A novel health investment framework: targeting health for socioeconomic development

Health economists have been studying the role that health plays in societal growth and development over the last several decades. Although there is extensive research linking health to positive socioeconomic outcomes at the population level, leaders continue to struggle to prioritize health investment among other shorter-term endeavors. This leads to questions being raised about the value of healthcare spending overall. In order to shift momentum towards more strategic and effective investment in health, leaders need to engage in more dynamic and innovative thinking about how to generate better outcomes from healthcare at lower cost.

To help achieve this goal, Economist Enterprise embarked on an ambitious research program to examine the complex relationship between health and wealth, and uncover how leaders can strategically invest in healthcare systems to optimize health and economic outcomes. Our research approach consisted of a mixed-methods process:

• In-depth literature review
• Advisory panel meeting and bilateral interviews with global experts in the field
• Construction of a novel framework for variable selection and collecting data, including over 50,000 data points across over 15 years (2004-2019) in the US at the state level
• Development of baseline model and scenarios based on this data, including a novel theoretical directed acyclic graph (DAG) to establish relationships between variables

Gaps in health economics for informed decision-making

Through our initial review of the evidence, we realized that the existing studies do establish a link between health and wealth, but the impact of the size and source of health investment is inconclusive.^1-15

For example, three of the studies we looked at reach contradictory conclusions about the influence and magnitude of impact of public versus private health expenditure.^16-18 Existing models are also largely focused on different variations of life expectancy’s incremental impact on GDP, and fail to capture critical nuances behind these measures that feel real and urgent. Most of these studies look at what can be attained, and not how to attain it.

As with any modeling, analytical and economic constraints must be considered. Macro-level observational data that has not been experimentally manipulated can pose challenges for causal inference due to potential endogeneity (ie, the property of being influenced within a system) and reverse causality among variables. In light of these observations, we determined that a new model must be designed to demonstrate relationships in a more granular way, which takes into account risks of endogeneity and causality yet still reflects the systemic nature of this issue.

While building this model, we consulted sources of established health systems theory such as the Harvard Flagship Model¹⁹ and the WHO Health System Building Blocks.²⁰ However these existing models showed limitations which we would need to overcome. The Harvard Flagship Model includes regulation as one of the control knobs, but does not include accountability. Formulating policies does not guarantee success; it is the implementation that matters, which can be ensured by making health systems more accountable.

Both the Harvard Flagship Model¹⁹ and WHO Health System Building Blocks²⁰ do not look at personal expenditure on healthcare (which includes insurance co-payments, out of pocket payments, etc.). They restrict analysis to government financing and incentives for healthcare workers/service providers. Finally, neither of these models link health outcomes to the wider economy. They only examine the effect of healthcare on financial risk of an individual, whereas healthcare can have much wider effects on outcomes like poverty, tax revenues, and more.^21,22

The framework

Fueled by the need to overcome these drawbacks, we created a new framework (see Figure 1) and populated it using US data over a 15-year time horizon. We collected more than 50,000 data points across 64 indicators, dividing them into key “levers”, or groups of variables, across the three categories.

The first step is focused on levers that are mostly in the hands of policymakers (with the exception of demographics) and examines the decisions that health leaders are making when it comes to the allocation of health resources. These levers show how a health system (the US, in this case) is financed, the current capacity of the existing healthcare infrastructure, the regulations that are in place and population health-related behaviors and risk factors (such as smoking and obesity) including interactions with the health system (such as visits for dental care), which are potential stressors on the health system and also influenced by health system attributes and resource allocation. For example, the demographics lever includes indicators like total population, birth rate, old-age dependency ratio, etc. The first step helps in understanding how resources are being prioritized currently.

The second step looks at a focused set of indicators of health outcomes, including levers of morbidity and mortality. These health outcomes look beyond the usual life expectancy to GDP model that many existing studies are based on,²³ and allow us to measure how effective health systems are in improving more specific health outcomes of the population through the current allocation of resources (in relation to Step 1). It also serves a dual function as performance goals that governments can consider when they prepare their health budgets and targets. The final step (Step 3) attempts to capture the impact that health outcomes have on the economy. It allows us to explore how changes in population health can directly affect the socio-economic performance of a country and make the case for more effective investment in healthcare as a driver of economic growth.

We started our analysis with a much broader list of indicators, but chose the most relevant and highest quality indicators through a structured selection process. Our model at present only focuses on the linear impact of the levers which are in the hands of health policymakers—health system attributes and resource allocation. Future iterations of the model could also consider feedback loops, such as the impact of Step 3 variables that are social determinants of health²⁴ on the health outcomes in Step 2.

Conclusion

This framework enables us to understand some of the critical nuances that link health and economic growth. We have taken a systems view to inform allocative efficiency of healthcare investment, as opposed to solely looking at its impact on national GDP. It is focused on systems (rather than harmful silos), which provides more pragmatic insights.

Most models currently focus on specific conditions or aspects of the health system, but in reality, this does not align with the way that countries budget or spend on health. This tool seeks to enable policymakers and budget holders to make decisions while realizing returns on healthcare investment at a top level. That being said, just like other models, this one also has some constraints that need to be considered when interpreting the data (see article on Recognizing limitations and Technical Report).