How can economics help decision- makers reduce health inequalities (inequities)?

1. How can economics help decision- makers reduce health inequalities (inequities)? Kenny Lawson Principal Research Fellow in Health Economics James Cook University

2. Five brief parts • What do we mean by equity? • Can economic help? • Resource allocation formulas • Equity weights in economic evaluation • Priority setting processes

3. What do we mean by equity?

4. Distinguishing between - “inequality” and “inequity” • Inequality: A generic term referring to the uneven distribution of health – purely descriptive term. • Inequity: A specific term that refers to avoidable differences and considered unjust. • Often an important distinction, inequalities does not necessarily mean inequitable – e.g. males and females, • This raises the question "when does inequality in health constitute inequity?" • This then focuses our policy objectives/targets

5. The key problem: social gradients Korda (2009) Narrow definition of “avoidable mortality”: Quote “Premature deaths from certain conditions that are considered to be largely avoidable given timely and effective health care) ….what about the wider determinants that are amenable to intervention?

6. Equity of access? Equity of outcome? • Likely to be significant resource implications • Horizontal equity? Equal “access/outcome” for equal need • Vertical Equity? Unequal “access/outcome” for unequal need – i.e. more focus on least well-off • Words are not enough – don’t we need a clear, measurable, and achievable target? • “Closing the gap: alludes to parity in life-expectancy (as much as a 14 year gap) by 2031 • - ….do we have the resources/knowledge to do this? So what do we what to achieve?

7. Can economics help? A recap on an economist way of thinking

8. (Recap) First principles • Develop a common measuring stick • To compare different interventions (apples & oranges) • Generic outcomes (DALYs, $), and costs ($) • Opportunity cost • Every time we use resources to meet one need, we give up the opportunity to use those resources to meet some other need • The margin • Technically, the extra cost/benefit associated with one more unit of production

9. “Value for money” (VFM): Decision making Best buys (I): choosing a specific intervention e.g. which housing intervention offers best VFM = technical efficiency Best buys (II) choosing between different interventions e.g. are statins or a health counsellor service best VFM = “allocative efficiency”

10. What’s missing: EQUITY ! • At the moment…“DALY is a DALY is a DALY” • Same valuation regardless of who gains! • Why? The illusion of seperatling out ‘efficiency’ from equity…. • Impossible – ethics always come 1st, economics 2nd Current approach can reinforce inequalities • Problem is recognised (by the sensible ones…), but yet just 2.5% of publications concern equity (Richardson, 2011) • So, from a practical viewpoint what can we do….?

11. Resource allocation formulas

12. Allocation of funds • Department of Health figures (2010/11): • Expenditure on Aboriginal and Torres Strait islander population was $4.6 billion • 3.7% of total exp, with a population share of 2.5% • Per person: $7,995 - 1.47 times higher than non-indigenous population • Is that fair? Do we know what would be?

13. What “should it be” ? • McDermott and Beaver (1996) – a ratio of 4 • Citizen’s jury in Perth – a ratio of 2.7 • Yerrigan calculate three components: • 2.9 (based on need), • 1.2 (for positive discrimination) • 1.75 for cultural security = a ratio of 5 * The main point is that we could do with transparency?

14. Resource allocation formulas: Generic stages • Total resource envelope 2. % given each category (e.g. 30% based on population size) 3. Share of each location in each category (e.g. area X has Y% share of national population. 4. Repeat for each category Its use comes in-and-out of favour

15. Resource allocation formulas: In action • Their use is quite wide (sometimes not so transparent): • The dimensions commonly based on are (Penno, et al): • Differential Need • Access • Unmet Need • Often proxies used: 1. Need: Population size, disease records, ethnicity 2. Access: Additional cost in delivery e.g. geography, public/private mix 3. Unmet need: Disproportionate outcomes e.g. disease specific (CVD), overall health expectancy

16. Challenges to RAF • How to combine categories / weights • Marginality compromised – what happens after we give the resources • Capacity issues to absorb funding: do we need “institutional readiness” to absorb the funding efficiently? • More work needed on this and also whether “proxies” are a fair reflection • In Victoria, Indigenous Community received X 2.5 than of non-indigenous

17. Equity weights in economic evaluation

18. Potential approaches 1: Equity Weights in CEA • We “weight” outcomes already….”quality adjustment” • Life expectancy to DALYs….e.g. 10 life years that are lived in 50% full health = 5 DALYs • So can be add an additional weight conditional upon initial distribution of health – where we give more value to an additional DALY for those with the least baseline health? • What should the weight be? What is our objective? Parity of health outcomes? • “Lifetime shortfall” – e.g. if group X has 20% less life expectancy then increase value of an extra life-year in that group by 20%?

