Will integration widen income-related health inequalities?

Just returned from Dublin, where I attended the International Health Economics Association annual conference. I’m amazed at how far health economics has progressed in the last 20 years. In the late 90s, my only exposure to the iHEA was dominated by studies on cost-effectiveness and quality-of-life research. While there were still sessions in Dublin on these topics, the research area of health economics is richer and more relevant to policy, with a growing number of researchers who come from other disciplines. It can only be a good thing for health economics and its impact on policies.

I was at the conference to chair two sessions. The first session focused on the return-on-investment of public health interventions, and the second one on health inequalities. The first session presented the return-on-investment tools NICE has recently developed for local communities in the areas of tobacco control and alcohol. These tools and plans for their use can be found on the website.

The second session brought together different strands of inequalities work, such as why we must take into account changes in population, like migration, birth, and death, when measuring health inequality in relation to incomes over time. It is important to take into account population changes when assessing the government’s performance on policies such as “improving the health the poorest the fastest”.

study from the early 2000s showed that the increase in income-related inequalities in health (measured in quality-adjusted years of life or QALYs) was masked by the influx of young, poorer, and healthier people entering the population through births and immigration, and the emigration of older, sicker, and more impoverished individuals.

It is important to consider how the integration of care can affect income-related inequalities of health over time. Integration that is successful in keeping the sick and poor alive may exacerbate income-related inequality on quality of living measures since these groups, by definition, have a lower standard of living. This would be a clear success of policy and not a failure. Integrating would improve the statistics on life expectancy for the poor, as they would not be dying so young. Inequalities in life expectancy would also be reduced. The impact on composite outcome measures, such as healthy living expectancy, will be complex.

We need to be careful when evaluating the impact of policies, the choice of metrics, and the population dynamics over time to determine the success of the policies and ambitions to reduce health inequalities.

Unexpectedly, outside my session, the themes of inequalities, health determinants, and wider health determinants were prominent. This is a good sign that health economists are looking beyond cost-effectiveness. The impact of the recession and the importance of wider determinants of health were discussed in depth. There was also a lot of information that was relevant to debates about NHS funding, such as the impact of changes in how NHS resources are distributed to address inequality and a reassessment of what drives NHS costs. The presence of comorbidities could drive more NHS costs compared to aging and expenses incurred in the last few weeks or months before someone’s death.

Watch this space. Much of what has been presented is still a work in progress. But there’s no doubt that health economists will be back with more to say.

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