research on health economics

Health Economics

• Living reference work entry
• First Online: 01 April 2023
• Cite this living reference work entry

• Faris El-Dahiyat 2 ,
• Daneh Obaid 2 &
• Ghaleb El Refae 3  

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Health Economics is concerned with the problem of allocating healthcare resources in case of scarcity and uncertainty. This includes both resource allocation to the healthcare system and to different activities and individuals within the healthcare system. As such, this entry sheds light on Health Economics as a tool to be used by different decision-makers to use limited resources efficiently. Furthermore, this entry discusses the concepts, principles, importance, and applications such as formulary management, cost saving, and Covid-19 pandemic. In addition, the entry explores challenges and future direction of Health Economics.

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College of Pharmacy, Al Ain University, Al Ain, UAE

Faris El-Dahiyat & Daneh Obaid

College of Business, Al Ain University, Al Ain, UAE

Ghaleb El Refae

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National Institute for Health and Care Excellence, London, United Kingdom

Dalia Dawoud

Department of Pharmacy, University of Huddersfield, Huddersfield, United Kingdom

Zaheer-Ud-Din Babar

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El-Dahiyat, F., Obaid, D., El Refae, G. (2023). Health Economics. In: Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. Springer, Cham. https://doi.org/10.1007/978-3-030-50247-8_8-1

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DOI : https://doi.org/10.1007/978-3-030-50247-8_8-1

Received : 01 October 2022

Accepted : 01 October 2022

Published : 01 April 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-50247-8

Online ISBN : 978-3-030-50247-8

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Applied Health Economics for Public Health Practice and Research

Professor of Health Economics

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Public health has been described as the organized activities of society to improve and protect the health of the population. Health economics applied to public health is the study of how we allocate our scarce societal resources to meet our public health wants and needs in the best way possible. This book presents current thinking on health economics methodology and application to the evaluation of public health interventions (PHIs). It is for health economists working in higher education and public healthcare systems, challenged with the economic evaluation of PHIs, when they have been used to applying health economics and the methods of economic evaluation to narrower clinical interventions in primary or secondary care settings. This book will also be of interest to public health practitioners wanting to incorporate health economics into their daily work. This book covers the history of economics of public health and the economic rationale for government investment in prevention; principles of health economics including scarcity, choice, and opportunity cost; evidence synthesis; key methods of economic evaluation with accompanying case studies; economic modelling of public health interventions; return on investment analysis with national and international case studies; and application of programme budgeting and marginal analysis (PBMA) to the prioritization of PHIs. It concludes with priorities for research in the field of public health economics, spanning an acknowledgement of the role played by the natural environment in promoting better health, through to precision public health, recognizing the role of genetics, the environment, and socioeconomic status in determining population health.

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Health Economics and Outcomes Research

• March 14, 2022

Table of Contents

What is health economics and outcomes research.

Over the last few decades, the field of health economics and outcomes research (HEOR) has emerged to fill a perceptible knowledge gap in the healthcare field. 

While clinical trials provide a robust “gold standard” for data on potential medicines and therapies, there has been an absence of real-world data about how medical treatments work and remain effective in the real-world setting. Amidst discussions between consumers, payers, healthcare providers, and pharmaceutical companies, health economics outcomes research has evolved as the trusted discipline to fill this gap. 

With HEOR data, regulating agencies are evolving their viewpoints to value and even expect this patient-centered data to inform their discussions and decisions, rather than relying on clinical trials alone.

HEOR ensures that the most reliable real-world evidence from healthcare consumers is used to select healthcare treatment options. With vast access to metadata on all aspects of healthcare, the HEOR discipline aims to extract the most relevant data to evaluate probing questions about healthcare, values, and decision-making. 

Beyond drug efficacy, HEOR explores how medication and therapies affect the lives of patients -including the cost burden of treatment, cost benefits, and impact on quality of life. HEOR can help bring to light the differentiation of and value differences between treatment options. 

HEOR studies harvest data from sources such as medical records, insurance claims, and patient surveys in order to explain how these medicines are performing across the globe. 

ISPOR provides the following answers to these basic questions about HEOR:

What Is Health Economics?

Health economics focuses on measuring and valuing the outcomes of healthcare interventions.

What Is Outcomes Research?

Outcomes research comprises a set of scientific disciplines that evaluate the effect of healthcare interventions on patients.

What is HEOR?

Health economics and outcomes research (HEOR) is the confluence of two fields that work together to provide powerful data and insights for healthcare decision-makers. HEOR scientists and analysts provide scientific research using real-world data, analytic tools, economic models, cost-benefit analysis, and other proven methodologies.

What questions does Health Economics Outcomes Research answer?

HEOR uses real-world data to provide answers to questions such as:

• What is the ongoing efficacy, safety, and overall benefit of this treatment?
• How does this treatment compare with its competitors?
• What is the cost burden to the consumer?
• What are the short and long-term improvements to patients’ quality of life? For example, on measures such as the Quality of Life Scale (QOLS) . 
• What is the economic value of this treatment?
• How does this treatment impact health outcomes? 

What is HEOR data used for?

As detailed by the NIH , HEOR data is used to help healthcare payers determine if treatments work in the populations they serve, and how much of the drug or treatment cost should be reimbursed by the healthcare system. 

HEOR data may be used to:

• Promote the expansion of a label claim with the FDA.
• Create a Health Technology Assessment to provide health professionals, payers, and policymakers with information on the values and benefits of a given treatment. 
• Generate data to fill an evidence gap to inform the marketing of a product.
• Provide a longitudinal analysis, offering a deeper probe into predictors of diseases and other more complex analyses of disease trends. 

What does HEOR research do?

HEOR includes the following types of research studies:

• Cost-effectiveness analysis
• Cost burden
• Economic models
• Evidence generation planning
• Clinical/claims database analyses
• Retrospective chart reviews
• Patient-centered outcomes research
• Systematic literature review

Who are the collaborative players that work within HEOR?

Collaborators include healthcare providers, payers, pharmaceutical companies, pharmaceutical manufacturers, medical device manufacturers, regulators, nonprofit membership organizations, healthcare professionals, patients, and caregivers. 

What is Real World Evidence in HEOR?

Real-world evidence in HEOR involves using real-world data to scientifically evaluate the effectiveness of treatments among patient users. 

Whereas in the recent past, clinical trial data has been used to evaluate approval of treatment, HEOR as a discipline has introduced robust research and modeling tools to the healthcare landscape. The US Food and Drug Administration (FDA) now boasts that “Real-world data (RWD) and real-world evidence (RWE) are playing an increasing role in health care decisions” and the FDA uses RWD and RWE to monitor postmarket safety and adverse events along with making regulatory decisions. 

What is Patient-Centered Outcomes Research? 

HEOR utilizes patient-centered outcomes research to capture qualitative data about how drugs are working in the lives of their users. 

• Patient-reported outcomes (PRO) include the status of a patient’s health condition or behavior that comes directly from the patient. These include patients’ reports of their health and experience with care.
• Patient-reported outcomes measures (PROM) are quality measures derived from outcomes reported by patients. Instruments to collect them include surveys and questionnaires, and can be collected through mobile apps or other devices.  

What are current HEOR Trends?

• Real-world evidence : Now that pharmaceutical regulators such as the US FDA have invited the use of real-world evidence into discussions about coverage decisions, the role of HEOR has been up-leveled. With the passage of the 21st Century Cures Act (2016), real-world data can be used to support the approval of new drug indications. HEOR departments now have an increasingly important role to play in post-market coverage decisions and FDA label expansion.
• Evolving technology to better capture data : The rapid proliferation of technology (fit bits, wearables, and other devices) to capture health data has transformed HEOR. The ability to accurately capture copious amounts of data has given HEOR scientists the leverage to ask probing questions to inform pharma drug development. Data can be harvested and analyzed to inform the design of a clinical trial or to support market access strategies for an existing treatment. 
• Patient-centered outcomes research: Regulators have also welcomed the use of data incorporating the “patient voice” where patients and caregivers can share their symptoms, struggles, improvements, and unique experiences to be used in HEOR studies. HEOR scientists, healthcare professionals, and other industry experts create Patient-Reported Outcomes (PRO) tools that can be used to measure health-related quality of life.  
• Value assessment
• Health equity: Addressing disparities in healthcare
• Healthcare financing: Funding future health technologies
• Patient engagement
• Drug and healthcare pricing
• Public Health: Focusing on key priorities
• Health Technology Assessment: Supporting cross-country HTA cooperation
• Health Data: Addressing infrastructure and interoperability
• Artificial Intelligence: Leveraging AI and advanced analytics

Suggested video to embed on real-world evidence (we should make our own)

What are evidence gaps and what is evidence generation in HEOR? 

Evidence gaps are areas where a pharmaceutical company needs additional analysis to provide evidence for the need for a new drug. An evidence gap analysis can take place before product launch and continue to be a useful tool to support the ongoing success of that asset.

The evidence generation plan is then developed to address this gap analysis and uses HEOR studies, analytics, and planned communications to fill evidence gaps. 

What is Value Assessment in HEOR?

Value assessment helps to define the value of treatment beyond a traditional cost assessment. Value assessments in HEOR can include the patient-reported outcomes and how patients would value the benefits of a given treatment. Value assessment can also include the perspective of other groups such as healthcare providers, payers, caregivers, or other stakeholders. Value assessment also can take into consideration cost per quality-adjusted life-year (QALY) along with cost-effectiveness analyses (CEA). 

HEOR and the Increased Volume of Health Data

The HEOR discipline is racing to keep up with the vast amount of health data that is being produced globally. According to RBC Capital Markets , about 30% of the world’s data is being generated by the healthcare industry. These experts say that by 2025, the compound annual growth rate of data for healthcare will reach 36%. HEOR seeks to harvest and utilize enormous amounts of data– to make it usable by the public, by payers and healthcare providers, and to make these analyses relevant to the healthcare discussions. 

FDA & ISPOR in HEOR

While clinical trials provide initial evidence to provide efficacy and safety of a treatment, with increasing access to treatment options and increasing availability to real-time data, healthcare professionals and payers are now expecting more. The industry is also growing and evolving rapidly. According to Expert Market Report’s latest report, ‘Global Health Economics and Outcomes Research (HEOR) Market Report and Forecast 2021-2026’, the global health economics and outcomes research (HEOR) industry attained a value of USD $1.23 billion in 2020. The market is further expected to grow in the forecast period of 2021-2026 at a compound annual growth rate (CAGR) of 12.5% to reach USD $2.46 billion by 2026.