19. Potential approaches 1: Equity Weights in CBA • Cost benefit analysis monetizes all outcomes based on “willingness to pay” • But “Willingness to pay” (WTP) is conditional upon “ability to pay” • Equity weights: E.G. Inflate WTP response by % difference from average income…. • E.G. If group X has an average salary 20% less than average income, then increase WTP response by 20% = so we value an extra DALY 20% higher value in that group

20. What happens in practice….. • ……Just one example in practice…… World Health Organisation (WHO) age and sex weights in DALYs • We lack an agreed set of categories for equity weights, and the relevant value judgments/data - disagreements in literature • Potential problems(?): • e.g. age weights need to adjust outcomes as patient age (?), • multiple weights perhaps…what about individual behaviour (e.g. risk) • How to combine different weights….additive/multiplicative… • Values may change over time…need to repeat reweighting periodically

21. Get on with it….. • “Don’t let perfect be the enemy of the good” • No weighting may be the worst assumption of them all • Decision making is a “live process” we can present information unweighted and weighted, and stakeholders then decide….

22. “Priority setting tools”

23. Decision making is a management process • Not an algorithm – “effect estimates” will never be enough • Key issue in translation ….”misalignment of incentives”: • E.G. Service managers = time pressure (do things); Researchers = purism of effect (is it right); Politicians = vote winners (will folk like it) • Rather than (only) provide evidence, can we “engage” directly to shape the decision making process • …….according to economic principles?

24. Programme budgeting and marginal analysis PBMA addresses priorities from the perspective of resources: 1. What resources are available in total? (role of “Resource allocation formulas) 2. In what ways are these resources currently spent? 3. What are the main candidates for more resources & what would be their effectiveness / cost? (Equity weights can come into play here) 4. Are there any areas of care which could be provided to the same level of effectiveness but with less resources, so releasing those resources to fund candidates from (3)? 5. Are there areas of care which, despite being effective, should have less resources because a proposal from 3. is more effective (for £s spent)? • Questions 1 and 2 pertain to the PROGRAMME BUDGET; Questions 3-5 are addressed in MARGINAL ANALYSIS

25. Logic of the process • What are the services where decision makers are prepared to make changes in (we can change everything at once…policymaking is a continuous process!) • What are important are costs and benefits of changes in that mix • If the mix of services can be changed to produce more benefit overall, this “should” be done • Economic thinking is not about cutting costs, but rather using what we have to maximise our benefits • Economics can help our fight against inequity

26. Examples in practice • Not routinely used in the policy-cycle • Key problems…..we return to “misalignment of incentives again”…. • Preconditions of success: needs buy-in and time from senior civil servants, and then political follow-through on the recommendations • But, many case study examples (mainly developed) world: Australia, New Zealand, Canada, UK • So how can we alignment incentives to help ensure the preconditions for successful priority setting are met?

27. Realigning incentives: “Communitarian” approach to priority setting • Priority setting is inherently an ethical process • Primary stakeholder should really be the community themselves (Gavin Mooney) - closer involvement/power to oversee the process… • The community have interests in: long term impacts, efficiency, improvement in overall social welfare, equity. • They can then align other actors to follow these objectives (politicans, civil servants, service staff) • So, consulate to elicit community objectives/trade-offs/values - informed consultation where “experts” come in to inform these preferences.

28. Policy makers convinced too? Inclusive decision making • Dr Peter Shergold, former head of the Department of the Prime Minister and Cabinet and the Aboriginal and Torres Strait Island Commission • He called for Indigenous Australians…” to be given the chance to take control of their lives, for political and administrative barriers to be cast aside in place of public and social innovation, and for new approaches to be trialled and evaluated.” (Craven & Dillon, 2013)

29. Summary • Equity considerations and priority setting is unavoidable • Decisions need to happen; we need to engage • Translation: Economics can help provide a framework for transparent/accountable decision making • But the actual value judgments (e.g. objectives, equity weights) need to come from the population/community • Economics needs to improve our way of eliciting community judgments – lets work together as researchers • “Don’t let perfect be the enemy of the good” • Key thing is for transparency & accountability • Cause for optimism: Central role of the community [e.g. Aboriginal Community Controlled Health Services (ACCHS) ]