high cost of healthcare research paper

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• Published: 23 June 2020

The high cost of prescription drugs: causes and solutions

• S. Vincent Rajkumar   ORCID: orcid.org/0000-0002-5862-1833 1  

Blood Cancer Journal volume  10 , Article number:  71 ( 2020 ) Cite this article

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Global spending on prescription drugs in 2020 is expected to be ~$1.3 trillion; the United States alone will spend ~$350 billion 1 . These high spending rates are expected to increase at a rate of 3–6% annually worldwide. The magnitude of increase is even more alarming for cancer treatments that account for a large proportion of prescription drug costs. In 2018, global spending on cancer treatments was approximately 150 billion, and has increased by >10% in each of the past 5 years 2 .

The high cost of prescription drugs threatens healthcare budgets, and limits funding available for other areas in which public investment is needed. In countries without universal healthcare, the high cost of prescription drugs poses an additional threat: unaffordable out-of-pocket costs for individual patients. Approximately 25% of Americans find it difficult to afford prescription drugs due to high out-of-pocket costs 3 . Drug companies cite high drug prices as being important for sustaining innovation. But the ability to charge high prices for every new drug possibly slows the pace of innovation. It is less risky to develop drugs that represent minor modifications of existing drugs (“me-too” drugs) and show incremental improvement in efficacy or safety, rather than investing in truly innovative drugs where there is a greater chance of failure.

Causes for the high cost of prescription drugs

The most important reason for the high cost of prescription drugs is the existence of monopoly 4 , 5 . For many new drugs, there are no other alternatives. In the case of cancer, even when there are multiple drugs to treat a specific malignancy, there is still no real competition based on price because most cancers are incurable, and each drug must be used in sequence for a given patient. Patients will need each effective drug at some point during the course of their disease. There is seldom a question of whether a new drug will be needed, but only when it will be needed. Even some old drugs can remain as virtual monopolies. For example, in the United States, three companies, NovoNordisk, Sanofi-Aventis, and Eli Lilly control most of the market for insulin, contributing to high prices and lack of competition 6 .

Ideally, monopolies will be temporary because eventually generic competition should emerge as patents expire. Unfortunately, in cancers and chronic life-threatening diseases, this often does not happen. By the time a drug runs out of patent life, it is already considered obsolete (planned obsolescence) and is no longer the standard of care 4 . A “new and improved version” with a fresh patent life and monopoly protection has already taken the stage. In the case of biologic drugs, cumbersome manufacturing and biosimilar approval processes are additional barriers that greatly limit the number of competitors that can enter the market.

Clearly, all monopolies need to be regulated in order to protect citizens, and therefore most of the developed world uses some form of regulations to cap the launch prices of new prescription drugs. Unregulated monopolies pose major problems. Unregulated monopoly over an essential product can lead to unaffordable prices that threaten the life of citizens. This is the case in the United States, where there are no regulations to control prescription drug prices and no enforceable mechanisms for value-based pricing.

Seriousness of the disease

High prescription drug prices are sustained by the fact that treatments for serious disease are not luxury items, but are needed by vulnerable patients who seek to improve the quality of life or to prolong life. A high price is not a barrier. For serious diseases, patients and their families are willing to pay any price in order to save or prolong life.

High cost of development

Drug development is a long and expensive endeavor: it takes about 12 years for a drug to move from preclinical testing to final approval. It is estimated that it costs approximately $3 billion to develop a new drug, taking into account the high failure rate, wherein only 10–20% of drugs tested are successful and reach the market 7 . Although the high cost of drug development is a major issue that needs to be addressed, some experts consider these estimates to be vastly inflated 8 , 9 . Further, the costs of development are inversely proportional to the incremental benefit provided by the new drug, since it takes trials with a larger sample size, and a greater number of trials to secure regulatory approval. More importantly, we cannot ignore the fact that a considerable amount of public funding goes into the science behind most new drugs, and the public therefore does have a legitimate right in making sure that life-saving drugs are priced fairly.

Lobbying power of pharmaceutical companies

Individual pharmaceutical companies and their trade organization spent approximately $220 million in lobbying in the United States in 2018 10 . Although nations recognize the major problems posed by high prescription drug prices, little has been accomplished in terms of regulatory or legislative reform because of the lobbying power of the pharmaceutical and healthcare industry.

Solutions: global policy changes

There are no easy solutions to the problem of high drug prices. The underlying reasons are complex; some are unique to the United States compared with the rest of the world (Table 1 ).

Patent reform

One of the main ways to limit the problem posed by monopoly is to limit the duration of patent protection. Current patent protections are too long, and companies apply for multiple new patents on the same drug in order to prolong monopoly. We need to reform the patent system to prevent overpatenting and patent abuse 11 . Stiff penalties are needed to prevent “pay-for-delay” schemes where generic competitors are paid money to delay market entry 12 . Patent life should be fixed, and not exceed 7–10 years from the date of first entry into the market (one-and-done approach) 13 . These measures will greatly stimulate generic and biosimilar competition.

Faster approval of generics and biosimilars

The approval process for generics and biosimilars must be simplified. A reciprocal regulatory approval process among Western European countries, the United States, Canada, and possibly other developed countries, can greatly reduce the redundancies 14 . In such a system, prescription drugs approved in one member country can automatically be granted regulatory approval in the others, greatly simplifying the regulatory process. This requires the type of trust, shared standards, and cooperation that we currently have with visa-free travel and trusted traveler programs 6 .