What is Health Technology Assessment (HTA)?

Health Technology Assessment (HTA) is a tool to aid reimbursement decisions and discuss how the pricing of health interventions compares to the benefits that these treatments provide to patients. The HTA process provides a climate in which HEOR can utilize all of its tools to illustrate treatment value. These tools can highlight the treatment’s value to society, to the health of patients, to the economics of an individual, or to a community. 

Patient-Reported Outcomes (PRO) data can highlight personal values and experiences of patients that had previously been neglected or deemed “unmeasurable.”

How is Artificial Intelligence (AI) used in HEOR?

HEOR scientists are constantly looking at ways to utilize artificial intelligence (AI) in research. HEOR studies utilize real-world data, and AI can be used to improve how data is collected, harvested, and utilized for HEOR studies. This can help capture patient-reported outcomes and can also be used to predict possible outcomes in treatments. 

How does HEOR fit in and inform on health care delivery?

HEOR ensures that when healthcare decisions are made, decision-makers (i.e., payers, healthcare providers, regulators, physicians, and other collaborators) take into consideration the different elements of “value” that together illustrate the complete benefit of the drug. 

The value flower has emerged as a visual that references these values that HEOR recognizes including:

• equity, 
• quality-adjusted life-years (QALYs), 
• net costs, 
• productivity, 
• family spillovers, 
• the value of knowing, 
• insurance value: financial & health, 
• fear of contagion and disease, 
• the severity of disease, 
• the value of hope, 
• real option-value, and 
• scientific spillovers. 

HEOR and Market Access

HEOR as a discipline differs from Market Access, even though these roles can work together in pharmaceutical companies on cross-functional teams. HEOR is concerned with illustrating the value of a given treatment or therapy to help determine drug pricing. 

Market access, on the other hand, works to ensure all patients that can get access to appropriate therapies, do gain access, and at the correct price. Research firms with expertise in market access can translate study findings into credible real-world evidence that directly supports specific marketing messages. This work requires both scholarly analysis and communications expertise and can result in peer-reviewed publications to facilitate appropriate product positioning and update. 

What is the future of HEOR?

HEOR as a field has greatly evolved and matured as it has proven its ability to utilize data – data to transform the healthcare landscape and decision-making process. As tools to capture data improve and become more precise, and as the volume of metadata increases, the field of HEOR is primed for ever greater involvement and leadership in this process. 

HEOR must rise to the challenge to keep up with the formidable speed at which health-related data is expanding. “Real-world evidence” and “real-world data” in “real-time” are a constant focal point for HEOR research. Making the data meaningful and relevant is the intent. And propagating study designs that integrate the patient voice is at the forefront of this growth.

The challenges and possibilities of using data to transform the healthcare landscape are central to the evolution of HEOR.

What are career opportunities in HEOR? 

HEOR is a rapidly growing field with a variety of career paths and positions within the discipline. HEOR careers often require scientific and technical background – and may require a doctorate in health economics, a Doctor of Pharmacy degree (PharmD), Master’s degree in Statistics or Mathematics, other degrees in clinical research, or other programs in cost-benefit analysis or healthcare analytics. Some pharmaceutical companies and research firms offer internship and fellowship opportunities, often open to upcoming graduates of HEOR/PharmD programs. 

Health Analytics bridges the gap between sound scientific data and market access. Phone: (410) 997-3314 Email: [email protected]

• Health Care
• Evidence Generation Planning
• Evidence Generation
• Translating to Market Access

• Open access
• Published: 12 May 2023

Global trends in the scientific research of the health economics: a bibliometric analysis from 1975 to 2022

• Liliana Barbu   ORCID: orcid.org/0000-0003-0641-7483 1  

Health Economics Review volume  13 , Article number:  31 ( 2023 ) Cite this article

6808 Accesses

2 Citations

18 Altmetric

Metrics details

Health science is evolving extremely rapidly at worldwide level. There is a large volume of articles about health economics that are published each year. The main purpose of this research is to explore health economics in the world's scholarly literature based on a scient metric analysis to outline the evolution of research in the field.

The Web of Science repository was used to get the data (1975–2022). The study explores 1620 documents from health economics. CiteSpace software was used to provide network visualisations. Four thousand ninety-six authors, 1723 institutions, 847 journals and 82 countries were involved in the sample. The current research contains a descriptive analysis, a co-authorship analysis, a co-citation analysis, and a co-occurrence analysis in health economics.

Drummond M.F (author), the USA (country), University of London (institution) and Value Health (journal) are among the most important contributors to the health economics literature. Co-authorship analysis highlights that cooperation between authors, institutions and countries is weak. However, Drummond M.F. is the most collaborative author, the USA is the most collaborative country, and University of York is the most collaborative institution. The study offers an image about the most co-cited references (Arrow K.J., 1963), authors (Margolis H.) and journals (British Medical Journal). The current research hotspots in health economics are “behavioural economics” and “economic evaluation”. The main findings should be interpreted in accordance with the selection strategy used in this paper.

All in all, the paper maps the literature on health economics and may be used for future research.

Introduction

The health economy is a branch of the economy that deals with concerns of the production and consumption of health services and healthcare that relate to efficiency, effectiveness, value, and behaviour. Applying economic ideas, concepts, and methods to institutions, actors, and activities that have an impact on people's health is known as health economics [ 1 ]. The health economy is studying how to allocate limited resources to meet human desires in the medical industry and disease care. The health economy often tries to meet the most pressing challenges facing the health system. Studies in health economics provide to decision-makers precious information about the effective use of resources that are available to maximize health benefits.

The health economics is a component of public health, a component that It can be used to examine health issues and medical treatment. Health economists consider the origin of their discipline to Petty W. (1623–1687) [ 2 ] who propose valuation of human life based on a person’s contribution to national production. Arrow K. is credited with creating the field of health economics in a work where he conceptually distinguished between health and other goods [ 3 ]. Since Arrow K.'s fundamental publication on health economics from 1963, the scale of the healthcare sector, the share of public budgets allocated to healthcare, and the body of research on health economics have all increased significantly [ 4 ].

The current pandemic context has proved the need for a functioning public health system capable of meeting any challenges. The World Health Organization report for 2020 presents an examination of 190 nations' global health spending from 2000 to 2018. The report shows that global health spending has increased consistently between 2000 and 2018, reaching $ 8.3 trillion, or 10% of world GDP [ 5 ]. At the level of OECD Member States, the latest estimates show an average increase in health spending of about 3.3% in 2019, whereas health spending as a percentage of GDP stayed about where it had been in prior years, at 8.8% [ 6 ]. These indicators rose sharply in 2020, as economies faced a pandemic. The increases were driven by an increase in the level of allocation of government resources for health, while private spending on health tended to decline. At EU level, the public sector plays a major role in funding health services. In 2/3 of Member States, more than 70% of health spending is funded by the public sector [ 7 ]. In 2020, the EU's overall public health spending was €1.073 billion, or 8.0% of GDP ( https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Government_expenditure_on_health ). For governments, public spending on health is one of the spending categories with the quickest growth.

Health economics is the application of economic theory, models, and empirical techniques to the analysis of decision-making by individuals, health care providers, and governments regarding health and health care. Even though the methodologies are distinct in terms of health care, health economics aims to apply the same analytical tools that would be applied to any good or service that the economy provides [ 8 ]. By offering a clear framework for decision-making based on the efficiency principle, health economics seeks to simplify decision-making [ 9 ]. Extensive government interference, insoluble uncertainty in many dimensions, asymmetric knowledge, barriers to entry, externality, and the presence of a third-party agent are all characteristics that set health economics apart from other fields [ 10 ].

Health economics is the field were interdisciplinarity bring additional value for society. Health economics development has not been without controversy. Health economics refers to a variety of elements that interact to affect the expenses and spending of the healthcare sector. Its controversy rises from the roles of people, healthcare providers, insurers, governmental bodies, and private companies in influencing the healthcare sector expenses. The parties that interact in this field have some conflicting goals. On the one hand, health care policymakers and public hospitals have as objective to provide real value to the patients, to balance public interest and economic restrictions. On the other hand, private hospitals, insurance companies aim to obtain profit for their shareholders. There are several weaknesses that should be rectified in the future. Among weaknesses it can be found deficiencies in the supply of health economists [ 11 ], a lack of financial resource independence between the local and central levels, the key macroeconomic variables' unfavourable behaviour, and the difficulty in developing new financing alternatives [ 12 ]. In addition to having too close relationships to national institutions and sponsors of health economics research, health economics also has excessively loose connections with general economic theory [ 13 ]. Considering increased demands in healthcare services and limited health care budgets, health economics faces real challenges in providing decision making frameworks and there will always be challenging healthcare decisions. Although it has not always been an impartial instrument, health economics does give useful information for policy [ 14 ]. Regarding how well economics integrates with promoting health, there is scepticism, and public health has mixed feelings on the subject. Health economics has been accused of focusing more on the consumption of healthcare services than the creation of healthcare [ 15 ]. Despite several methodological limitations, health economics can provide helpful concepts and principles that aid in comprehending the effects of resource allocation decisions [ 9 ]. All practitioners must have a elementary comprehension of some economic concepts to both understand the helpful ideas the field may provide and recognize its inadequacies.

The main purpose of the research is to examine the health economics literature published worldwide based on a scient metric analysis to outline the development of the field's research. The existence of a multitude of articles published on health economics determines the need to address and measure it quantitatively. Such an analysis is justified by the need must be aware of the current trends and future directions of research in the field of health economics. Health science is evolving extremely rapidly at worldwide level. There is a large volume of articles about health economics that are published each year. Another argument is that there several computer programs which allows for scient metric analysis of health economics publications. This article contributes to the bibliometric literature on health economics by offering answers to the subsequent research inquiries: How scientific production has evolved in health economics? Who are the most important authors and publications in health economics? What are the geographical and institutional hubs of knowledge production in health economics? What kind of collaboration between authors, organizations, and nations are there in the field of health economics research? Which are the most cited authors and the most cited papers, and which are the most attractive journals for publishing research results in health economics? What are the most debated conceptual approaches in health economics?

