How important is healthcare for population health?

To what extent does a population’s access to healthcare determine its health? I have heard the claim that “as little as 10% of a population’s health is linked to access to healthcare”, or some variant of it, in many places. Some examples include the Health Foundation, the AHRQ, the King’s Fund, the WHO, and determinantsofhealth.org. This claim is appealing: it feels counter-intuitive and it brings to the fore questions of public health and health-related behaviour. But it’s not clear what it means.

I can think of two possible interpretations. One, 10% of the variation in population health outcomes is explained by variation in healthcare access. Or two, access to healthcare leads to a 10% change in population health outcomes compared to no access to healthcare. Both of these claims would be very hard to evaluate empirically. Within many countries, particularly the highest income countries, there is little variation in access to healthcare relative to possible levels of access across the world. Inter-country comparisons would provide a greater range of variation to compare to population outcomes. But even the most sophisticated statistical analysis will struggle to separate out the effects of other economic determinants of health.

It would also be difficult to make sense of any study that purported to estimate the effect of adding or removing healthcare beyond any within-country variation. The labour and capital resource needs of the most sophisticated hospitals are too great for the poorest settings, and it is unlikely that the wealthiest democratic countries could end up with the level of healthcare the world’s poorest face.

But what is the evidence for the claim of 10%? There are a handful of key citations, all of which were summarised at the time in a widely cited article in Health Affairs in 2014. For each of the two ways we could think about the contribution of healthcare above, we would need to look at estimates of the probability of health conditional on different levels of healthcare, Pr(health|healthcare). Each of the references for the 10% figure above in fact provides evidence for the proportion of deaths associated with ‘inadequate’ healthcare, or to put in another way, the probability of having received ‘inadequate’ care given death, Pr(healthcare|health). This is known as transposing the conditional: we have got our conditional probability the wrong way round. Even if we accept mortality rates as an acceptable proxy for population health, the two probabilities are not equal to one another.

Interpretation of this evidence is also complex. Smoking tobacco, for example, would be considered a behavioural determinant of health and deaths caused by it would be attributed to a behavioural cause rather than healthcare. But survival rates for lung cancers have improved dramatically over the last few decades due to improvements in healthcare. While it would be foolish to attribute a death in the past to a lack of access to treatments which had not been invented, contemporary lung cancer deaths in low income settings may well have been prevented by access to better healthcare. Thus using cause-of-death statistics to estimate the contributions of different factors to population health only typically picks up those deaths resulting from medical error or negligence. They are a wholly unreliable guide to the role of healthcare in determining population health.

A study published recently in The Lancet, timed to coincide with a commission on healthcare quality, adopted a different approach. The study aimed to estimate the annual number of deaths worldwide due to a lack of access to high-quality care. To do this they compared the mortality rates of conditions amenable to healthcare intervention around the world with those in the wealthiest nations. Any differences were attributed to either non-utilisation of or lack of access to high-quality care. 15.6 million ‘excess deaths’ were estimated. However, to attribute to these deaths a cause of inadequate healthcare access, one would need to conceive of a counter-factual world in which everyone was treated in the best healthcare systems. This is surely implausible in the extreme. A comparable question might be to ask how many people around the world are dying because their incomes are not as high as those of the top 10% of Americans.

On the normative question, there is little disagreement with the goal of achieving universal health coverage and improving population health. But these dramatic, eye-catching, or counter-intuitive figures do little to support achieving these ends: they can distort policy priorities and create unattainable goals and expectations. Health systems are not built overnight; an incremental approach is needed to ensure sustainability and affordability. Evidence to support this is where great strides are being made both methodologically and empirically, but it is not nearly as exciting as claiming healthcare isn’t very important or that millions of people are dying every year due to poor healthcare access. Healthcare systems are an integral and important part of overall population health; assigning a number to this importance is not.

Picture credit: pixabay

Paul Mitchell’s journal round-up for 17th April 2017

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Is foreign direct investment good for health in low and middle income countries? An instrumental variable approach. Social Science & Medicine [PubMed] Published 28th March 2017

Foreign direct investment (FDI) is considered a key benefit of globalisation in the economic development of countries with developing economies. The effect FDI has on the population health of countries is less well understood. In this paper, the authors draw from a large panel of data, primarily World Bank and UN sources, for 85 low and middle income countries between 1974 and 2012 to assess the relationship between FDI and population health, proxied by life expectancy at birth, as well as child and adult mortality data. They explain clearly the problem of using basic regression analysis in trying to explain this relationship, given the problem of endogeneity between FDI and health outcomes. By introducing two instrumental variables, using grossed fixed capital formation and volatility of exchange rates in FDI origin countries, as well as controlling for GDP per capita, education, quality of institutions and urban population, the study shows that FDI is weakly statistically associated with life expectancy, estimated to amount to 4.15 year increase in life expectancy during the study period. FDI also appears to have an effect on reducing adult mortality, but a negligible effect on child mortality. They also produce some evidence that FDI linked to manufacturing could lead to reductions in life expectancy, although these findings are not as robust as the other findings using instrumental variables, so they recommend this relationship between FDI type and population health to be explored further. The paper also clearly shows the benefit of robust analysis using instrumental variables, as the results without the introduction of these variables to the regression would have led to misleading inferences, where no relationship between life expectancy and FDI would have been found if the analysis did not adjust for the underlying endogeneity bias.