For complex biologic products, such as insulin, it is impossible to make the identical product 15 . The term “biosimilars” is used (instead of “generics”) for products that are almost identical in composition, pharmacologic properties, and clinical effects. Biosimilar approval process is more cumbersome, and unlike generics requires clinical trials prior to approval. Further impediments to the adoption of biosimilars include reluctance on the part of providers to trust a biosimilar, incentives offered by the manufacturer of the original biologic, and lawsuits to prevent market entry. It is important to educate providers on the safety of biosimilars. A comprehensive strategy to facilitate the timely entry of cost-effective biosimilars can also help lower cost. In the United States, the FDA has approved 23 biosimilars. Success is mixed due to payer arrangements, but when optimized, these can be very successful. For example, in the case of filgrastim, there is over 60% adoption of the biosimilar, with a cost discount of approximately 30–40% 16 .

Nonprofit generic companies

One way of lowering the cost of prescription drugs and to reduce drug shortages is nonprofit generic manufacturing. This can be set up and run by governments, or by nonprofit or philanthropic foundations. A recent example of such an endeavor is Civica Rx, a nonprofit generic company that has been set up in the United States.

Compulsory licensing

Developed countries should be more willing to use compulsory licensing to lower the cost of specific prescription drugs when negotiations with drug manufacturers on reasonable pricing fail or encounter unacceptable delays. This process permitted under the Doha declaration of 2001, allows countries to override patent protection and issue a license to manufacture and distribute a given prescription drug at low cost in the interest of public health.

Solutions: additional policy changes needed in the United States

The cost of prescription drugs in the United States is much higher than in other developed countries. The reasons for these are unique to the United States, and require specific policy changes.

Value-based pricing

Unlike other developed countries, the United States does not negotiate over the price of a new drug based on the value it provides. This is a fundamental problem that allows drugs to be priced at high levels, regardless of the value that they provide. Thus, almost every new cancer drug introduced in the last 3 years has been priced at more than $100,000 per year, with a median price of approximately $150,000 in 2018. The lack of value-based pricing in the United States also has a direct adverse effect on the ability of other countries to negotiate prices with manufacturers . It greatly reduces leverage that individual countries have. Manufacturers can walk away from such negotiations, knowing fully well that they can price the drugs in the United States to compensate. A governmental or a nongovernmental agency, such as the Institute for Clinical and Economic Review (ICER), must be authorized in the United States by law, to set ceiling prices for new drugs based on incremental value, and monitor and approve future price increases. Until this is possible, the alternative solution is to cap prices of lifesaving drugs to an international reference price.

Medicare negotiation

In addition to not having a system for value-based pricing, the United States has specific legislation that actually prohibits the biggest purchaser of oral prescription drugs (Medicare) from directly negotiating with manufacturers. One study found that if Medicare were to negotiate prices to those secured by the Veterans Administration (VA) hospital system, there would be savings of $14.4 billion on just the top 50 dispensed oral drugs 17 .

Cap on price increases

The United States also has a peculiar problem that is not seen in other countries: marked price increases on existing drugs. For example, between 2012 and 2017, the United States spent $6.8 billion solely due to price increases on the existing brand name cancer drugs; in the same period, the rest of the world spent $1.7 billion less due to decreases in the prices of similar drugs 18 . But nothing illustrates this problem better than the price of insulin 19 . One vial of Humalog (insulin lispro), that costs $21 in 1999, is now priced at over $300. On January 1, 2020, drugmakers increased prices on over 250 drugs by approximately 5% 20 . The United States clearly needs state and/or federal legislation to prevent such unjustified price increases 21 .

Remove incentive for more expensive therapy

Doctors in the United States receive a proportionally higher reimbursement for parenteral drugs, including intravenous chemotherapy, for more expensive drugs. This creates a financial incentive to choosing a more expensive drug when there is a choice for a cheaper alternative. We need to reform physician reimbursement to a model where the amount paid for drug administration is fixed, and not proportional to the cost of the drug.

Other reforms

We need transparency on arrangements between middlemen, such as pharmacy-benefit managers (PBMs) and drug manufacturers, and ensure that rebates on drug prices secured by PBMS do not serve as profits, but are rather passed on to patients. Drug approvals should encourage true innovation, and approval of marginally effective drugs with statistically “significant” but clinically unimportant benefits should be discouraged. Importation of prescription drugs for personal use should be legalized. Finally, we need to end direct-to-patient advertising.

Solutions that can be implemented by physicians and physician organizations

Most of the changes discussed above require changes to existing laws and regulations, and physicians and physician organizations should be advocating for these changes. It is disappointing that there is limited advocacy in this regard for changes that can truly have an impact. The close financial relationships of physician and patient organizations with pharmaceutical companies may be preventing us from effective advocacy. We also need to generate specific treatment guidelines that take cost into account. Current guidelines often present a list of acceptable treatment options for a given condition, without clear recommendation that guides patients and physicians to choose the most cost=effective option. Prices of common prescription drugs can vary markedly in the United States, and physicians can help patients by directing them to the pharmacy with the lowest prices using resources such as goodrx.com 22 . Physicians must become more educated on drug prices, and discuss affordability with patients 23 .

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Vincent Rajkumar, S. The high cost of prescription drugs: causes and solutions. Blood Cancer J. 10 , 71 (2020). https://doi.org/10.1038/s41408-020-0338-x

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• Volume 8, Issue 9
• Systematic review of high-cost patients’ characteristics and healthcare utilisation
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• Joost Johan Godert Wammes 1 ,
• Philip J van der Wees 1 ,
• Marit A C Tanke 1 ,
• Gert P Westert 2 ,
• Patrick P T Jeurissen 1
• 1 Radboud University Medical Center, Scientific Center for Quality of Healthcare/Celsus Academy for Sustainable Healthcare , Nijmegen , The Netherlands
• 2 Radboud University Medical Center , Scientific Center for Quality of Healthcare , Nijmegen , The Netherlands
• Correspondence to Mr. Joost Johan Godert Wammes; Joost.Wammes{at}radboudumc.nl

Objectives To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries.

Design Systematic review.