The remainder of the paper is structured as follows. The second section introduces a short literature review. Research methodology and data collection are presented in Sect. 3. Section 4 contains the quantitative and qualitative scient metric analysis on health economics by using CiteSpace software (descriptive analysis, collaboration analysis, co-citation analysis and keywords co-occurrence analysis). The last part concludes the analysis, presents the research limitations, and describes future directions of research.

Literature background

Although there are thousands of articles published on health economics, very few articles aim for bibliometric analysis of the field and use computer programs. A first article published by Rubin, R. M. and Chang, C. F. (2003) aims at the study of 5,545 indexed articles, in the period 1991–2000, in the EconLit database, in the Health Economics section [ 16 ]. The second study is published by Wagstaff, A. and Culyer, A. J. in 2012 and extends the previous bibliometric research done by Rubin and Chang also based on the articles indexed in EconLit on health, over 40 years [ 17 ]. The third study, published by Moral-Munoz J.A et all in 2020, focuses on articles indexed in the Web of Science, between 2010 and 2019, which have the word "health" and do not use scientometric software [ 18 ].

It would be worth mentioning a descriptive analysis of the field conducted by Jakovljevic M. and Pejcic A. in 2017, but without the use of bibliometric indicators. The authors quantitatively analyze health economics publications by querying the PubMed, Scopus, WoS and NHS economic evaluation Database between 2000 and 2016 and conclude with the existence of an upward flow of health economics publications [ 19 ]. In this context, the proposed research is characterized by focusing on WoS articles that refer strictly to "health economics" and their computer processing to obtain maps and connections between studies.

Research methodology

Research methods.

In the current paper two research methods were used: bibliometric analysis and knowledge mapping. Regarding the first one, it should be mentioned that bibliometric research methods are used delivering quantitative analysis of textual works, in this case publications about health economics. This method allows bibliographic overviews of scientific production in the field. In the scientific community, the technique is increasingly employed to provide details regarding relationships between various groups [ 20 ]. Bibliometric analysis uses statistical tools and different metrics as part of the analysis (frequency/ count, co-citation, co-authorship, co-occurrence, betweenness centrality, citation burst, modularity, centrality, sigma, Silhouette etc.). Bibliometric analysis naturally presents itself as a tool to qualify, then quantify, the study conducted [ 21 ].

Regarding the second one, bibliometric analysis uses a large quantity of information that should be transformed in knowledge. This is done by using data visualization and knowledge maps. An enormous and complex collection of knowledge resources can be more easily accessed and navigated by using knowledge mapping strategies [ 22 ]. Knowledge mapping is the process of making knowledge maps, it makes explicit knowledge graphic and visual. Knowledge maps are static, they are a “snapshot in time” that aids in understanding and organizing knowledge flow for researchers [ 23 ]. A process, method, or instrument called “knowledge mapping” is used to analyse knowledge to find traits or meanings and perceive knowledge in an understandable and transparent way [ 24 ]. One of the advantages of knowledge mapping includes the freedom to combine without restriction, i.e., without restrictions on the number of connections and concepts that can be established [ 25 ].

Data source and search strategy

For this analysis we decided to use one of the most reliable databases: Web of Science (WoS) because it contains a data for large period. The data was retrieved from the Web of Science Core Collection by using title search tool TI = (health economics). The primary literature data were downloaded on 7th of October 2022. The query objective was to integrate in this analysis all research papers related to health economics. We did not introduce any restrictions regarding the topic or time span for searching documents. We intend to have a comprehensive view of the research area and to see its evolution over time. As a result, 2340 documents were retrieved. Among publications about health economics, the most numerous documents are the articles (37.6%), followed by editor materials (19.8%), meeting abstracts (13.9%) and book reviews (13.3%). There are also review articles on the subject, proceeding papers, letters, books, and book chapters which were kept in the sample. The other types of documents were removed resulting a sample of 2305 publications. The language of almost all publications is English (91.4%), followed by German (4.3%). The percentage of publications produced in other languages, such as French, Spanish, Portuguese, Russian etc. is less than 1.5% for each of them. Publications in other languages than English were eliminated, remaining 2108 documents in the sample.

The next step is to identify and remove duplicates by using Excel function (Conditional Formatting – Highlight Duplicate Values), therefore 8 duplicates were removed. In the sample under analysis, a multitude of types of documents indexed in WoS and referring to the concept of health economics can be observed. During the step of checking for duplications, it was found that there are too many duplicates of documents’ title, most of them due to editorial materials or book reviews. This led to a thorough analysis of publication by type of document (eg there are more than 10 reviews for one book or more than 10 editorial materials signed by the same editor). We identified some publications which are irrelevant for the purpose of our analysis. One hundred eighty-six editorial materials without citations and all 286 book reviews were removed resulting 1628 publications. We kept the editorial materials with citation because some of them have more than 100 citations. We searched for anonymous publications, more exactly we looked for incomplete data (author’s name is missing) and we removed 8 documents.

For the remaining documents the "Full Record and Cited References" was downloaded on 13th of October 2022 (txt files) and used as original data for the proposed bibliometrics analysis and science mapping. The final data collection, which consists of 1620 publications, is supported by 16,755 citing articles (excluding self-citations) and has been cited 18,504 times (excluding self-citations), giving it an H-index of 59. The data are statistical analysed by using annual distribution of publications, authors, journals. Co-authorship analysis focuses on collaboration between authors, institutions, and countries. Cited references, cited authors, and cited journal are used in co-citation analyses, and finally, the co-occurrence will integrate keyword in this research.

The graphical representation of selection procedure can be seen in Fig.  1 .

Selection procedure flow chart. Source: Authors

Visualization tools

Bibliometric method needs a certain amount of data to be statistically credible. This is the reason for that computerized data treatment is needed. Moreover, databases contain hundreds or thousands of entries which are analysed by using computer software. There is many bibliometric software, each of them has particularities and weaknesses. CiteSpace was chosen in this study because it is very user friendly, intuitively, and easy to use. CiteSpace 6.1.R2. available for free download at https://citespace.podia.com . A variety of networks created from scientific publications, such as collaboration networks, author co-citation networks, and document co-citation networks, are supported by structural and temporal analysis in CiteSpace. CiteSpace can produce knowledge domain X-rays. The CiteSpace parameters for this investigation were as follows: time-slicing was from 1975 to 2022, years per slice was 1 year, Look Back Years (LBY) = -1, Link Retaining Factor (LRF) = -1. For text processing and links, we preserved the default settings. We used several nodes (authors, institutions, journal, references, keywords) and metrics (such as citation burstiness, Sigma, Silhouette, rad Q, betweenness centrality) depending on the study that was done. Top N% is set to be equal to 100%, Top N is set to be 50, and g-index is set to be 25.

Statistical analysis

The first step to follow in the scient metric analysis is to analyse the evolution of publications’ number in the researched field. The way in which they are distributed over the years indicates the attention that the field of health economics has benefited from and the speed at which its conceptual development took place. The first 3 papers about health economics were published in 1975, indicating the lowest number of annual publications, but also a concept that has existed for over 4 decades. From Fig.  2 , a general upward trend of health economics publications can be observed, but with numerous upward and downward fluctuations, generating sinusoidal cycles with an average duration of 3–4 years. The period 1975 – 1986 is characterized by a very low number of publications, 98 publications written by 110 authors in 12 years, representing 6% of the total sample. The next two decades (1987 – 2006) are characterized by a slightly increasing trend in the number of publications, with an annual average of approximately 23 publications on health economics, reaching a total of 454 publications written by 826 authors and representing 28% of the total number of analysed publications. Cyclical evolution is highlighted by booms in 1987, 1990, 1995, 1999, 2001.

Literature production related to health economics between 1975 and 2022. Source: Authors

The following period, 2007 – 2022 (16 years) is characterized by an upward evolution of the number of health economics publications, 1068 publications with an annual average of 67 articles (3261 authors involved), meaning 2.3 times more numerous as in the previous two decades and representing 66% of the total sample. In 2017, 86 studies on health economics were published, reaching the highest value in the analysed period. The quantitative evolution of publications in health economics it is explained by a higher interest of the researchers and policymakers to explore the benefits of health economics. The need to identify the ways in which health economics contributes to the healthcare system development represent a solid motivation to continue intensive research in the field.

The evolution of the citations’ number follows, like a shadow, the evolution of publications’ number. The upward trend is maintained, also respecting the previously presented temporal distribution, but without cyclical and sinusoidal fluctuations. The evolution of the citations’ number indicates the growing interest of specialists in researching the field, especially after 2000 when a constant and galloping annual increase in citations begins. The last 5 years show a very high interest of researchers and academics in health economics research, with a maximum point in 2021, with over 2000 citations, an evolution argued by the emergence of the global pandemic. All the figures and observations indicate a constant interest in the conceptualization of health economics and foresee a deeper development in the future.

Geographical analysis allows a better understanding of the field. The 1620 publications involved the work of authors from 82 countries. Among them, the first 10 states with significant contributions in the field of health economics stand out: the USA (605 papers), England (400), Canada (115), Australia (103), Netherlands (75), Scotland (64), Germany (59), Switzerland (57), France (47) and Italy (43). 96.8% of all publications were produced by top-10 countries. According to statistics, the USA is the top nation. 37% of all analysed documents are written by American authors, which is 1.5 times more than values recorded by England (rank 2) and 5.2 times more than Canada, rank 3. There are 49 nations where there are fewer than or equal to 5 publications during entire period.

In our study, a sum of 4096 different authors were identified, and they individually published between one and 16 papers, but only 170 persons are co-authors of more than 3 papers. Table 1 lists the top 10 authors with publications about health economics. Drummond M.F. is the leader, even if he published Essentials of Health Economics with his co-author, Mooney G.H., in 1982. He is affiliated to University of Yor (the UK). The top ten most productive authors published 107 articles, which represents 6.6% of the total publications. The most authors (95.8% of all authors) contributed to the health economics research with less than two papers. It should be noted that the number of authors is 2.5 times over the number of papers., which means that publications are made by cooperation between researchers.

