Cost-effectiveness Analysis Archives

Expanding Horizons: Capturing the Full Societal Value of Healthcare Interventions
Healthcare interventions have traditionally been assessed primarily within the confines of the healthcare system. However, the ripple effects of these interventions extend far beyond, influencing various aspects of society, including economic productivity, educational attainment, and overall societal well-being. Recognizing and quantifying these broader impacts is crucial for a more comprehensive evaluation of healthcare interventions, leading to more informed decision-making and optimal resource allocation. Achieving this requires not only refined analytical methods but also stronger intersectoral collaborations, fostering coordinated efforts between healthcare, education, employment, social welfare, and economic sectors to ensure that data, insights, and strategies are shared across domains for maximum societal benefit.[1]

The role of health in human capital cannot be overstated. A healthy population exhibits higher productivity, increased labor force participation, and enhanced income generation. This results in higher tax revenues and greater government spending on social programs, emphasizing the critical importance of healthcare interventions in fostering economic prosperity and societal well-being.[1]

Health technology assessments (HTAs) are essential in determining the value of healthcare interventions. They assess the benefits and costs of new technologies, including pharmaceuticals, medical devices, and procedures, with the aim of informing decisions about their use and reimbursement. However, traditional HTA approaches often overlook significant benefits or harms associated with healthcare interventions that extend beyond the healthcare system itself. For instance, a treatment that reduces disability and improves productivity among patients can generate economic gains by reducing long-term disability costs, stimulating job creation, and fostering economic growth in related sectors. Yet, these broader economic impacts frequently escape the traditional HTA analysis framework. Neglecting such economic impacts in HTA evaluations may undervalue the true worth of interventions and hinder informed decision-making.[2-4]

To address these limitations, a conceptual framework has been introduced to estimate and reward the broader value of healthcare interventions. This framework employs a multifaceted approach, acknowledging direct health benefits and indirect effects on sectors like education, employment, and social services. Crucially, this framework emphasizes intersectoral collaboration, enabling healthcare decision-makers to work closely with stakeholders from education, labor, and social service sectors to capture the full range of impacts healthcare interventions generate. Such collaboration ensures that the societal value of interventions is fully understood and appropriately weighted.[5]

It integrates conventional cost-effectiveness analysis, macroeconomic methods, and a voting scheme to capture and evaluate the broader economic and societal impacts of healthcare interventions. Incorporating patient-centred value assessments within this framework can further enhance its comprehensiveness by capturing outcomes that matter most to patients, such as improvements in quality of life, functional independence, and satisfaction with care. Patient-reported outcomes, patient preferences, and lived experiences offer invaluable insights that ground this broader evaluation framework in the real-world priorities of the individuals it seeks to serve.

The distributional cost-effectiveness analysis (DCEA), which is considered as a patient-centric and equitable CEA, assesses how healthcare interventions affect different population subgroups and their equitable distribution of outcomes. While DCEA enhances HTA by considering patient-centered outcomes and equity, it stops short of capturing the broader societal impacts of health technologies. Therefore, it is recommended to augment DCEA with macroeconomic analysis to comprehensively assess the societal value of healthcare interventions.Incorporating patient-centered value assessment into DCEA would further enhance its utility by integrating both equity and personal value perspectives, ensuring that interventions deliver meaningful benefits across diverse population groups. [6]

The input-output model is a macroeconomic analysis tool traditionally used and can be effectively utilized to evaluate the extensive economic effects of healthcare interventions. This model helps to map out how different sectors of the economy interact and shows the far-reaching impact of healthcare beyond its immediate environment. By fostering intersectoral collaboration between healthcare economists, labor economists, and policymakers, input-output modeling can become even more effective in capturing cross-sector impacts, such as changes in workforce participation, educational attainment, and social service utilization. This quantitative model advocates for a holistic appreciation of healthcare’s contributions, advocating for policies that recognize and reward the full spectrum of impacts across the economy.[7]