Data sources PubMed and Embase databases were searched until 30 October 2017.

Eligibility criteria and outcomes Our final search was built on three themes: ‘high-cost’, ‘patients’, and ‘cost’ and ‘cost analysis’. We included articles that reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Analyses were limited to studies that covered a broad range of services, across the continuum of care. Andersen’s behavioural model was used to categorise characteristics and determinants into predisposing, enabling and need characteristics.

Results The studies pointed to a high prevalence of multiple (chronic) conditions to explain high-cost patients’ utilisation. Besides, we found a high prevalence of mental illness across all studies and a prevalence higher than 30% in US Medicaid and total population studies. Furthermore, we found that high costs were associated with increasing age but that still more than halve of high-cost patients were younger than 65 years. High costs were associated with higher incomes in the USA but with lower incomes elsewhere. Preventable spending was estimated at maximally 10% of spending. The top-10%, top-5% and top-1% high-cost patients accounted for respectively 68%, 55% and 24% of costs within a given year. Spending persistency varied between 24% and 48%. Finally, we found that no more than 30% of high-cost patients are in their last year of life.

Conclusions High-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.

• high-need high-cost
• integrated delivery of health care
• health care utilization
• health care costs

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https://doi.org/10.1136/bmjopen-2018-023113

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Strengths and limitations of this study

Based on an extensive literature search, this review included 55 studies of high-cost patients’ characteristics and healthcare utilisation.

Andersen’s behavioural model was used to categorise the characteristics of high-cost patients into predisposing, enabling and need characteristics.

Grey literature was not included in our systematic review. However, we identified 55 studies and compared high-cost patients’ characteristics and healthcare utilisation across payers and countries.

We did not assess the quality of the studies because of the methodological diversity of the studies.

Background 

It is widely known that healthcare costs are concentrated among a small group of ‘high-cost’ patients. 1 Although they receive substantial care from multiple sources, critical healthcare needs are unmet and many receive unnecessary and ineffective care. 2–5 This suggests that high-cost patients are a logical group to seek for quality improvement and cost reduction.

Especially in the USA, many providers or insurance plans have pursued this logic and developed programmes for ‘high-need, high-cost patients’. So far, such programmes, including, for example, care coordination and disease management, have had favourable results in quality of care and health outcomes and mixed results in their ability to reduce hospital use and costs. 6 Research has shown that the effectiveness and efficiency of the programmes increase when interventions are targeted to the patients that most likely benefit. 2 7 8 Little is known, however, about variations in clinical characteristics and care-utilisation patterns across payer-defined groups or countries. 9 Such insight in the health requirements of high-cost patients is prerequisite for designing effective policy or programme responses.

We conducted this systematic review to synthesise the literature on high-cost patients’ characteristics and healthcare utilisation. Andersen’s behavioural model (see Methods section) was used to organise the findings. Our analysis was aimed at identifying drivers of costs that matter across payer types and countries. We aimed to inform the development of new interventions and policy, as well as future research in high-cost patients.

Our methodology was based on established guidance for conducting systematic reviews. 10 11 Our main research questions was ‘Who are the most expensive patients, what health care services do they use, what drives these high costs, and what drivers matter across payers and countries?’.

Study selection

A preliminary search in PubMed was conducted to identify key articles and keywords. On the basis of these findings, we developed a search strategy covering the most important terms. We then reshaped the search strategy by consulting an information specialist of our university. The final search was built on three themes: ‘high-cost’, ‘patients’, and ‘cost’ and ‘cost analysis’. The sensitivity of the search was verified with the key articles we found earlier. We searched PubMed and Embase on 30 October 2017. Full details of our search strategy are attached in online  supplementary appendix 1 .

Supplementary file 1

Inclusion and exclusion criteria.

Articles were reviewed by author A using title and abstract to identify potentially eligible studies. Author B verified a random sample of articles to guarantee specificity and sensitivity of the selection process. Only studies from high-income countries—as defined by the World Bank 12— and studies published in 2000 and later were included. Studies not written in English and conference abstracts were excluded. In the second step, titles and abstracts were reviewed by author A to assess whether articles fit within our definition of high-cost patients: the article reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Author B verified a random sample of articles at this selection step. In the third step, full-text articles were retrieved and independently screened by author A and author B for our inclusion criteria. At this step, we aimed for studies covering a broad range of services across the continuum of care at health system level and excluded all studies with a narrow scope of costs (eg, hospital costs and pharmaceutical costs) and all studies with a narrow population base (primarily disease oriented studies, or studies in children). At each step of this selection process, (in-)consistencies were discussed until consensus was reached. On basis of the discussions, the criteria were refined, and the prior selection process was repeated.

Data extraction

A data extraction form was developed by the research team to ensure the approach was consistent with the research question. Author A extracted all data. To guarantee specificity and sensitivity of data extraction, author B and author C both independently extracted the data of five random articles. A meeting was held to discuss (in-)consistencies in extraction results. On basis of this discussion, the data extraction form was refined, and the prior data extraction was repeated. Per article, the following key elements were extracted: author, year, country, definition of high-cost patients, inclusion and exclusion criteria of the study population, cost data used to determine total costs, characteristics of the high-cost patients such as diagnoses, age, gender, ethnicity, determinants for high costs including associated supply side factors (concerning the supply of health services), subpopulations and healthcare use and costs (per subpopulation). We also made a narrative summary of the findings per article (provided in online  supplementary appendix 2 ). To identify the most important medical characteristics, only those diseases with a high prevalence (≥10%) among high-cost patient populations or medical characteristics overrepresented in high-cost populations were extracted. Medical characteristics (prevalent diseases) were categorised and presented at the level of International Statistical Classification of Diseases, 10th Revision (ICD-10) chapters.

Supplementary file 2

Data synthesis.