From the point of view of affiliation, the 4096 authors belong to 1723 institutions. The top 10 organizations with many health economics articles are University of London (91 publications), University of California System (54), University of York (51), Harvard University (45), University of Birmingham (41), University of Pennsylvania (34), University of Oxford (30), University of Aberdeen (28), University of California Los Angeles (28) and University of Washington (28). The list is dominated by institutions from the UK and the USA. The top-10 institutions contributed to health economics research field by 230 papers which represents 26.5% of total publications.

It is very important to see which journals have published the most articles about health economics. Regarding the publication’s titles, 847 distinct journals published all 1620 documents related to health economics. It should be mentioned that 782 journals (92.3%) published from one to three articles on health economics during 1975 – 2022. Table 2 lists the top 10 most prolific journals, and together they have published 364 articles, which means 43% of all publications in the sample. The leading journal is the Value in Health (Impact Factor = 5.156) with 160 papers meaning 9.8% of all publications from the sample.

Co-authorship analysis

Co-authorship networks and social network analysis are becoming more and more effective techniques for evaluating collaboration patterns and locating top scientists and institutions [ 26 ]. The author collaboration network can help identify authors with high contributions and reveal the co-operative relationships between the authors. By using CiteSpace, the co-authorship network was created without pruning the sliced networks. Co-authors network has 1028 nodes and 1166 links. Figure  3 presents the network between the most collaborative authors in health economics, all of them published 4 or more publications as co-authors. As indicated by the node name, each node represents a different author, and the font size corresponds to the number of publications for each author. The connections made by the co-authorship of researchers are represented by the interconnections between each pair of nodes. The degree of cooperation between the two authors is indicated by the thickness of the link.

The network of authors’ collaboration in health economics. Source: Authors

Co-authors’ map shows that there are not strong collaboration relationships between authors, the network density level is 0.0022. Moreover, they are divided in small research groups and cooperation for research in health economics is insignificant. Top five collaborative authors are Drummond M. (20 publications), Mooney G. (16), Trosch R. (8), Marchese D. (8) and Fuchs V. (8). They are followed by Basu A. (7), Edwards R. (7), Coast J. (7), Peeples P. (7) and Comella C. (6).

In Fig.  3 it can be seen the cooperation between two research teams. These research teams are formed around key authors in health economics and integrated as most collaborative ones. First research team is created around Drummond M. and Mooney G. They published in 1982 and 1983, in British Medical Journal, 9 papers about different aspects of health economics [ 27 , 28 ]. The second research team is created around Trosch R. and Marchese D., who participated between 2012 and 2015 at several annual meeting, conferences, and congresses to present their work about clinical and health economics outcomes registry in cervical dystonia [ 29 , 30 ]. There are 72 scholars as co-authors in at least 3 publications showing a weak cooperation in health economics. From the perspective of citation burst, there are 5 bursting authors with a burst duration between 2 and 8 years: Drummond M. 1981–1999, Mooney G. 1982–1986, Marchese D. 2012–2015, Trosch R. 2012–2015, and Peeples P. 2018–2020. Bust analysis confirms the existence of the two research teams and their period of activity.

We continue exploring the co-authorship analysis by studying the level of cooperation between institutions. For this purpose, we generated a network where the nodes are the institutions, and we did not used pruning methods. The level of cooperation is revealed by the thickness between institutions’ nodes. The network contains 751 nodes, 944 links, and a density of 0.0034. In Fig.  4 are labelled the institutions with more than 4 collaborative papers, the label size is depending on the number of collaborative publications. No institution has a large value of centrality, meaning that cooperation among the analysed institutions is weak, the links are very transparent because of an insignificant number of publications written by collaboration between organizations or universities.

The network of institutions’ collaboration in health economics. Source: Authors

As seen in Fig.  4 , the top-10 most collaborative institutions in health economics area are: University of York (28 publications), University of Oxford (23), University of Pennsylvania (21), University of Washington (20), University of Birmingham (17), Erasmus University (16), Harvard University (16), Bangor University (15), University of California Los Angeles (13) and University of Toronto (12). There are six institutions for which there was identified citation burst as follows: University of Oxford 2016–2020, University of Pennsylvania 2017–2022, University California Los Angeles 2013–2016, King’s College London 2006–2011, London School of Hygiene & Tropical Medicine 2008–2010, University of Washington 2015–2018. Cooperation among institutions is depending on cooperation among authors. It is understood that poor collaboration at the individual level is followed by an identical one at the organizational level.

Progress in any field can be achieved only by communication. Analysing country co-authorship may lead to identification of leading states in health economics research. The visualisation map for country collaboration reveals a network of 202 nodes, 710 links and 0.035 density. It should be noted that country co-authorship network has a density 10 times larger than institutions co-authorship network. The map was generated in CiteSpace without pruning parameter. In Fig.  5 are displayed the countries having more than 5 collaborative health economics-related publications.

The network of countries’ collaboration in health economics. Source: Authors

As can be observed, the biggest nodes correspond to the most prominent and cooperative nations. The collaboration between institutions from these nations is shown by the links between the nodes. The discrepancies between the first two countries and the other states are obvious. The network of the most collaborative country, the USA, consist in 521 publications. It is followed by England with 344 publications. It is obvious that these two nations played a crucial part in worldwide academic exchanges in health economics area. The third and the fourth most collaborative countries are Canada (105 publications) and Australia (100 publications), which shows a degree of cooperation 5 times lower than that of the leading country. The top-10 most collaborative countries continue with the following nations: Netherlands (74 publications), Germany (58), Switzerland (56), Scotland (48), France (46) and Italy (43). Citation burst was identified for 4 countries: the USA 1975–1981, Scotland 1982–2003, Switzerland 1999–2006, and China 2020–2022. Citation burst analysis reveals that China, which stated to published research in health economics in 2006, faces an upward trend in the last two years.

Co-citations analysis

The following step of our current analysis is to find the most frequently cited publications in health economics sector. Co-citation reference analysis help to identification of the most important references in health economics. 16,755 references are linked to our sample. We obtain a co-citation network of 1550 nodes and 7240 links with a density of 0.0060. The network map was obtained without pruning parameter. In Fig.  6 are labelled the papers with more than 5 co-citations. Table 3 lists the top 10 articles in the field of health economics by the number of citations.

Visualization of reference co-citation networks for health economics research. Source: Authors

As we expected, the most influential paper is published by Arrow K.J. in 1963. In his paper, the author investigates and studies the unique distinctions between medical care and other goods and services in normative economics. He focuses on medical-care industry and its efficacy by rethinking the industry from economics perspective. This publication is the basic brick in the conceptualization of health economics. Unfortunately, this part of analysis reveals some basic limitation in bibliometric analysis: incomplete and compromised database because of incorrect data filled by authors. As it can be seen in Fig.  6 , the second most influential paper belongs to an anonymous author who wrote in 1996 a paper about cost effectiveness. A manual search in references database revealed the possibility to correlate the anonymous publications to a book written by Gold M.R., Siegel J.E., Russell L.B. and Weinstein M.C. The authors published in 1996 a book about cost effectiveness in health and medicine and there are several book reviews about it. The third and the fourth most co-cited publications are signed by Drummond M.F. and his co-authors. In fact, it is about a book entitled “Methods for the Economic Evaluation of Health Care Programmes”, first published in 1987 at and then renewed in the following editions: 1997 (2nd), 2005 (3rd) and 2015 (4th). Regardless the edition number, the book is a worldwide bestseller and it very cited in health economics research. It should be mentioned that the 2nd edition of the book appears twice in the database because some authors incorrectly cited Drummond. There are many book reviews for this book because it describes techniques and tools for evaluation of health care programs. It provides syntheses of new and emerging methodologies, and it is less concerned with the theoretical and ethical foundations of the methodologies (Drummond M.F et all, 2005). The book promotes basic health economic concepts and theories.

The citation burst was checked to see the period when a document citation increases sharply in frequency. There are 12 cited papers with citation burst fluctuating from 3.95 for Volpp K.G (2008) and 9.58 for Arrow K.J. (1963). Ten of twelve papers with citation burst are the ones from Table 3 , the most co-cited documents in health economics. The top-10 papers by burst are Arrow K.J. 1963 (period 2012–2018, citation burst 9.58), Drummond M.F. 1997 (2000–2008, 8.76), Anonymous 1996 (1999–2011, 8.86), Drummond M.F 2005 (2008 – 2019, 8.42), Kahneman D. 2011 (2013–2022, 5.03), Williams A. 1985 (1986–1998, 4.44), Lakdawalla (2018–2022, 4.44), Kahneman D. 1979 (2019–2022, 4.38) and Grossman M. 1972 (2016–2019, 4.35).

Two of Kahneman D.’s works stands out. One of them is represented by a book, another worldwide bestseller, entitled “Thinking, Fast and Slow” published in 2011 in London. His psychological book is appreciated because it aids in the public understanding of issues related to engineering, medicine, and behavioural science. The second paper is written by Kahneman D. and Tversky A. in 1979 and presents opponents of the anticipated utility theory as a framework for risky decision-making and introduces an alternative model called prospect theory.

We can find highly cited authors whose work is well known in the health economics research community by using author co-citation networks. CiteSpace configurations are the same. The network of co-cited writers has 1422 nodes, 12,462 linkages, with a density of 0.0123. The node size reflects the number of co-citations by author. In Fig.  7 the nodes with co-citations over 14 are labelled by the corresponding first author. Once again there are incomplete data in the database. We face with an anonymous person as the most cited author in health economics research. This author without name was 300 time co-cited. We manually checked the database to find additional information about this anonymous author. According to the findings we assume it is about Margolis H. who published in 1982 a book about selfishness, altruism, and rationality. Margolis H. is a professor at the University of Chicago and in his book about social choice propose and argue a distinction between self-interest and group-interest for a person, and he also develop an equilibrium model for his theory [ 41 ].

Visualization of authors co-citation networks for health economics research. Source: Authors

Drummond M.F. is on the second position, positioning himself with two publications in the top-10 most co-cited authors. Once again it is about his publication with Mooney G.H. about Essentials in Health Economics which was already mentioned in the paper. Williams A. is the third co-cited author, followed by Culyer A.J and Arrow K.J. It should be noted that World Health Organization’s (WHO) publications are ones of the most co-cited document in health economics research. Unfortunately, it is hard to identify the titles of WHO’s publications from 1993 and 2009 (see Table 4 ) because there is more than one publication per year for this international organization. However, we assume that it is about an anonymous publication focused on tuberculosis as a worldwide problem [ 42 ] (published in 1993) and a publication about health risk at the global level [ 43 ] (published in 2009).