Ali et al. (2024) introduced a structured voting scheme to guide intervention decisions by weighing health benefits against broader impacts. A voting scheme guides intervention adoption decisions, balancing health benefits against broader impacts. It categorizes interventions into four quadrants based on net health and broader impact balances, aiding decision-makers in prioritizing interventions that maximize societal benefits. Quadrant I, the ideal choice, (positive net health benefits + net broader impact) represents interventions that provide the highest value for money, actively sought after by organizations and individuals striving to maximize the impact of their allocated resources. Quadrant II (positive net health benefits + negative net broader impact), these interventions are pursued when the augmented net health benefits outweigh the negative broader impacts, justifying the investment in the intervention. Conversely, Quadrant III (negative net health benefit + negative net broader impact) interventions are deemed inadequate choices, as they engender detrimental outcomes. Options within this quadrant should be unequivocally rejected due to their deleterious effects. Lastly, Quadrant IV (negative net health benefit + positive net broader impact) includes interventions that may be pursued when the positive societal impacts outweigh the negative augmented net health benefits [5].

The voting scheme is designed to integrate interdisciplinary perspectives from multiple stakeholders, including patients, healthcare providers, policymakers, researchers, advocacy groups, payors, and community representatives, ensuring a balanced and inclusive approach to decision-making. The votes are cast to determine the adoption of interventions, facilitating a democratic and transparent process, where the diverse values and priorities of the community are reflected in the final choices.

For example, Reset-O, a US FDA-approved prescription digital therapeutic for opioid use disorder, has been shown to not only improve patient outcomes by reducing opioid use, but also to generate broader societal benefits, such as reducing health inequity and improving employment rates among treated individuals. This highlights the importance of evaluating both direct health benefits and wider economic and social outcomes when assessing healthcare interventions.[5] Similarly, large-scale vaccination programs have demonstrated positive impacts far beyond healthcare, improving school attendance rates, enhancing labor productivity, and reducing household economic vulnerability. Comprehensive mental health programs have similarly shown to improve employment retention, reduce criminal justice system involvement, and strengthen family stability. Such practical examples highlight the importance of evaluating both direct health benefits and wider economic and social outcomes when assessing healthcare interventions.[5]

In conclusion, broadening the evaluation of healthcare interventions to encompass impacts beyond the healthcare sector is essential for a comprehensive understanding of their true value. By integrating frameworks like DCEA, input-output models, and inclusive voting schemes, we can better capture the extensive economic and societal benefits these interventions offer. This holistic approach not only promotes more informed decision-making but also ensures that the contributions of healthcare interventions are fully recognized and rewarded, ultimately leading to enhanced well-being and economic growth across society.

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References:
1. Bleakley H. Health, human capital, and development. Annu. Rev. Econ.. 2010 Sep 4;2(1):283-310.
2. Goodman CS. HTA 101 Introduction to health technology assessment. 2014.
3. Richardson J, Schlander M. Health technology assessment (HTA) and economic evaluation: efficiency or fairness first. Journal of market access & health policy. 2019 Jan;7(1):1557981.
4. Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. The European Journal of Health Economics. 2018 Jan;19:123-52.
5. Ali AA, Kulkarni A, Bhattacharjee S, Diaby V. Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework. PharmacoEconomics. 2024 May 17:1-4.
6. Asaria M, Griffin S, Cookson R. Distributional cost-effectiveness analysis: a tutorial. Medical Decision Making. 2016 Jan;36(1):8-19.
7. Leontief W. National economic planning: methods and problems. Challenge. 1976 Jul 1;19(3):6-11.
MarksMan Healthcare

March 4, 2025

Cost-effectiveness Analysis, Healthcare intervention, HTA, Societal Impacts
When is the Ideal Moment to Start HEOR Activities Within Product Development?
Health Economics and Outcomes Research (HEOR) has grown into a vital domain in the health and care industry. It plays a key role by offering vital insights into how drugs in the market affect both health and finances. With the means of data driven and evidence-based insights, it plays a critical role in the development and dissemination of new healthcare products by providing important findings regarding clinical development strategies, informing regulatory decisions, and supporting market access. Medical affairs teams in pharmaceutical companies are often concerned about the ideal time for beginning HEOR activities within the product development cycle. The simple answer to this question is: the earlier, the better.[1]