Andersen’s behavioural model was used to categorise characteristics and determinants for high costs into predisposing, enabling and need characteristics. Andersen’s model assumes that healthcare use is a function of (1) characteristics that predispose people to use or not to use services, although such characteristics are not directly responsible for use (eg, age, gender, education, ethnicity and beliefs); (2) enabling characteristics that facilitate or impede use of services (income/wealth/insurance as ability to pay for services, organisation of service provision and health policy); and (3) needs or conditions that laypeople or healthcare providers recognise as requiring medical treatment. The model also distinguishes between individual and contextual (measured at aggregate level, such as measures of community characteristics) determinants of service use. Andersen hypothesised that the variables would have differential ability to explain care use, depending on the type of service. For example, dental care (and other discretionary services) would be explained by predisposing and enabling characteristics, whereas hospital care would primarily be explained by needs and demographic characteristics. 13 14

We presented all data according to five general categories, including study characteristics, predisposing characteristics, enabling characteristics, need characteristics, and expenditure categories and healthcare utilisation. We presented summary tables of results, extracted central themes and topics from the studies and summarised them narratively. All studies were analysed according to payer and country to identify the most important drivers across settings.

Patient and public involvement

Patients and or public were not involved in the conduct of this study.

General information

Our search strategy resulted in 7905 articles. After first broad eligibility assessment, 767 articles remained. After screening of titles and abstracts, 190 articles remained for full-text screening, from which 55 were ultimately included ( figure 1 ).

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Flow diagram of article selection.

A description of the studies is given in table 1 . The majority of the studies were conducted in the USA (n=42). The remaining studies were conducted in Canada (n=9), Germany (n=1), Denmark (n=1), the Netherlands (n=1) and Taiwan (n=1). All were retrospective cohort studies, and descriptive and logistic regression analysis were the main analytic approaches used. The study period ranged from 6 months to 30 years. The most frequent observation period was 1 year.

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Description of the included studies

A range of definitions for high-cost patients were used, and some studies used more than one definition to distinguish between age groups, between high-cost and very high-cost patients or to study persistently high-cost patients (>1 year high costs). In general, patients belonging to the top-1%, top-5%, top-10% or top-20% of spending were considered high-cost patients.

The study population differed between the studies. We categorised eighteen studies as ‘total population’ studies, including studies in universal insurance schemes (of all ages; nine Canadian studies, one Dutch, one German and one Danish study), studies that combined data of different payers or survey studies. Respectively 9, 7 and 14 studies were among US Medicare, US Medicaid or US commercial populations. The remaining studies compared high-cost patients in multiple US payers or were among US dual eligibles (eligible for both Medicare and Medicaid), US Veterans Affairs (VA) beneficiaries or among elderly in the Taiwanese insurance system. Some studies used additional criteria to determine the population. Age, healthcare use or insurance were most frequently used as secondary condition to determine the population.

In 50 studies, total costs per patient were based on the insurance plan or public programme. In the remaining studies, total costs were based on a survey or identified from a variety of sources.

Predisposing characteristics

Table 2 presents predisposing, enabling and need characteristics associated with high-cost patients. Age was related to high-cost patients in several ways. First, high-cost patients were generally older, and higher age was associated with high costs. This held for each payer type. Second, persistently high-cost patients were generally older than episodic high-cost patients, and higher ages were associated with persistently high costs. Third, the magnitude of cost concentration and the threshold for high costs differed between age groups. 15 As younger groups are generally healthier, costs are concentrated among fewer individuals. Fourth, clinical diagnoses and utilisation patterns varied across age groups, 15–17 and some subgroups were related to particular ages, including mental health high-cost patients among younger ages. 18 Finally, although age was related to high costs, total population studies showed that approximately half of the high-cost populations were younger than 65 years. 17 19

Predisposing, enabling and need factors for high-cost patients

Studies showed inconsistent results for gender. Respectively 9 and 16 studies noted males and females were overrepresented in high-cost patients. Besides, gender was associated with different segments of the high-cost population, including males in top-1% or persistently extreme-cost patients, and females in top-2%–5% or persistently high-cost patients, 17 20 or males in mental health high-cost patients. 18

Eleven studies reported the association between ethnicity and high costs. In two Canadian total population studies and three US Medicaid studies, whites were over-represented among high-cost populations, whereas in four US Medicare studies blacks were over-represented.

Socioeconomic status is regarded as both a predisposing characteristic and an enabling characteristic in Andersen’s model, and we found evidence for both relationships. One Canadian study found that high costs were most strongly associated with food insecurity, lower personal income, non-homeownership and living in highly deprived or low ethnic concentration neighbourhoods. 21 Other studies found that social deprivation seemed to increase risk for high costs more than material deprivation. 22

Ganguli et al studied health beliefs among high-cost US Medicare patients: socioeconomic status, social network, patient activation and relationships with and trust in the clinician and the health system all increased or decreased costs, depending on the context. Trust was particularly important and modified the interaction between patient activation and costs: when patients trusted their physicians, patient activation was associated with lower costs. When trust was lacking, patient activation was associated with higher costs. 23

Health behaviours, including underweight, obesity, physical inactivity and former smoking were significantly related to high costs. 24 25

Enabling characteristics

The studies’ abilities to assess the effect of insurance were limited because most study populations were determined by insurance. Nevertheless, the studies indicated that increased insurance may have indicated specific or additional care needs. For example, six US Medicare studies reported that high-cost patients were more likely dually eligible, and four US Medicaid studies reported that certain eligibility statuses were associated with high costs. In addition, increased insurance was associated with high costs because it lowers costs. Two US commercial studies mentioned that high-cost patients were more likely to have a health maintenance organisation plan, a preferred provider organisation plan or comprehensive insurance compared with high-deductible health plans, and insured status was associated with less consideration of costs in decision making. 23

