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In the complex tapestry of healthcare, there exist easily recognizable inequities in access, treatment options, and outcomes. They manifest in various ways: from life expectancy to the burden of diseases and the quality of care delivered. Sometimes, these inequalities are associated with multiple factors, such as socioeconomic status, race, gender, age, geographic location, employment status, and income level. By and large, Health Economics and Outcomes Research (HEOR) seeks to unlock the complex relationship between economics, healthcare outcomes, and social drivers of health. With a set of powerful and sophisticated tools in modeling and analyses at its disposal, HEOR works to grasp the underlying causes of these inequities and proposes effective interventions.[1,2]

Health inequalities are not only a matter of statistics; but are also profound reflections of systemic injustices pointing towards broader issues of social justice and systemic discrimination. In this case, such disparities – whether in life expectancy, burden of disease, or access to healthcare – are all borne disproportionately by the marginalized communities. Since the seminal study in 2010 that established the connection between social status and health, health inequalities have garnered significant attention.[3] However, as of 2024, these inequalities not only persisted but, in some aspects of health had actually increased. A study in 2020 concluded that inequality has increased considerably over time: with life expectancy improving for the top 60% of the earners while stagnating for the poorest 40%. Moreover, the life expectancy for women has declined in the bottom five deciles of deprivation – a very certain and grave indication of the growing divide between the rich and the poor.[4]

These observations emphasize the imperative to address the escalating health inequalities in our society.[5] HEOR plays a crucial role in this effort by allowing us to delve deeper into the roots of these inequalities and offer data-driven solutions. HEOR’s arsenal of modeling techniques offers a window into the complex dynamics of health inequalities. From economic modeling to predictive analytics and simulation methods, these tools enable researchers to dissect the multifaceted nature of disparities.[3]

Models, such as economic models, can be used to screen interventions for cost-effectiveness while considering health outcomes at the same time. Cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) are the two commonly used methods that see which of the chosen approaches is going to give better value for money in the reduction of health disparities.[6] Models of this nature can be used to show how the limited resources can be better allocated so as to have a more positive effect on health in disadvantaged communities. Another key tool involves predictive models that allow data to project health outcomes by social determinants like income and education. This kind of model can identify which groups are at a higher risk of health problems and would be helpful in the development of targeted interventions. Simulation models go a step further by allowing researchers to test different scenarios for their potential impact on health inequalities. This helps policymakers understand the potential outcomes of different policies or programs before they are implemented.[7-9]

The application of HEOR modelling extends beyond theoretical abstraction, translating into tangible actions to combat health inequalities. These models look underneath the aggregate data and give the outline of disparities, pinpointing exactly where interventions are most urgently needed. Armed with this information, policymakers can craft evidence-based policies that address the root causes of health inequities, allocate resources equitably, and ultimately reduce health disparities. Furthermore, HEOR models serve as a litmus test for the effectiveness of interventions, enabling continuous refinement and optimization of strategies to narrow the health gap.[9]

The path toward health equity is not without obstacles. Data limitations and related variables can affect the accuracy of models, leading to incomplete or skewed analyses. Moreover, social determinants of health are complex, often interacting in ways that are difficult to predict. Biases are another factor that can reflect differences in the final result.[10,11]

Some of these challenges can be tackled with advances in technology. Artificial intelligence and machine learning provide new ways of analyzing large datasets to uncover complex patterns that could potentially lead to more effective solutions for health inequalities.[9] However, at the same time with these advancements, there is a need for far greater commitment to ethical practices and a focus on fairness and equity.[12,13]

In conclusion, modeling health inequalities in HEOR is essential to creating a more equitable healthcare system. By using models to understand and address health disparities, we can navigate this labyrinth, paving the way for a future where resources are allocated based on need and every individual has an equal opportunity to thrive and achieve better health outcomes.

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References

1. Islam MM. Social determinants of health and related inequalities: confusion and implications. Frontiers in public health. 2019 Feb 8;7:414037.
2. Singu S, Acharya A, Challagundla K, Byrareddy SN. Impact of social determinants of health on the emerging COVID-19 pandemic in the United States. Frontiers in public health. 2020 Jul 21;8:564623.
3. Marmot M. Fair society, healthy lives. Fair society, healthy lives. 2010. https://www.instituteofhealthequity.org/resources-reports/fair-society-healthy-lives-the-marmot-review/fair-society-healthy-lives-full-report-pdf
4. Marmot, Michael. 2020. “Health equity in England: the Marmot review 10 years on.” BMJ 368.
5. Mahase E. A decade on from Marmot, why are health inequalities widening?. BMJ: British Medical Journal (Internet). 2019 Jun 17;365:l4251.
6. Cookson R, Mirelman AJ, Griffin S, Asaria M, Dawkins B, Norheim OF, Verguet S, Culyer AJ. Using cost-effectiveness analysis to address health equity concerns. Value in Health. 2017 Feb 1;20(2):206-12.
7. Rojas JC, Fahrenbach J, Makhni S, Cook SC, Williams JS, Umscheid CA, Chin MH. Framework for integrating equity into machine learning models: a case study. Chest. 2022 Jun 1;161(6):1621-7.
8. Wolfson MC. POHEM: a framework for understanding and modelling the health of human populations. World health statistics quarterly 1994; 47 (3/4): 157-176. 1994.
9. Smith BT, Smith PM, Harper S, Manuel DG, Mustard CA. Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions. J Epidemiol Community Health. 2014 Apr 1;68(4):384-9.
10. Perrin E, Ver Ploeg M, editors. Eliminating health disparities: measurement and data needs.
11. Bilheimer LT, Klein RJ. Data and measurement issues in the analysis of health disparities. Health services research. 2010 Oct;45(5p2):1489-507.
12. Haseltine WA. Can Artificial Intelligence Help Eliminate Health Disparities? 2024 https://www.insideprecisionmedicine.com/topics/can-artificial-intelligence-help-eliminate-health-disparities
13. Chen IY, Joshi S, Ghassemi M. Treating health disparities with artificial intelligence. Nature medicine. 2020 Jan;26(1):16-7.