One can consider engaging in HEOR activities as early as the preclinical or discovery phases of product development. This way, HEOR insights can contribute significantly to the foundational understanding of the economic and clinical landscape. It has been observed that early HEOR involvement can result in more informed decision-making, and influence stakeholder engagement and investment decisions. HEOR helps identify and address potential market access barriers. By understanding the economic landscape and the outcomes that matter most to payers and patients, developers can tailor their products to meet these needs.[2]

HEOR activities can further strengthen evidence generation in Phase I and II clinical trials. This period is considered critical for gathering preliminary data on safety, efficacy, and health-related quality of life (HRQoL) outcomes. Complementing this data with observations from HEOR studies can help refine economic models and identify the most relevant clinical endpoints. Additionally, HEOR data in early phases can be very useful to inform trial design in Phase III studies, ensuring that all necessary economic and QOL endpoints are captured, to support future health technology assessments (HTAs).[3]

Advancing to Phase III clinical trials marks a very important milestone in the development of a new pharmaceutical product. At this stage, HEOR has a critical role of providing comprehensive data and evidence needed to confirm the product’s value proposition in the market. This is the phase where the most extensive and rigorous data on efficacy, safety, and outcomes are collected. HEOR activities in Phase III should define clinical endpoints, finalize economic models, undertake full cost-effectiveness analyses, and prepare dossiers required for regulatory submission and reimbursement applications. Embedding HEOR insights into Phase III activities can help predict and prevent potential concerns from regulatory agencies and payers. It showcases the value of the product not only through its clinical efficacy but also through the economic and patient-centered outcomes.[4,5]

Following Phase III and the acquisition of marketing authorization, the product is launched in the market and becomes available for prescription to patients in the real-world setting. Throughout this stage, the accumulating real-world evidence (RWE) further helps in refining the value of the product by evaluating the efficacy and performance of the product in the diverse patient population. This evidence further substantiates the ongoing research by providing invaluable and robust insights into long-term outcomes, adherence patterns, and overall economic impact of the product. Continuous HEOR efforts post-launch help to support ongoing market access, reimbursement renewals, and potential label expansions. They also provide feedback that can be used to optimize the product’s use and improve patient outcomes over time.[5]

In summary, HEOR is one continuous process that evolves over the whole cycle of product development. Early initiation of HEOR activities during the development process embeds economic and patient outcome considerations within the strategy of the product from the very beginning. This method not only increases the chances of a drug receiving the appropriate regulatory and reimbursement approval but also guarantees that new therapies are beneficial to both the patient and the medical system. Such proactive HEOR strategy, will put the pharmaceutical companies in a position to better navigate the complexities associated with market access, and to ensure that innovative therapies reach the patients who deserve them most.

Become A Certified HEOR Professional – Enrol yourself here!

References:
1. Holtorf AP, Brixner D, Bellows B, Keskinaslan A, Dye J, Oderda G. Current and future use of HEOR data in healthcare decision-making in the United States and in emerging markets. American health & drug benefits. 2012 Nov;5(7):428.
2. Van Nooten F, Holmstrom S, Green J, Wiklund I, Odeyemi IA, Wilcox TK. Health economics and outcomes research within drug development: challenges and opportunities for reimbursement and market access within biopharma research. Drug discovery today. 2012 Jun 1;17(11-12):615-22.
3. Zou KH, Baker CL, Cappelleri JC, Chambers RB. Data Sources for Health Economics and Outcomes Research. InStatistical Topics in Health Economics and Outcomes Research 2017 Nov 22 (pp. 1-13). Chapman and Hall/CRC.
4. Garrison Jr LP, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real‐world data for coverage and payment decisions: the ISPOR real‐world data task force report. Value in health. 2007 Sep;10(5):326-35.
5. Chou JW, Portelli A, Cournoyer A. De-risking Market Launch: Leveraging HEOR Evidence Planning to Support Informed Payer Decisions. J Clin Pathways. 2022;8(8):63-65.
MarksMan Healthcare

August 2, 2024

Cost-effectiveness Analysis, HEOR, Market Access, Product Life Cycle, RWE
Modelling Health Inequalities in HEOR: A Path to Equitable Healthcare
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.

Become A Certified HEOR Professional – Enrol yourself here!

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.
MarksMan Healthcare

May 27, 2024

Cost-effectiveness Analysis, Health inequalities, HEOR, Predictive analysis