Twelve studies addressed the relationship between income and high costs. In three US studies, higher incomes were associated with high costs, whereas five Canadian studies found that lower incomes were associated with (mental health) high costs. However, one US, one Taiwanese and one Canadian study reported that income was not significantly related to high costs. Finally, among high-cost US Medicare patients, personal resources and education were associated with increased use of resources (higher socioeconomic status (SES) was linked to higher priced care) and also with lower resources use. 23

Organisational enabling factors

The number of primary care physicians, specialists and hospital beds were associated with higher per capita preventable costs among high-cost US Medicare patients. 26 Reschovsky et al 27 found several weak or insignificant relationships between organisational factors and high costs within the high-cost population but found that high-cost US Medicare patients more likely had a medical specialist as usual source of care than a primary care physician or surgeon. Finally, high-cost US Medicare patients were only modestly concentrated in hospitals and markets (they were widely distributed through the system). High concentration hospitals (with relatively many high-cost patients) had a 15% higher median cost per claim, were more likely for-profit and teaching hospitals, had lower nurse-to-patient ratios, were more likely to care for the poor and had higher 30-day readmission rates and lower 30-day mortality rates. High concentration hospital referral regions had higher annual median costs per beneficiary, a larger supply of specialists but equal supply of total physicians, a lower supply of long-term care beds, higher hospital care intensity and higher end-of-life spending. 28

Need characteristics

Medical characteristics of high-cost patients are presented in table 2 . We categorised medical characteristics to ICD-10 chapters. Circulatory diseases, mental and behavioural disorders, endocrine, nutritional and metabolic, diseases of the respiratory system, diseases of the genitourinary system, neoplasms and diseases of the musculoskeletal system and connective tissue were most frequently reported among high-cost patients. The prevalence of chronic disease(s) and multimorbidity were also dominant among high-cost patients. For example, Bynum et al 16 showed that over 26.4% of high-cost US dual eligibles suffered from five or more chronic conditions.

Two studies presented medical characteristics across US payers. Both studies showed that high-cost commercial patients had the lowest numbers of comorbidities and that high-cost Medicaid patients had the highest prevalence of mental illness. 9 29 We further compared the prevalence of diabetes, congestive heart failure, lung disease and mental disorders across the studies. The prevalence of diabetes, congestive heart failure and lung disease was relatively low (≈5%–25%) in US commercial and total population studies. In US Medicaid, the prevalence of congestive heart failure and lung disease were relatively high (≈15%–40%; one study reported a prevalence of diabetes and lung disease >60% 30 ), and the prevalence of mental illness was particularly high (≈30%–75%). In US Medicare, the prevalence of diabetes, congestive heart failure and lung disease were highest (≈20%–55%) and the prevalence of mental illness more modest (≈10%–25%). In total populations, approximately 30%–40% of high-cost patients were treated for mental illness. Besides, the prevalence of each of the chronic diseases in the Dutch study was comparable with the prevalence in other total population studies. Finally, persistent high-cost patients had a higher number of comorbidities and a higher prevalence of each of the diseases compared with episodic high-cost patients.

High-cost patients were more likely to die, and those in the process of dying were more likely to incur high costs. The mortality differed between payers, much less between countries. The mortality among Danish and Dutch high-cost patients was comparable with the mortality in other total population studies. In US Medicare studies, the mortality ranged from 14.2% to 27.4%, compared with 11.7% in one US Medicaid study and 5%–13% in total populations. In addition, top-1% patients were more likely to die compared with top-5% patients, 17 31 and persistent high-cost patients were more likely to die than episodic high-cost patients. 32 Finally, among US dual eligibles, mortality varied much across age and residence groups; nearly half of dual eligibles aged 65 years and older died. 16

Expenditure patterns and healthcare utilisation

In each study, costs were heavily concentrated. The top-10% patients roughly accounted for about 68% of costs (range: 55%–77%), the top-5% patients accounted for about 55% of costs (range: 29%–65%) and top-1% patients for approximately 24% (range: 14%–33%) within a given year. Costs were generally less concentrated in US Medicare and more concentrated in total populations.

A wide range of parameters were used to describe high-cost patients’ healthcare utilisation ( table 3 ). Inpatient acute hospital care was most often reported as a primary expenditure category for high-cost patients. In line with this, 17 studies reported hospitalisations, admissions or inpatient days as important cost drivers. Lieberman found that total spending per beneficiary correlated strongly with the use of inpatient services, 33 likewise several studies found that increasing levels of use (ie, top-1% compared with top-5%) were associated with increasing proportions of spending on (inpatient) hospital care. 15 17 23 24 34 35 Guo et al 36 reported that high-cost users consumed more units of each of the service category analysed, with the exception of laboratory tests; these findings were confirmed elsewhere. 35 37 In addition, it was found that 91% of high-cost patients received care in multiple care types. 38 Mental care services were listed as expenditure category only in studies of total populations, US Medicaid and US VA. Finally, one study determined the frequency use of expensive services among high-cost patients: expensive treatments (expensive drugs, intensive care unit treatment, dialysis, transplant care, and Diagnosis Related Groups >€30 000) contributed to high cost in approximately one-third of top-1% patients and in less than 10% of top-2%–5% patients. 17

Expenditure patterns and utilisation of high-cost patients

Four studies quantified the amount of ‘preventable’ spending (based on preventable emergency department visits and preventable (re-)admissions) among high-cost patients. As shown above, various supply side characteristics were associated with higher preventable costs among high-cost US Medicare patients, and approximately 10% of total costs were preventable. 26 Another study found that 4.8% of US Medicare spending was preventable and that high-cost patients accounted for 73.8% of preventable spending. Moreover, 43.8% of preventable spending was accounted for by frail elderly, and preventable spending was particularly high for heart failure, pneumonia, chronic obstructive pulmonary disease/asthma and urinary tract infections. 39 Figueroa et al 30 found that preventable spending differed by insurance type among US non-elderly: 3.5%, 2.8%, and 1.4% of spending were preventable among US Medicaid, US Medicaid managed care and privately insured high-cost patients, respectively. Similarly, Graven et al 29 found that proportions of preventable spending differed between payers and that persistent high-cost patients had higher proportions of preventable spending.