There are no scholars who have a betweenness centrality greater than zero. This indicates that there is no author more influential than other scholars, and no one exert a significant influence on the evolution of health economics research. The evolution of health economics theory was influenced by all the authors discussed in this paper.

In terms of burstiness, there are 35 cited authors with citation burst between 9.26 and 3.90. It means that their papers were intensively cited during a specific period. The top-10 cited authors by bursts is Drummond M. 1988 (bursts of 9.26, period 1995–1999), Maynard A, 1982 (8.60, 1998–2003), WHO 2009 (8.09, 2009–2015), OECD 2013 (7.77, 2013–2022), Williams A. 1982 (7.63, 1986–2003), Johannesson M. 1996 (7.59, 1996–2003), Kahneman D. 2000 (7.55, 2016–2022), WHO 1993 (7.02, 2011–2022), Cutler D.M. 2007 (6.97, 2012–2016) and Donaldson C. 1995 (6.94, 1995–2003). Even if they are not included in the previous ranking, the following cited authors should be mentioned because their burstiness periods exceeds 10 years: Fuchs V.R. 21 years (bursts of 4.54, period 1977–1998), Williams A. 17 years (7.63, 1986–2003), Mooney G. 14 years (5.29, 1995–2009), Dolan P. 14 years (4.84, 2003–2017) and Weinstein M.C. 13 years (4.14, 1999–2011).

The same way as previous maps, the cited journal visualization map for health economics research (Fig.  8 ) was created in CiteSpace, but this network has 1273 nodes (cited journals), 25,008 linkages, and a density of 0.0309. The cited journals with more than 38 citations are labelled in the network.

Journal co-citation network visualization for health economics research. Source: Authors

The top ten journals by citations in health economics are presented in Table 5 . The BMJ – British Medical Journal (381 citations) is the journal published by British Medical Association and the most prominent cited journal in health economics area. It is followed by the New England Journal of Medicine (306 citations) and The Lancet (257 citations). The journal published by American Medicinal Association ranks on the fourth place. A journal that receives a lot of citations and has a high citation burstiness score has garnered the interest of academics recently.

The citation surge affects 70 cited journals. The cited journal with the strongest citation bursts is Plos One (21.79, 2014–2022), which is not the most cited one. It is followed by British Medical Journal (20.22, 1982–2006), Value Health (13.15, 2018–2022), BMJ Open (12.48, 2017–2022), Applied Health Economics and Health Policy (10.38, 2017–2022), BMC Health Services Research (10.16, 2019–2022), Frontiers in Public Health (9.99, 2020–2022), Cost Effectiveness and Resource Allocation (9.66, 1998–2005), JAMA Internal Medicine (9.24, 2019–2022) and BMC Public Health (8.71, 2016–2022). It should be noted that 8 cited journals of the ranking are bursting to the present. British Medical Journal (24 years), American Journal of Psychiatry (15 years), The Journal of Health Services Research and Policy (14 years), The New England Journal of Medicine (13 years) and Medical Care (12 years) are the cited journals with the longest periods of bursting, even if the interest in these journals is currently low. It must be added that four of the most cited journals in health economics research are on a top-10 list of journals with the highest JIF in 2021. All these journals are one of the most influential journals in health research.

Co-occurrence analysis

In this section of the analysis, we can pinpoint the key ideas and areas of interest in health economics research. To discover the primary study subjects in many scientific research domains, keywords are generally regarded as one of the most crucial elements of any research paper [ 44 ]. Co-occurrence analysis is used to identify the conceptual structure of the field. Without any pruning, the network of related keywords is shown in Fig.  9 . The network of co-occurred keyword has 694 nodes (keywords), 2823 links (connections), and a density of 0.0117. One percent of all keywords, those with a frequency greater than or equal to five, are labelled.

Keywords co-occurrence network for health economics research. Source: Authors

Table 6 presents the top 30 keywords which are used and connected in the 1620 analysed papers. “Health economics” and “cost effectiveness” are the most co-occurred items in health economics research, they have been connected for 121 times. “Care” follows them as the second high-count keyword with a frequency of 115. One crucial statistic used in the analysis of the keyword co-occurrence network is centrality. Centrality shows a keyword's strength, influence, or other specific characteristics. In this analysis all the keywords have a null betweenness centrality.

By using bursts detection, we tried to identify research hotspots in health economics. Surprisingly, there are only two keywords with citation bursts during 1975–2022: “behavioural economics” and “economic evaluation”. The keyword with the strongest bursts is “behavioural economics” (5.57) and it caught scholars’ attention between 2019 and 2022. The second keyword by citation bursts is “economic evaluation” (4.62). This item is bursting from 2020 to 2022. It can be observed that both research themes have short periods of bursts, and they continue bursting to present.

CiteSpace allows a cluster analysis of keywords to identify topics that have captured the attention of researchers. By applying clustering tool, the keywords network has been divided in 14 clusters, labelled by keywords. Table 7 presents the top 10 keywords clusters, in descending order of their size, and the most used keywords in the analysed sample of publications. There are 14 clusters with different sizes, from 80 research topics in health economics to 4 research topics. Their Silhouette values varies from 0.757 to 0.995 which means that keywords match well to their own cluster. Figure  10 show that the clustering configuration is appropriate.

Keywords clusters. Source: Authors

The largest cluster (#0) is labelled “Health economics” and has 80 components. It contains publications about health economics, cost effectiveness, quality of life, and management. Cost effectiveness analysis and health technology assessment are subjects in the second largest cluster (#1). It is labelled “Value framework” and has 78 topics. The third cluster (#2) “Economic evaluation” contains 75 topics and the most important are care, economic evaluation, outcome, and benefits. Other research topics refer to behavioural economics, demand, cost, quality of life, risk, cancer, public heath, financial incentives, therapy, etc.

The evolution over time of the keywords can be seen in Fig.  11 , structured by cluster. CiteSpace restricts the time pane analyses to the period 1990 – 2022. Figures  11 and 12 present how interest of researchers in health economics has evolved over time. In Fig.  12 are labelled the keywords with a frequency larger than 10. In the 1990s the hot topics of research in health economics were “care”, “impact”, “health economics”, “cost”, “cost effectiveness”, “quality of life”, “outcome”, “economic evaluation”. The most debated research topics in the 2000s were “children”, “air pollution”, “patient”, “management”, “people”, “public health”, “choice”, “therapy and “risk”. In the 2010s focus is on “behavioural economics”, “population”, “obesity”, “uncertainty”, “ technology”, “health policy”, “health system”. How future research in health economics looks? It cannot be estimated with certainty, but some directions are drawn as follows: “inequality”, “care expenditure”, “health technologies”, “analysis plan”, “adaptative design”, “transparency”, “biodiversity”. These topics may shape the future literature in health economics.

Timeline view of keywords clusters in health economics between 1990 and 2022. Source: Authors

Time zone view of keywords clusters in health economics between 1990 and 2022. Source: Authors

The performed literature analysis enables us to respond to the research queries that were addressed in the paper's introduction, as follows:

How scientific production has evolved in health economics?

It can be observed a general upward trend of health economics publications, but with numerous upward and downward fluctuations, generating sinusoidal cycles with an average duration of 3–4 years. The period 1975 – 1986 is characterized by a very low number of publications. The next two decades (1987 – 2006) are characterized by a slightly increasing trend in the number of publications, with an annual average of approximately 23 publications on health economics. The following period, 2007 – 2022 is characterized by an upward evolution of the number of health economics publications, 1068 publications with an annual average of 67 articles. The evolution of the citations’ number indicates the growing interest of specialists in researching the field, especially after 2000 when a constant and galloping annual increase in citations begins. The last 5 years show a very high interest of researchers and academics in health economics research, which is justified by the existence of worldwide Covid pandemic period.

Who are the most important authors and publications in health economics?

In our study, 4096 different authors were identified, and they individually published between one and 16 papers. Among the most important authors in health economics are Drummond M.F., Jonsson B., Coast J., Donaldson C. and Edwards R.T. Regarding the publication’s titles, 847 distinct journals published all 1620 documents related to health economics. Value Health, Health Economics, British Medical Journal, Pharmacoeconomics and Health Policy are among journals with high interest in health economics publications.

What are the geographical and institutional hubs of knowledge production in health economics?

The analysed publications involved the work of authors from 82 countries. The states with significant contributions in the field of health economics are the USA, England, Canada, Australia, and Netherlands. From the point of view of affiliation, the authors belong to 1723 institutions. The institutions with a high number of publications about health economics are University of London, University of California System, University of York, Harvard University and University of Birmingham.

What kind of collaboration between authors, organizations, and nations are there in the field of health economics research?

There are not strong collaboration relationships between authors. They are divided in small research groups and cooperation for research in health economics is insignificant. The most collaborative authors are Drummond M., Mooney G., Trosch R., Marchese D., and Fuchs V. There are two research teams created around Drummond M. and Mooney G., on the one hand, and around Trosch R. and Marchese D., on the other hand. Cooperation among institutions is depending on cooperation among authors. It is understood that poor collaboration at the individual level is followed by an identical one at the organizational level. The most collaborative institutions in health economics area are University of York, University of Oxford, University of Pennsylvania, University of Washington, and University of Birmingham. Regarding collaboration between countries, the USA and England played a key role in worldwide academic exchanges in health economics area, followed by Canada, Australia, and Netherlands.

Which are the most cited authors and the most cited papers, and which are the most attractive journals for publishing research results in health economics?

