Distributional Cost-Effectiveness Analysis – Making Health Equity Count in HTA

Unfair health inequalities blight lives, generate enormous costs, and exist everywhere. Until recently, however, health technology assessment (HTA) has focused on effectiveness and efficiency rather than equity and inequality.

 

New training resources are now available in methods of distributional cost-effectiveness analysis that can start to change this. DCEA can provide quantitative information about the equity impacts of health technologies and the trade-offs that sometimes arise between equity and efficiency. 

 

The Oxford University Press Handbook of Distributional Cost-Effectiveness Analysis is an all-in-one guide for researchers, policy advisers, policy makers and research funders who wish to learn about, commission and use these methods to reduce health inequalities and promote both equity and efficiency in health and healthcare. 

 
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The handbook provides both hands-on training for postgraduate students and analysts and an accessible guide for academics, practitioners, managers, policymakers, and stakeholders.  Accompanying spreadsheet training exercises are freely available.  Updates on training materials and courses are available via the International Health Economics Association special interest group on equity-informative economic evaluation.

 

What’s not measured gets marginalised. For too long, equity has been marginalised in Health Technology Assessment (HTA).

 

DCEA can provide distributional breakdowns of effects and opportunity costs by equity-relevant social variables or disease categories.  It can also use equity weights to summarise equity impacts and analyse trade-offs between equity and efficiency. 

 

A recent systematic review found 54 peer reviewed DCEA studies published to date, mostly after 2015, relating to diverse disease categories, intervention types and populations and using various equity criteria, with socioeconomic status and race/ethnicity being the most frequent. A large majority of reviewed studies (78%) found a favourable equity impact of the health programme under investigation, and only 6% found an unfavourable equity impact. 

 

This may be a sign of publication bias, whereby equity analysis is more likely to be conducted in cases where a favourable impact is anticipated.  It may also indicate a tendency to focus on the favourable distribution of benefits rather than the unfavourable distribution of burdens due to opportunity costs.  Both issues require attention, since decision makers need to know when equity impacts are unfavourable, and they need a full picture of equity impacts including who bears the largest burdens of opportunity cost as well as who gains the largest benefits.

 

Why subgroup analysis is not enough

 

Effectiveness studies (trials, quasi-experiments, and meta-analysis thereof) can sometimes give partial information on equity impacts, for example through subgroup analysis or trials in disadvantaged populations. However, effectiveness studies usually fail to address equity issues of interest to decision makers, such as:

 

  • The distribution of the health opportunity costs of cost-increasing programmes,
  • Impacts on health inequality within the general population,
  • Impacts on inequality in health outcomes beyond the trial follow-up period,
  • Sizes of health inequality impacts compared with other programmes, and
  • Trade-offs between equity and efficiency objectives.

 

DCEA can provide information about all these issues.

 

WHO has been campaigning about health equity for decades, and this issue has recently been brought to global media attention by glaring inequalities in coronavirus infection and mortality rates related to ethnicity and deprivation.

 

Distributional Cost-Effectiveness Analysis (DCEA)

 

DCEA is an umbrella term for any study that provides information about equity in the distribution of costs and effects as well as value for money. It can be useful whenever a decision is expected to have different consequences for different people, for example, decisions about purchasing new technologies, designing healthcare benefit packages, improving delivery infrastructure and investing in prevention.

 

DCEA can involve simply exploring the implications of giving special priority or “equity weight” to improving the health of programme recipients compared with non-recipients.  It can also involve more detailed analysis of the distribution of health benefits and burdens within the general population by equity-relevant social variables (e.g. socioeconomic status, geographical location, indigenous status, ethnicity, gender, age), disease variables (e.g. disease classification, severity of illness, proximity to death, rarity of condition) or risk factors. It can also involve analysing distributional consequences for non-health outcomes, such as income or financial protection from out-of-pocket health care costs, and evaluation of potential trade-offs between equity and efficiency objectives. 

 

The distinctive aim of DCEA is to extract new information about equity “out” of the analysis, rather than to incorporate value judgements about equity “into” it. DCEA is not about finding an algorithmic approach to replace context-specific deliberation with a universal equity formula.

 

DCEA can be used as an input into context-specific deliberation by decision makers and stakeholders.  HTA inescapably makes value judgements about equity – but implicitly. A common one is, for example, the value judgement that all health-adjusted life-years are equally valuable.  DCEA makes these judgements explicit. It specifies the kinds of distributional consequences to expect and proposes measures. At a minimum, it can provide simple descriptive information.

 

The equity-efficiency impact plane

 

The equity-efficiency impact plane is a way of thinking about trade-offs between efficiency and equity.  It can also be used for visualising the findings of a DCEA study and to help decision makers keep both equity and efficiency objectives in sight.

 

 

Both efficiency impact and equity impact can be measured in various ways, according to the objectives of the relevant decision maker.

 

A policy that falls in the “win-win” quadrant improves both total health and health equity, and one that falls in the “lose-lose” quadrant harms both.  In low- and middle-income countries, vaccination and other infectious disease control programs often fall into the “win-win” quadrant: large population health gains that disproportionately benefits socially disadvantaged groups.  By contrast, investments in high-cost treatments for late-stage chronic disease sometimes fall into the “lose-lose” quadrant of being neither cost-effective nor likely to reduce social inequality in health – in which case other ethical or political arguments are needed to justify funding.

 

Equity and efficiency impacts may also be opposed.  In “win-lose” quadrant, the option is cost-effective (i.e. is improves total health) but harms equity, and in the “lose-win” quadrant, the option is not cost-effective (i.e. is harms total health) but improves equity. When interventions fall in the “win-lose” or “lose-win” quadrant, it may be worth considering re-designing them to move closer to the “win-win” quadrant. 

 

For example, socially disadvantaged groups may gain less than advantaged groups from a decision to fund a medical technology due to unequal access – placing the intervention in the “win-lose” quadrant.  If so, additional investment to improve access for disadvantaged groups might improve the equity impact (i.e. shifting the intervention to the right on the equity impact axis) but could increase total programme costs (i.e. shifting the intervention downwards on the efficiency impact axis). Whether this re-design moves the intervention into the “win-win” quadrant would need careful analysis.

 

Analysing equity-efficiency trade-offs

 

In the “win-lose” and “lose-win” cases, equity trade-off analysis is required.  This analysis can be done informally by making intuitive judgements based on the pre- and post-policy distributions.  It can also be done formally, for example by quantifying equity impacts using specific inequality indices or weighting benefits for certain groups

 

Quick and dirty approaches

 

Simplified “aggregate” approaches are also available, when there is not enough time or resource to conduct a full DCEA study.  These combine aggregate outputs from standard CEA with information about distributions of the relevant disease or risk factors and utilisation of the relevant category of care.  This can provide some useful information about equity impact, even without detailed modelling.

 

Conclusion

 

The Handbook of Distributional Cost-Effectiveness Analysis should stimulate studies that combine efficiency and equity in all countries, whatever their stage of development, where equity in health and healthcare is of concern, or where aspirations to Universal Health Coverage are an imperative. These developments should also spur theorists and practitioners to develop further techniques and create better data for decisions and, of course, better decisions.

 

 

 

 

 

 

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A decisive contribution! An extremely valuable insight from a leading-edge expert in the field and certainly a lot of fuel for thought for the whole Decide ecosystem. Thanks a lot Richard!