Twenty-one studies reported on the persistency of high costs. We found three approaches for studying persistency. First, studies reported prior healthcare use and/or reported posterior healthcare use for patients with high costs in a given index year. In other studies, persistent high-cost patients were compared with episodic high-cost patients. Spending persistency varied between 24% and 48% for top-5% patients, and between 28% and 45% for top-10% patients. Spending persistence was relatively high in US Medicaid and relatively low in US Medicare. Increasing persistence was associated with increasing expenditures on all service types. 37

We reviewed 55 studies on high-cost patients’ characteristics and healthcare utilisation and made comparisons across payers and countries. The studies consistently point to a high prevalence of multiple (chronic) conditions to explain high-cost patients’ utilisation. Besides, we found a high prevalence of mental illness across all the studies, most notably in US Medicaid and total population studies. We found that various health system characteristics may contribute to high costs. Preventable spending was estimated at maximally 10% of spending. Furthermore, we found that high costs are associated with increasing age and that clinical diagnoses and utilisation patterns varied across age groups. However, still more than half of high-cost patients are younger than 65 years. High costs were associated with higher incomes in the USA, but with lower incomes elsewhere. Finally, we confirmed that high-cost patients are more likely to die, and decedents are more likely to incur high-costs. However, no more than 30% of high-cost patients were in their last year of life.

Strengths and weaknesses

This is the first systematic review of scientific literature on high-cost patients’ characteristics and healthcare utilisation. Future studies might consider inclusion of grey literature. We included studies of various payer types and countries, allowing comparisons across settings. However, most studies were conducted in the USA and Canada, which limits the generalisability of the findings. Although our comparison across countries did not reveal large differences in mortality or prevalence of common chronic diseases, these analyses were based on a limited number of variables, studies and countries. It is likely that the specific characteristics and utilisation of high-cost patients vary across localisations due to a wide range of epidemiological and health system factors. One limitation is that we, because of methodological diversity, did not assess the quality of the included studies, and some studies by design did not control for confounding. To our knowledge, no agreed on framework exists for risk of bias assessment of the kind of studies included in our review. One limitation in current frameworks for observation/cross-sectional studies is that these are primarily designed for studies that aim to assess intervention effects in comparative studies. The internal validity of the findings in our included studies is mainly contingent on its ability to control for relevant confounders. However, no consensus exists about what factors should reasonably be controlled for. The external validity of the findings of each of the studies depend on the breadth of the population studied and the scope of the costs considered for establishing total costs. Our study selection process was aimed at identifying studies with a broad population studies and a wide range of costs considered. Finally, the studies used various approaches for defining the needs and measuring multimorbidity among their populations, which limits the comparability across studies.

Reflections on our findings

Current research in high-cost patients has focused on care redesign of the treatment of patients with multiple chronic morbidities. 7 40 One contribution of our review is our identification of notable differences in characteristics and utilisation across payers and countries. This (clinical) diversity of high-cost patients may even be larger at a local level. Segmentation analysis has been suggested as a method to identify homogenous and meaningful segments of patients with similar characteristics, needs and behaviour, which allows for tailored policy. 41 Such segmentation analysis may powerfully inform population health management initiatives. Given the multiple needs and cross-sectoral utilisation of high-cost patients, we suggest such analyses should capture both characteristics and utilisation as broadly as possible, to fully apprehend high-cost patients care needs and utilisation. In the context of high-cost patients, multimorbidity complicates segmentation, and the usefulness of segmentation may depend on the way multimorbidity is dealt with. To illustrate a potent example, Hayes et al 42 defined high-need, high-cost patients as ‘people having three or more chronic conditions and a functional limitation that makes it hard for them to perform basic daily tasks’.

Our findings also reveal several supply-side factors that contribute to high costs. However, no firm conclusions can be drawn about the strength of these effects. The apparent limited impact of organisational factors on spending is in line with Andersen’s model predictions, where multimorbidity and health status are prime determinants of healthcare costs. 43 However, such findings are surprising given the abundance of evidence for supplier induced demand and medical practice variation. 44 High-cost populations may be too diverse for studying the impact of organisational factors; for such studies, more homogenous populations may be prerequisite.

Four of our included studies estimated the amount of ‘preventable’ spending among high-cost patients. Preventable spending was estimated at maximally 10% of spending, which is relatively low compared with the amounts of savings that have been reported elsewhere. 8 Preventable spending was mainly defined as preventable emergency department visits or preventable (re-)admissions, as such echoing the two primary targets of most high-need high-cost programmes, including care coordination and disease management. The algorithms used were said to be relatively narrow and could have included other diagnostic categories. 29 Besides, future studies might consider more broad measures of preventable or wasteful spending and develop algorithms to identify duplicate services, contraindicated care, unnecessary laboratory testing, unnecessary prolonged hospitalisations or any other kinds of lower value services.

It was striking that three US studies reported that higher incomes were associated with high costs, whereas other studies found that lower incomes were associated with high costs. These findings may point to disparities in health, the price that some Americans pay for their care and the reduced accessibility to care of low-income patients. This may particularly hold for the uninsured. Besides, these findings suggest tailored interventions for lower income patients may be worthwhile.

Policy and research implications

Based on our findings, we deduced four major segments of high-cost patients for which separate policy may be warranted, including patients in their last year of life, patients experiencing a significant health event who return to stable health (episodically high-cost patients), patients with mental illness and patients with persistently high costs characterised by chronic conditions, functional limitations and elder age.