The most influential paper is published by Arrow K.J. in 1963, entitled “Uncertainty and the Welfare Economics of Medical Care”. The second most influential paper belongs to an anonymous author who wrote in 1996 a paper about cost effectiveness. We assume that is a book written by Gold M.R., Siegel J.E., Russell L.B. and Weinstein M.C., entitled “Cost-Effectiveness in Health and Medicine”. The third and the fourth most cited publications are signed by Drummond M.F. and his co-authors. In fact, it is about a book entitled “Methods for the Economic Evaluation of Health Care Programmes”, first published in 1987 at and then renewed in several editions. Another influential book was written by Kahneman D., entitled “Thinking, Fast and Slow” and published in 2011. The most cited author is Margolis H., who published in 1982 a book about “Selfishness, Altruism, and Rationality”. Drummond M.F. is on the second position with the publications about “Essentials in Health Economics”. Williams A. is the third cited author, followed by Culyer A.J and Arrow K.J. It should be noted that World Health Organization’s (WHO) publications are ones of the most cited document in health economics research. The most cited journals in health economics are The BMJ – British Medical Journal, The New England Journal of Medicine, The Lancet, Journal of American Medicinal Association and Health Economics. Beside them, other very influential journals are Plos One, Value Health, BMJ Open, Applied Health Economics and Health Policy and BMC Health Services Research.

What are the most debated conceptual approaches in health economics?

“Health economics”, “cost effectiveness” and “care” are the most debated concepts in health economics. But the current research hotspots in health economics are “behavioural economics” and “economic evaluation”.

Discussions and conclusions

The current bibliographic analysis was done for a specialized literature: health economics. This analysis contributes to the evaluation of the progress of the global knowledge in health economics and to the evaluation of the interest in health economics research. Moreover, the research allows the identification of the authors who contributed to the theoretical conceptualization of health economics, but also the identification of the most cited works in the field. A bibliometric analysis of the health economics research topic was produced, based on 1620 papers that were published between 1975 and 2021 and indexed in WoS. According to the tables and figures above, we have identified the important authors, publications, nations, organizations, keywords, and references.

By giving information on the current state of the art and identifying trends and research possibilities through the selection and analysis of the most pertinent publications published in the subject of health economics, the current study completes the body of existing research.

Through an extensive field mapping, the study increases the added value for the study of health economics theory. The development patterns of health economics are described by identifying trends in research production in that field and the most productive nations. The identification of top contributors’ points to possible collaborators (universities and researchers) for additional research projects. Finding the most appealing source names reveals publishing prospects for health economics-related articles. Leading thematic areas and developing research areas can be found to help academics identify research gaps in health economics.

Limitations and future research directions

Even though the bibliometric analysis and mapping visualization on articles relevant to health economics in the current research have produced numerous fascinating results, this methodology has several drawbacks. These limitations are due to the bibliometric analysis and quality of database. A quantitative analysis reduces the influence of subjective judgments. In several parts of the analysis, we were forces to use manual search because of inadequate or incomplete data. Maybe, manual analysis is required to learn additional specifics about different aspects of health economics theory by using a systematic review analysis.

The following limitations of the current study should be considered. First, the search strategy leads to a lost in publications which do not contain the query word in the publication title. Therefore, the main findings should be interpreted in accordance with the selection strategy used in this paper. The dataset is downloaded only from WoS, maybe multi-source searching is more convincing. Publications in other languages were not analysed. For some publications the name of author was missing. Some journals change their title in time, and they appear twice as being different journals. In this analysis it was used an inhomogeneous sample due to the type of publications.

Therefore, these restrictions remain issues that need to be resolved in additional research. To sum up, our analysis cannot cover every crucial publication concerning health economics, but we believe that the results allow us to have reliable insight into the knowledge domain. This study could be carried out in the future utilizing new search criteria, time periods, or bibliometric analytic parameters.

Availability of data and materials

The data can be extracted from Web of Science. All data are available upon application.

Abbreviations

European Union

Gross Domestic Product

Journal Impact Factor

Organisation for Economic Co-operation and Development

Science Citation Index Expanded

Social Science Citation Index

The United Kingdom

The United States of America

World Health Organization

Web of Science

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Project financed by Lucian Blaga University of Sibiu through the research grant LBUS-IRG-2022-08.

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Barbu, L. Global trends in the scientific research of the health economics: a bibliometric analysis from 1975 to 2022. Health Econ Rev 13 , 31 (2023). https://doi.org/10.1186/s13561-023-00446-7

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About the Concentration in Health Economics and Policy

The concentration in Health Economics and Policy prepares doctoral students to address the most pressing challenges in health and health care through innovative, rigorous and interdisciplinary research in the field of health economics. This program integrates traditional training in economics with practical training in health policy and health services research to train the next generation of health economists.

The curriculum offers a broad exposure to the health economics literature and public health disciplines, and stresses the policy implications of these fields of research. The curriculum stresses a foundation in applied modern microeconomic theory, economic evaluation, quantitative methods and econometrics, including PhD-level courses from the Department of Economics in the Krieger School of Arts and Sciences.

Doctoral students are paired with a faculty adviser from the Health Economics concentration with similar research interests. Faculty in the Health Economics concentration are working in a variety of research areas including understanding health insurance design, the economic implications of health and health care disparities, market forces and health care prices, pharmaceutical economics, and payment design and access. Doctoral students will also have the opportunity to work with other faculty within the Department, as well as faculty from other Departments including International Health, Population, Family, and Reproductive Health, Biostatistics, the School of Medicine, School of Nursing, the Carey Business School, and the Department of Economics. Students also often work with various centers and initiatives across the University, including the Hopkins Business of Health Initiative.

What Can You Do With a Graduate Degree In Health Economics And Policy?

The program prepares students for successful research careers as health economists. Former students have gone onto careers in academia, government, research-oriented non-profits, and the private sector. Visit the  Graduate Employment Outcomes Dashboard to learn about Bloomberg School graduates' employment status, sector, and salaries.

View a list of selected recent graduates and dissertation titles for the PhD Concentration in Health Economics and Policy.

Curriculum for the Concentration in Health Economics and Policy

Browse an overview of the requirements for this PhD program in the JHU  Academic Catalogue  and explore all course offerings in the Bloomberg School  Course Directory .

Admissions Requirements

For general admissions requirements, please visit the How to Apply page.

Standardized Test Scores

Standardized test scores are  not required and not reviewed  for this program. If you have taken a standardized test such as the GRE, GMAT, or MCAT and want to submit your scores, please note that they will not be used as a metric during the application review.  Applications will be reviewed holistically based on all required application components.

Matthew Eisenberg, PhD, MPhil,

uses applied health economics methods to study how consumers make decisions about their healthcare.

All accepted PhD students receive a standard funding package.  As of September 1, 2023 this package includes full tuition support, a $30,000 per year stipend, individual health, dental, and vision insurance and the University Health Services clinic fee for four years.

For funding sources, please see PhD funding page .

Need-Based Relocation Grants Students who  are admitted to PhD programs at JHU starting in Fall 2023 or beyond can apply to receive a $1500 need-based grant to offset the costs of relocating to be able to attend JHU.   These grants provide funding to a portion of incoming students who, without this money, may otherwise not be able to afford to relocate to JHU for their PhD program. This is not a merit-based grant. Applications will be evaluated solely based on financial need.  View more information about the need-based relocation grants for PhD students .

Questions about the program? We're happy to help. [email protected] 410-955-2488

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Health Economics

Program snapshot.

The Common Fund’s Health Economics program supported research to identify factors influencing adoption of new innovations in treatments, diagnosis, and preventive strategies; so that past and future investments by NIH may have greater public health impact. To that end the program focused on identifying factors that influence the adoption of high-value health technologies and personalized medicine approaches.

Examples of program research accomplishments include:

• Expanded research community in health economics that is now being utilized at multiple NIH institutes to address issues in which economic analysis of factors can influence health and the adoption of NIH-supported innovations.
• Identified factors that influence both optimal adoption of high-value health technologies and phasing out low value technologies.
• Expanded understanding of preferences from physicians, payers, and patients that impact adoption of personalized medicine approaches. Also assessed the value of these approaches using test cases such as gene testing to inform breast cancer treatment or prescription drug effectiveness.
• Developed the State Health Practices Database for Research  (SHPDR), a resource containing data on state-level statues that can be used with common statistical analysis packages to inform health economics research.

Health economics research promotes a deeper understanding of how stakeholders (patients, providers, health care systems, payers, etc.) value new technologies and interventions aimed at improving health. The value of any NIH-funded biomedical research will ultimately depend on the actions of these participants in the health care system because they determine what interventions are actually delivered and how well they are used. The Health Economics Program identified factors determining adoption of effective health technologies, innovations, and discoveries; as well as discontinued use of lower value or ineffective technologies.

Personalized medicine holds the promise to revolutionize medical practice by taking into account individual differences in lifestyle, environment, and biology when prescribing treatments for diseases. However, this practice is currently not being used for most diseases. By studying early implementations of personalized medicine, research from this program offered insights into how preferences from health care system participants affect the uptake of this approach as well as the benefits compared to standard one-size-fits-all approaches.

Equally important as the research findings from this program are the tools that have been developed. These resources are being leveraged by individual NIH Institutes and Centers to conduct related, but more disease-focused, research on these questions to optimize the adoption of future biomedical technologies and interventions.

Although Common Fund support for the Health Economics Program has come to a close, trans-NIH interest in health economics research to support turning discovery into health continues in the form of a scientific interest group. NIH staff interested in joining the Health Economics SIG should contact Gregory Bloss ( [email protected] ).

Announcements

2016 Data Now Available in the State Health Practices Database

Public Resource on Diffusion of Medical Technologies

Program Transition

The Health Economics program has transitioned from Common Fund support. Common Fund programs are strategic investments that achieve a set of high-impact goals within a 5-10 year timeframe. At the conclusion of each program, deliverables transition to other sources of support or use by the broader scientific community. Read the Health Economics close-out report summary .

The Health Economics program was supported by the Common Fund from 2011 to 2017. On September 28-29, 2017 the program hosted a workshop, Turning Discovery into Health: The Contributions of Health Economics Research , to highlight the results of the program and to stimulate ideas for future economic methods that can be applied to the NIH mission. Health economics research remains an area of priority at multiple NIH institutes and centers. For more information see the notice Clarifying NIH Priorities for Health Economics Research .

Please note that since the Health Economics program is no longer supported by the Common Fund, the program website is being maintained as an archive and will not updated on a regular basis. 

State Health Practice Database for Research Website

Health Economics Scientific Interest Group

Trans-NIH interest in health economics research has sparked the formation of a scientific interest group . NIH staff interested in joining the Health Economics SIG should contact Gregory Bloss ( [email protected] ).