Uncovering waste in US healthcare: evidence from ambulance referral patterns. Journal of Health Economics [PubMed] Published 22nd March 2017

This study looks to unpick some of the reasons behind the estimated waste in US healthcare spending, by focusing on mortality rates across the country following an emergency admission to hospital through ambulances. The authors argue that patients admitted to hospital for emergency care using ambulances act as a good instrument to assess hospital quality given the nature of emergency admissions limiting the selection bias of what type of patients end up in different hospitals. Using linear regressions, the study primarily measures the relationship between patients assigned to certain hospitals and the 90-day spending on these patients compared to mortality. They also consider one-year mortality and the downstream payments post-acute care (excluding pharmaceuticals outside the hospital setting) has on this outcome. Through a lengthy data cleaning process, the study looks at over 1.5 million admissions between 2002-2011, with a high average age of patients of 82 who are predominantly female and white. Approximately $27,500 per patient was spent in the first 90 days post-admission, with inpatient spending accounting for the majority of this amount (≈$16,000). The authors argue initially that the higher 90-day spending in some hospitals only produces modestly lower mortality rates. Spending over 1 year is estimated to cost more than $300,000 per life year, which the authors use to argue that current spending levels do not lead to improved outcomes. But when the authors dig deeper, it seems clear there is an association between hospitals who have higher spending on inpatient care and reduced mortality, approximately 10% lower. This leads to the authors turning their attention to post-acute care as their main target of reducing waste and they find an association between mortality and patients receiving specialised nursing care. However, this target seems somewhat strange to me, as post-acute care is not controlled for in the same way as their initial, insightful approach to randomising based on ambulatory care. I imagine those in such care are likely to be a different mix from those receiving other types of care post 90 days after the initial event. I feel there really is not enough to go on to make recommendations about specialist nursing care being the key waste driver from their analysis as it says nothing, beyond mortality, about the quality of care these elderly patients are receiving in the specialist nurse facilities. After reading this paper, one way I would suggest in reducing inefficiency related to their primary analysis could be to send patients to the most appropriate hospital for what the patient needs in the first place, which seems difficult given the complexity of the private and hospital provided mix of ambulatory care offered in the US currently.

Population health and the economy: mortality and the Great Recession in Europe. Health Economics [PubMed] Published 27th March 2017

Understanding how economic recessions affect population health is of great research interest given the recent global financial crisis that led to the worst downturn in economic performance in the West since the 1930s. This study uses data from 27 European countries between 2004 and 2010 collected by WHO and the World Bank to study the relationship between economic performance and population health by comparing national unemployment and mortality rates before and after 2007. Regression analyses appropriate for time-series data are applied with a number of different specifications applied. The authors find that the more severe the economic downturn, the greater the increase in life expectancy at birth. Additional specific health mortality rates follow a similar trend in their analysis, with largest improvements observed in countries where the severity of the recession was the highest. The only exception the authors note is data on suicide, where they argue the relationship is less clear, but points towards higher rates of suicide with greater unemployment. The message the authors were trying to get across in this study was not very clear throughout most of the paper and some lay readers of the abstract alone could easily be misled in thinking recessions themselves were responsible for better population health. Mortality rates fell across all six years, but at a faster rate in the recession years. Although the results appeared consistent across all models, question marks remain for me in terms of their initial variable selection. Although the discussion mentions evidence that suggests health care may not have a short-term effect on mortality, they did not consider any potential lagged effect record investment in healthcare as a proportion of GDP up until 2007 may have had on the initial recession years. The authors rule out earlier comparisons with countries in the post-Soviet era but do not consider the effect of recent EU accession for many of the countries and more regulated national policies as a consequence. Another issue is the potential of countries’ mortality rates to improve, where countries with existing lower life expectancy have more room for moving in the right direction. However, one interesting discussion point raised by the authors in trying to explain their findings is the potential impact of economic activity on pollution levels and knock-on health impacts from this (and to a lesser extent occupational health levels), that may have some plausibility in better mortality rates linked to physical health during recessions.

Credits

Economics of personalised medicine: an introduction

Personalised medicine appears to be an inevitable future of health care, and economists aren’t ready for it.

It has various monikers and related concepts including precision medicine, stratified medicine, pharmacogenomics, pharmacogenetics and predictive medicine. But, whatever you call it, it means big changes in health care. Sociologists, ethicists, medics and others have all been confronting it in recent years. Economists have been relatively slow on the uptake, though some have begun thinking about it (see for example here, here, here)

Some of my current work involves evaluating predictive medicine in the form of a screening intervention, and we have a paper in the pipeline discussing some of the implications for cost-effectiveness analysis. This work is presenting a number of new challenges but is also highlighting some opportunities for the optimisation of health care.

Over the next few months I will be introducing and discussing some of the potential implications of personalised medicine for our discipline. These will include familiar topics in health economics and will probably fall under the following headings:

  1. Demand for health and health care
  2. Need
  3. Supply of health care
  4. Provider behaviour
  5. Health insurance
  6. Costs
  7. Economic evaluation
  8. Decision modelling
  9. Population health
  10. New market failures
  11. Equity

These may merge, change or disappear as I progress, but I hope to cover as many angles as possible. Hopefully, with your feedback, we might be able to help guide future work in this area.