Many interventions have been taken to increase value of end-of-life care. Advance care planning has shown to increase the quality of end-of-life care and decrease costs. 45–47 In addition, health systems might consider strengthening their palliative care systems. 48 Increasing value for episodically high-cost patients requires appropriate pricing of procedures and drugs, for example, through selective contracting of providers, reference pricing or competitive bidding. 49 In addition, bundled payments for procedures and associated care may improve care coordination and reduce the use of duplicative or unnecessary services. 50 Multidisciplinary needs assessment and shared decision making may reduce unwarranted variation in expensive procedures. Mental health high-cost patients are known for their medical comorbidities, which suggests these patients might benefit from multidisciplinary cross-sectoral healthcare delivery, for example, through collaborative care. 51 52 Finally, persistent high-cost patients might benefit from a variety of models, including disease management, care coordination or ambulatory intensive care units, depending on the needs of the population and local circumstances. 8 53–55 Especially population health management approaches may be beneficial for these populations. Sherry et al recently examined five community-oriented programmes that successfully improved care for high-need, high-cost patients. The five programmes shared common attributes, including a ‘whole person’ orientation, shared leadership, flexible financing and shared cross-system governance structures. 56

One study addressed health beliefs and patient networks among high-cost patients. 23 More of such research is needed as health beliefs may be more amenable to change than other drivers of high costs. One study analysed the use of expensive treatments by high-cost patients. 17 Better insight in such healthcare utilisation patterns is needed to inform interventions and policy aimed at high-cost populations. There is a need for segmentation variables and logic that is informative at either microlevel, mesolevel and macrolevel. More research is needed to identify determinants of preventable and wasteful spending.

In conclusion, high-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.

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Contributors JJGW drafted the first manuscript and conducted the analyses. JJGW and PJvdW selected eligible studies. JJGW, PJvdW and MACT conceptualised the study and interpreted the data. GPW and PPTJ made a substantial contribution to the development of the research question and interpretation and presentation of the findings. All authors provided feedback to and approved the final manuscript.

Funding The study was conducted as part of a research program funded through the Dutch Ministry of Health.

Disclaimer The funding source had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the manuscript for publication.

Competing interests None declared.

Patient consent None required.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement Detailed forms with extracted data are available from the authors upon request.

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The High Cost of Prescription Drugs in the United States : Origins and Prospects for Reform

• 1 Program On Regulation, Therapeutics, And Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
• Comment & Response Factors Influencing Prescription Drug Costs in the United States Jack L. Arbiser, MD, PhD JAMA
• Comment & Response Factors Influencing Prescription Drug Costs in the United States Victor Roy, MPhil; Luke Hawksbee, MPhil; Lawrence King, PhD JAMA
• Comment & Response Factors Influencing Prescription Drug Costs in the United States—Reply Ameet Sarpatwari, JD, PhD; Jerry Avorn, MD; Aaron S. Kesselheim, MD, JD, MPH JAMA
• Original Investigation Changes in List Prices, Net Prices, and Discounts for Branded Drugs in the US, 2007-2018 Inmaculada Hernandez, PharmD, PhD; Alvaro San-Juan-Rodriguez, PharmD; Chester B. Good, MD, MPH; Walid F. Gellad, MD, MPH JAMA
• Viewpoint A Court Decision on “Skinny Labeling”: Another Challenge for Less Expensive Drugs Bryan S. Walsh, JD; Doni Bloomfield, JD; Aaron S. Kesselheim, MD, JD, MPH JAMA

Importance   The increasing cost of prescription drugs in the United States has become a source of concern for patients, prescribers, payers, and policy makers.

Objectives   To review the origins and effects of high drug prices in the US market and to consider policy options that could contain the cost of prescription drugs.

Evidence   We reviewed the peer-reviewed medical and health policy literature from January 2005 to July 2016 for articles addressing the sources of drug prices in the United States, the justifications and consequences of high prices, and possible solutions.

Findings   Per capita prescription drug spending in the United States exceeds that in all other countries, largely driven by brand-name drug prices that have been increasing in recent years at rates far beyond the consumer price index. In 2013, per capita spending on prescription drugs was $858 compared with an average of $400 for 19 other industrialized nations. In the United States, prescription medications now comprise an estimated 17% of overall personal health care services. The most important factor that allows manufacturers to set high drug prices is market exclusivity, protected by monopoly rights awarded upon Food and Drug Administration approval and by patents. The availability of generic drugs after this exclusivity period is the main means of reducing prices in the United States, but access to them may be delayed by numerous business and legal strategies. The primary counterweight against excessive pricing during market exclusivity is the negotiating power of the payer, which is currently constrained by several factors, including the requirement that most government drug payment plans cover nearly all products. Another key contributor to drug spending is physician prescribing choices when comparable alternatives are available at different costs. Although prices are often justified by the high cost of drug development, there is no evidence of an association between research and development costs and prices; rather, prescription drugs are priced in the United States primarily on the basis of what the market will bear.

Conclusions and Relevance   High drug prices are the result of the approach the United States has taken to granting government-protected monopolies to drug manufacturers, combined with coverage requirements imposed on government-funded drug benefits. The most realistic short-term strategies to address high prices include enforcing more stringent requirements for the award and extension of exclusivity rights; enhancing competition by ensuring timely generic drug availability; providing greater opportunities for meaningful price negotiation by governmental payers; generating more evidence about comparative cost-effectiveness of therapeutic alternatives; and more effectively educating patients, prescribers, payers, and policy makers about these choices.

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Kesselheim AS , Avorn J , Sarpatwari A. The High Cost of Prescription Drugs in the United States : Origins and Prospects for Reform . JAMA. 2016;316(8):858–871. doi:10.1001/jama.2016.11237

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• Issue Brief

Health Care Costs: What’s the Problem?

