABC-CBAData 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

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Breast cancer trends by ethnicityIncidence c.f. mortality trends – census-linked data

Index

Incidence from CancerTrends

Mortality from NZCMS

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Objective 1: ABC-CBAData 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

• 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

Objective 1: ABC-CBAOption 1: Time dependent Markov model:

Subpopulation

Cure

Diagnosis & Treatment Remission

Pre-terminal

Terminal

Death

Died of other causes

Maori Women age 45 in 2006

Cervical Cancer

DW=0.25; 3 months DW=0.20; variable time

DW=0.75; 5 months

DW=0.93; 1 month

After 5 years

Questions and Answers

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Objective 1: ABC-CBAActual 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

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Objective 1: ABC-CBAEarly 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

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Objective 2: NZACE-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 lifetablesBuilds 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)

Assessing Cost-Effectiveness of Prevention

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Objective 2: NZACE-Prevention

Focusing on six major risk factors (covering 38% of lost DALYs, all relevant to inequalities) & have initially selected 91 interventions.

Risk factor Examples of interventions to model

Tobacco useTobacco taxation increases, mass media campaigns, to expanding Quitline use and providing new nicotine products for quitting.

High blood pressureReduction of salt in processed foods (voluntary and mandated options), to the introduction of the polypill.

High cholesterolPromoting the use of food products with plant sterols to expanding the use of statins and introduction of the polypill.

Alcohol useAlcohol taxation increases and alcohol advertising restrictions, to brief interventions (by GPs).

Physical inactivityMass media-based campaigns and community programmes to encourage use of pedometers, to a “green prescription” from a GP.

Overweight & obesity Reduction of TV advertising (high fat/high sugar foods and drinks), to diet and physical activity programmes.

Existing method; selecting of interventions

Main initial focus, combining in absolute risk approach, looking at fiscal policies (i.e. taxes and subsidies)

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Cost-effectiveness of alcohol interventions

ACE-Prevention (Australia), Cobiac et al

Questions and Answers

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Obj. 3: Capacity and academic rigour

1. 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)

2. 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

3. Comparing DALYs & QALYs. – Assessing the difference for an intervention that impacts on disability/quality of

life

Methodological research

BODE3: Current developments

• 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 23

Price elasticities as a complex example of ‘link models’

BODE3: Current developments

• 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

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Using expert knowledge

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

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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.htmluow.otago.ac.nz/cancertrends-info.html

uow.otago.ac.nz/nzcms-info.html