This page last reviewed on June 4, 2024

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The past, present, and future of health economics in India

Singh, Nihaal 1, ; Shukla, Rushikesh 1 ; Acharya, Sourya 1 ; Shukla, Samarth 2

1 Department of Medicine, Jawaharlal Nehru Medical College, DMIMS (DU), Sawangi, Wardha, Maharashtra, India

2 Department of Pathology, Jawaharlal Nehru Medical College, DMIMS (DU), Sawangi, Wardha, Maharashtra, India

Address for correspondence: Nihaal Singh, MBBS Student, Jawaharlal Nehru Medical College, DMIMS (DU), Sawangi, Wardha, Maharashtra - 442 001, India. E-mail: [email protected]

This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 4.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Health economics has long been neglected as a subset of the larger discipline of Economics and Finance. However, this could not be further from the truth. There is a large body of researchers and professionals alike that are of the consensus that extensive studying and working upon Healthcare Economics can help us avert the situation that arose in the wake of the recent COVID-19 pandemic. Applying the core principles of Health Economics to a situation like that would help avert bad outcomes. In this article, the authors begin by defining and establishing the concepts of Health Economics and then building on them. We further explain the concepts in light of the Indian Economy and Healthcare Sector and how it has seen unprecedented growth in the last decade. Furthermore, we touch on the various diseases that put the most strain on the healthcare infrastructure and what we can do to make the situation better. We also shed light on how the COVID-19 pandemic affected the Health Economics in the Indian setting and go on to elaborate how India managed to tackle it. Finally, we elaborate on what steps we can take, as researchers and healthcare professionals, to make it easier for the common man to get access to better and more economical healthcare. We determine the importance and effectiveness of data collection and processing and also how to make better research attempts to study, evaluate and process that said data. The onus falls on the academic and the healthcare professional to ensure that the true meaning of Health Economics is not reduced to a mere numbers game, but is something which is truly subjective and for the benefit of the masses.

Introduction

Health Economics is an amalgamation of two very distinct schools of thought. One is heavily reliant on trying to describe what health means to an individual and what factors influence health, both at the individual and at the community level. The other ties healthcare to its monetary statistics and tries to point out how tweaking certain modalities can influence the final cost that the individual seeking healthcare services has to bear. We will talk about both these lines of investigation in the sections that follow.

Health economists since times immemorial have faced an important dilemma. The dilemma being that health economics is remarkably distinct from its parent branch, which is Classical Economics. This situation arises due to the often constant involvement of third-party players like insurance companies and hospital financing infrastructure. This, combined with issues such as the vagueness of involved metrics, viz-a-viz. QALY (Quality Adjusted Life Years), which are very commonly used to measure healthcare statistics makes it increasingly difficult to trust the computations that are churned out when the numbers are run.

Factors such as the presence of third-party insurers, external intervention, barriers to entry, inevitability of uncertain outcomes, and heavy government involvement make it extremely difficult to consider healthcare in the same domain as other goods and services. Thus, a very distinct perspective is required to evaluate healthcare from the economic lens.

It is important to note that healthcare is conceptually very distinct from other goods that are dealt with in Classical Economics. This is due to the fact that healthcare can be incredibly subjective in terms of success. And this subjective viewpoint can vary from individual to individual and community to community. To avoid making erroneous calculations and coming to conclusions that are based on a standardized metric, we must ensure that healthcare data are collected and processed with due context always analyzed with respect to the individual or community in question.

Health economics in the Indian context

There have been several factors that have caused a massive growth of the healthcare sector in India. Some of these are increase in population, increased life-expectancy, affordable private healthcare, more spare income, and Government’s emphasis on improving healthcare.

This growth means that Indian Healthcare is now valued at over a massive $40 billion. And out of this more that 80% of spending is in private sector and by means of money-at-hand. India thus, has various growth opportunities along with challenges that India has to tackle on its way to success. Medical tourism has become one of the most popular destinations in India, with a $2 billion business. Many super-specialty hospitals, highly qualified medical staff, telemedicine, and government incentives all help to boost health tourism in the country. The huge population, diverse genetic pool, and wide range of disorders make it ideal for clinical trials and personalized medicine research. [ 1 ]

By 2030, India will become the most populated nation on this planet, and approximately 200 million Indians would be at minimum 60-years-old. The expanding ageing demographic, on the other hand, is putting a huge strain on the healthcare infrastructure. With the growth in infectious and behavioral illnesses, urbanization has put a strain on national infrastructure.

A few of the major issues is that healthcare in India is primarily paid for out of cash; over 3/4 th of hospitals and almost half of hospital beds are privately owned. The lower classes cannot pay private health insurance since it is mostly private. [ 1 ]

Our healthcare expenditure is insufficient; overall healthcare spending accounts for only 4.1 percent of gross domestic product (GDP), the lowest among the Brazil, Russia, India, China and South Africa (BRICS) countries. In India, there is a difference in the providing of resources and infrastructure among various socio-economic regions. In comparison to the WHO’s assessment of the global averages of 3,960 beds/million people, India has roughly 860 beds/million inhabitants.

Conditions that most severely burden Health Economics in India

A place like in India where you have less to eat and more glucose in one’s body creates an irony that by itself remains unsolved. Thousands of cases of explosively high numbers of patients with uncontrolled diabetes are handled and often lead to the worst prognosis. The number of patients suffering from diabetes in India, mainly type 2, is rising each day. It also raises the cases of ophthalmic issues and requirement of dialysis machines. As dreadful this disease is, the more dreadful its outcome on economics is. It holds a major chunk of India’s economy to deliver dialysis machines to every Primary Health Centre and government aided hospitals. Even after adjusting for GDP per capita, our regression study found that direct diabetes expenses are closely and positively linked with a country’s GDP per capita, and that the United States stands out as having exceptionally high expenditures. [ 2 ]

Dialysis is not just expensive for the individual itself but it is a major monetary setback even for the country’s GDP. The cataract and glaucoma caused by the same needs equal monetary help for treatment. Carelessness of the patient and the doctors leading to such preceding health concerns require a stronghold in the money. India has to report every case to the WHO which sets up a margin of how the work needs to proceed. The work to reduce its monetary dependence needs to be started at the level of doctors and government aided hospitals itself. Diabetes being 13 th most dangerous cause of death in India, each diabetes case needs to be examined well and its related prognosis needs to be set so that we do not suffer from its allied morbid progression like renal failure in case of diabetes. Renal failure being another burden for Indian economics. The dependence of patients on dialysis machines needs to be reduced as well. The next step that needs to be taken is at the state level and to improve prevention methodologies.

Hypertension

Ischemic heart disease being the sixth most common form of disability-related death in India and hypertension being the most important etiology of it. The prevalence of hypertension in the Indian subcontinent however is due to various factors like poor nutrition due to poverty, low air quality, and the proportion of people who are aware of risk factors like smoking. According to data released in 2017, hypertension, often known as high blood pressure, affects nearly three out of ten Indians and is responsible for 17.5% of all deaths and 9.7% of disability-adjusted life years (DALYS) in the country. DALYS are a unit of measurement that measures the entire disease burden as well as the years lost owing to disability, illness, and premature mortality. [ 3 ] Obesity had the largest socioeconomic status gradient, followed by diabetes and hypertension. [ 4 ]

The associated treatment is also very expensive. The government of India provides the poor with generic medicine for the same. The ischemic heart damage associated with hypertension also bridges a lot of problems along with them. The diagnosis of such heart disease requires a lot of expensive interventions and also the cost of a basic ECG has to be taken into account. Stenting being the most expensive intervention whereas anticoagulant and statin therapy being another one. Even after all of the medicine provided by the doctors and the government at dirt cheap costs, the prognosis of such illness is not always good. The increase in death in relation to this demands an increase in incineration plants and dumping grounds. Bio-waste management is also required. All of this increases the monetary demand from the government and hence bending the economy more towards the decline.

Tuberculosis

The burden of tuberculosis (TB) in India outweighs any other factor that droop the monetary use in health economics India. According to a paper published in The Lancet, TB-related fatalities cost India $32 billion per year, or over Rs 2 lakh crore. [ 5 ] The extreme poverty commonly linked with TB is one of the most important characteristics of the disease’s burden in India. Despite the availability of free TB diagnosis and treatment services, persons with TB may become impoverished. [ 6 ]

For impoverished TB patients, getting nominally free TB testing and treatment facilities can be costly. The Indian government puts innumerable efforts in building vaccines for TB, giving vaccines to people for free, preventing TB by cleanliness, despite all the effort, it goes in vain if people are not educated enough to understand the situation. Cost of BCG vaccination surges high due to the huge population. The reporting of all cases has to be mandatorily done to the WHO and hence, WHO creates a lot of pressure on the country for it to escape such lethal and highly contagious disease. The progression of TB to lung abscess or any pulmonary condition may also be very difficult to treat. The reports of drug-resistant TB have been on rise for a long time in India. No specific treatment modality or regime offers 100% better prognosis to this.

Burden due to overpopulation

The population, though not having an ill effect on the health of an individual, has major concerns when it comes to economics for the country. Feeding the population of the country and looking after its wellbeing and its unfurling illnesses is not a piece of cake and surely requires a handful of money. With each additional member aged 60 years and above, the likelihood of catastrophic health expenditures increases: 33% of households with one 60+ member and 38% of households with 2 or more 60+ members experienced catastrophic health expenditures, compared to only 20% of households with all members under the age of 60 years. [ 7 ]

The major burden carried by the population is the spread of infectious diseases like TB, COVID-19. Both of them explained further. Spread of such highly contagious diseases piles up a plethora of patients under latency. With such a skyrocketing population, waste management becomes an undefeatable issue. For that matter, not just India but many other Asian countries suffer from the same issue. Malnutrition also accompanies the population. The protein energy malnutrition burden has also seen a rise in numbers in India. The fact that per square meter of space shared by a set of people is not enough to suffice the entire population. The majority of patients with lung disorders in India are chronic obstructive pulmonary disorders or a vast number of people suffer from exaggerated allergy in India. Most of these illnesses are in dire need of clean air. The pollution in India is indexed at peak. All such factors need special attention from the government and enough funding has to be made to engulf them all. The more the population, the greater will be the monetary support required. Increase in morbidity associated with population-related disease, will lead to rise in the requirement of better end care facilities. More hospice care will hence require more economic grasp from the country. The grasp of which is really loose in a country like India.

How COVID-19 affected health economics in India

The COVID-19 pandemic was a global catastrophe that caused a massive and ugly strain on the healthcare infrastructure. However, during the second wave of the infection, no country was as strained for medical resources, as India.

India’s corporate medical industry has made a considerable contribution, accounting for over 2/3 rd s of inpatient treatment. Several private hospitals began their preparations in reaction to the COVID-19 outbreak, which included considerable expenditures in infrastructure for treatment, as well as appropriate equipment and increased labor. Furthermore, owing to prolonged treatment times for other diseases and elective procedures, clinics, and laboratories have seen a significant drop in income, as the outbreak is expected to reduce private institutions’ operating profit by nearly half this year. [ 8 ]

The medical industry, in collaboration with the federal and local agencies, developed a sophisticated reaction strategy to counter the outbreak, including the establishment of specialized COVID-19 clinics, exclusion facilities, and technology-enabled identification of resources. The Government of India also used software to efficiently control the pandemic, developing a variety of programs at the federal and local strata. The Aarogya Setu smartphone app, which helped with symptomatic detection and tracing of contacts, was commonly deployed across India. [ 8 ]

We saw unprecedented number of hospitalizations and deaths following the pandemic and the wave upon wave of rise in infections was not mitigated by loose government regulations. This must be a wake-up call to authorities in the nation to make better policies for future situations that keep in mind the fragile state in which it puts the Healthcare Economy.

What can be done to improve the state of health economics in India

Health technology assessments (HTAs) are an essential tool for determining the economic worth of medical treatments across the world. It is utilized to divide healthcare spending in an effective and equitable manner. There is no centralized medical reimbursement system in India, no payee-willingness levels, no general statement, regulations, or standards for health economics assessment models. Furthermore, healthcare delivery is not consistent. [ 1 ]

Nevertheless, HTAs could still be used to navigate general compensation of medical procedures, to advise valuation process for new drugs or drug categories, and to assist healthcare authorities in developing clinical practice framework to ensure uniformity in delivery and scientifically backed treatments for peak effectiveness.

The majority of health economics research in India are partnerships with scholars from other countries. In India, there is a dearth of understanding of the ideas and methodologies for performing pharmaco-economic analyses. In India, there is presence of a few practitioners of healthcare economic analysis working in education and professional institutes, although these are small communities of expertise. On a national basis, we need educational sessions and the exchange of best practices. This will aid in the provision of information as well as a reservoir of qualified academics. [ 1 ]

The outcome of an investigation is determined by the data. As a result, the input obtaining process for these studies must be reliable. In India, policies for healthcare evaluation models must be authorized by the administration. There is still a lot of work to be done, even though the maiden pharmaco-economic recommendations have seen development and delivery to consumers.

Thus, delivering upon better data collection and processing, institutions, both private and public can make better and more informed decisions that will make it easier for the healthcare sector, as well as the individual gain benefit out of situations. This is only possible if everyone involved makes a collective effort in the spirit of improving the Health Economy of the nation.

Health Economics is a discipline that has only been vaguely defined and intensely debated. In order to make better decisions that are data-driven and justifiable by number crunching, we must partake in extensive research and planning at both national and international levels. This will enable us to make bold and substantial claim about how to better manage situations that strain the Healthcare Infrastructure and the associated financial institutions.

There are some diseases that affect the individual and the collective more so and in higher frequencies than the others, and it is imperative that we target them and isolate them. Following Pareto’s distribution, 20% of work will solve 80% of our problems. With this spirit, we must move forward by being both optimistic and rational. This will help us tackle the problems posed to us, such as the recent COVID-19 pandemic much more efficiently and economically.

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Fall 2024, research internship, center on health policy, economic studies.

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Interns spend approximately 12 weeks working alongside leading experts in government and academia from all over the world. Brookings also offers students the opportunity to intern in departments such as communications, human resources and central operations management. The mission of the Internship Program is to provide students with a pre-professional learning experience that offers meaningful, practical work experience related to their field of study or career interest. Students engage in career exploration and development as well as learn new skills.

Interns will have the opportunity to attend virtual meetings, think tank events, professional development workshops, and public Brookings events.  In addition, interns will have the opportunity to network with other interns throughout the Institution.

About the Economic Studies Program: The Economic Studies program analyzes current and emerging economic issues facing the United States and the world and, based on that research, provides innovative, practical policy recommendations. ES’s work covers a broad array of economic issues, including economic growth, economic opportunity and mobility, the labor market, regulation, health policy, the environment, tax policy, retirement policy, and fiscal and monetary policy. Economic Studies scholars influence key economic policies by conducting rigorous and relevant research and then disseminating and communicating their findings to the policy community and to the broader public.

The Center on Health Policy produces rigorous research and analysis that informs the national health care policy debate. Recent and ongoing research examines impediments to prescription drug competition, strategies to enhance insurance coverage, provider consolidation, insurance market design, and policies to improve mental and behavioral health care in the U.S.

This internship is an opportunity for undergraduate students in their junior or senior year and graduate students with an interest in health policy and economics to apply principles and theory learned in the classroom in a professional environment. This internship pays an hourly rate of $17.50; applicants must be willing to commit to a minimum number of hours per week (no less than 25 hours per week, during regular business hours – full-time preferred), with some flexibility around an academic course schedule.

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• SPHHP dean contributes to JAMA paper on Women’s Health Initiative

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SPHHP dean contributes to JAMA paper on Women’s Health Initiative

By DAVID J. HILL

Published June 5, 2024

The headlines around coverage of a paper published last month in the Journal of the American Medical Association (JAMA) about the Women’s Health Initiative (WHI) hormone trials didn’t quite capture the intent of the study, according to a UB investigator and longtime WHI researcher.

The JAMA paper was intended to assist clinicians in interpreting the trial findings according to type of medication and personal factors that might help with understanding absolute risk of several, concerning, negative outcomes, including heart disease, stroke, blood clots and breast cancer, including age at administration. The goal of the JAMA publication was to provide further detail, clarity and longer-term assessment of risks and benefits, says Jean Wactawski-Wende, dean of the School of Public Health and Health Professions and a co-author on the JAMA paper.

The trials started in 1993 to determine risks and benefits of longer-term use of hormone therapy (HT) after menopause. One misnomer, Wactawski-Wende says, was that WHI was assessing menopausal symptoms, but it wasn’t. That was already understood. WHI was designed to determine the balance of risks and benefits on chronic disease in postmenopausal women.

The estrogen-plus-progestin trial was stopped early in 2002 amid fears over the safety of combination hormone therapy for women randomized to active drug compared to placebo. Overall the results indicated increases in risk of breast cancer, heart disease, stroke and blood clots. The estrogen-only trial compared to placebo showed an increase in stroke and elevated blood clots, with lower breast cancer risk.

The most recent JAMA paper, a long-term follow-up of the WHI, reports that for menopausal women under age 60, the benefits of hormone therapy for those on estrogen alone likely outweigh the risks.

For women taking estrogen plus progestin, the risk outweighed the benefits, but in women under age 60, the absolute risk was quite small. This detail by type of formulation and age at which it was started indicates that some women who are experiencing moderate to severe symptoms may consider use, as long as the drugs are taken using the smallest effective dose for the least amount of time to manage menopausal symptoms. These decisions should only be made in consultation with a clinician, Wactawski-Wende says.

Wactawski-Wende says there’s an important nuance in the messaging WHI researchers have tried getting out to both clinicians prescribing and women considering taking hormone therapy. She wants to set the record straight on some of the misinterpretations people have of the most recent WHI data, including news reports stating WHI was flawed and that initial fears related to the combination hormone therapy trial were overblown.

“The WHI trials were never flawed. The purpose of the WHI trials was to learn more about chronic disease risk and benefit in menopausal women. The focus of WHI was never about symptoms of menopause,” says Wactawski-Wende, who is also a SUNY Distinguished Professor in the Department of Epidemiology and Environmental Health.

“This paper is not a turnaround on WHI,” Wactawski-Wende adds. “The new information it presents is extending out 20 years from the start of the trial and looking at the initial and long-term risks of hormone therapy. The longer you go out from active treatment, the more the risks reduce.”

The initial WHI study consisted of three randomized clinical trials: the Hormone Therapy (HT) Trial, the Diet Modification (DM) Trial and the Calcium and Vitamin D (CaD) Trial.

The Hormone Trial focused on estrogen alone for women who had a hysterectomy and a combination of estrogen plus progestin, which was added as a protective effect for women who still had a uterus. Researchers halted the estrogen-plus-progestin arm early, in 2002, amid concerns that women who took combination hormone therapy had increased risk of coronary heart disease, stroke, pulmonary embolism and breast cancer.

“What tends to get lost is that at the time the WHI trials were started, there was no trial to say estrogen alone or estrogen plus progestin protected against heart disease, and there were increasing concerns about breast cancer risk,” Wactawski-Wende says. “Many women were being put on estrogen hormone therapy for prevention of heart disease when those studies had been done. Women were often starting HT in their 60s or later assuming it would be beneficial to the heart,” she says.

The U.S. Department of Health and Human Services reports that the WHI results in 2002 found that postmenopausal women taking estrogen plus progestin for menopause symptoms had a higher risk of breast cancer, heart disease, stroke and blood clots. While their risk of fractures and colorectal cancer were lower, these benefits did not outweigh the risks and, as a result, many women stopped taking hormone therapy worldwide. That, in turn, led to a sharp decline in breast cancer that has been shown in published reports from many countries.

The JAMA paper concludes: “For postmenopausal women, the WHI randomized clinical trials do not support menopausal hormone therapy to prevent cardiovascular disease or other chronic diseases. Menopausal hormone therapy is appropriate to treat bothersome vasomotor symptoms among women in early menopause, without contraindications, who are interested in taking hormone therapy.”

Wactawski-Wende says it’s important to recognize that the WHI continues to provide researchers with valuable information on chronic disease in postmenopausal women, and particularly women in the 80- to 100-year-old age group. She notes there are still 50,000 active WHI participants 30 years after the start of the study.

“We continue to learn so much from these women,” she says. “They’re a remarkable cohort.”

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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