The cost of health care in the United States far exceeds that in other wealthy nations across the globe. In 2020, U.S. health care costs grew 9.7%, to $4.1 trillion, reaching about $12,530 per person. 1 At the same time, the United States lags far behind other high-income countries when it comes to both access to care and some health care outcomes. 2 As a result, policymakers and health care systems are facing increasing demands for more care at lower costs for more people. And, of course, everyone wants to know why their health care costs are so high.

The answer depends, in part, on who’s asking this question: Why does U.S. health care cost so much? Public policy often highlights and targets the total cost of the health care system or spending as a percentage of the gross domestic product (GDP), while most patients (the public) are more concerned with their own out-of-pocket costs and whether they have access to affordable, meaningful insurance. Providers feel public pressure to contain costs while trying to provide the highest-quality care to patients.

This brief is the first in a series of papers intended to better define some of the key questions policymakers should be asking about health care spending: What costs are too high? And can they be controlled through policy while improving access to care and the health of the population?

What (or Who) Is to Blame for the High Costs of Care? 

Total U.S. health care spending has increased steadily for decades, as have costs and spending in other segments of the U.S. economy. In 2020, health care spending was $1.5 trillion more than in 2010 and $2.8 trillion more than in 2000. While total spending on clinical care has increased in the past two decades, health care spending as a percentage of GDP has remained steady and has hovered around 20% of GDP in recent years (with the largest single increase being in 2020 during the COVID-19 pandemic). 1 Health care spending in 2020 (particularly public outlays) increased more than in previous years because of increased federal government support of critical COVID-19-related services and expanded access to care during the pandemic. Yet, no single sector’s health care cost — doctors, hospitals, equipment, or any other sector — has increased disproportionately enough over time to be the single cause of high costs.

One of the areas in health care with the highest levels of spending in the United States is hospital care, which has accounted for about 30% of national health care spending 3 for the past 60 years (and has remained very close to 31% for the past 20 years) (Figure 1). Although hospital spending is the focus of many cost-control policies and public attention, the increases are consistent with the increases seen across other areas of health care, such as for physicians and other professional services. Total spending for some smaller parts of nonhospital care has more than doubled over the past few decades and makes up an increasing proportion of total spending. For instance, home health care as a percentage of total spending tripled between 1980 and 2020, from 0.9% to 3.0%, and drug spending nearly doubled as a proportion of health care spending between 1980 and 2006, from 4.8% to 10.5%, and currently represent 8.4% of health care spending. 1  

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The largest areas of spending that might yield the greatest potential for savings — such as inpatient care and physician-provided care — are unlikely to be reduced by lowering the total number of insured patients or visits per person, given the growing, aging U.S. population and the desire to cover more, not fewer, individuals with adequate health insurance. 

In the past decade, policymaker and insurer interventions intended to change the mix of services by keeping patients out of high-cost settings (such as the hospital) have not always succeeded at reducing costs, although they have had other benefits for patients. 4  

Breaking Down the Costs of Care

Thinking about total health care spending as an equation, one might define it as the number of services delivered per person multiplied by the number of people to whom services are delivered, multiplied again by the average cost of each service: 

Health Care Spending=(number of services delivered per person)×(number of people to whom services are delivered)×(average cost of each service) 

Could health care spending be lowered by making major changes to the numbers or types of services delivered or by lowering the average cost per service? 

Although recent data on the overall utilization of health care are limited, in 2011, the number of doctor consultations per capita in the United States was below that in many comparable countries, but the number of diagnostic procedures (such as imaging) per capita remained higher. 5 Furthermore, no identifiable groups of individuals (by race/ethnicity, geographic location, etc.) appear to be outliers that consume extraordinary numbers of services. 6 The exception is that the sickest people do cost more to take care of, but even the most cost-conscious policymakers appear to be reluctant to abandon these patients. 

In addition to the fact that the average number of health care services delivered per person in the United States was below international benchmarks in 2020,7 the percentage of people in the United States covered by health insurance was also lower than that in many other wealthy nations. Although millions of people gained insurance8 through the Affordable Care Act and provisions enacted during the COVID-19 pandemic, 10% of the nonelderly population remained uninsured in 2020. 9 When policymakers focus on reducing health care spending, considering the equation above, and see that the United States already has a lower proportion of its population insured and fewer services delivered to patients than other wealthy nations, their focus often shifts to the average cost of services.

It's Still the Prices … and the Wages 

A report comparing the international prices of health care in 2017 found that the median list prices (charges) for medical procedures in the United States heavily outweighed the list prices in other countries, such as the United Kingdom, New Zealand, Australia, Switzerland, and South Africa. 10  

For example, the 2017 U.S. median health care list price for a hospital admission with a hip replacement was $32,500, compared with $20,900 in Australia and $12,200 in the United Kingdom. In comparisons of the list prices of other procedures, such as deliveries by cesarean section, appendectomies, and knee replacements, the U.S. median list prices of elective and needed services were thousands of dollars — if not tens of thousands of dollars — more. 10 Yet, the list price for these services in the United States is often much higher than the actual payments made to providers by public or private insurance companies. 11

Public-payer programs (particularly Medicare and Medicaid) tend to pay hospitals rates that are lower than the cost of delivering care12 (though many economists argue these payments are slightly above actual costs, and providers argue they are at least slightly below actual costs), while private payers historically have paid about twice as much as public payers. 13 (See another brief in this series, “ Surprise! Why Medical Bills Are Still a Problem for U.S. Health Care ,” for more information about public and private payers’ role in health care costs.) However, the average cost per service is still high by international standards, even if it’s not as high as list prices may suggest. The high average costs are partially driven by the highly labor-intensive nature of health care, with labor consuming almost 55% of the share of total U.S. hospital costs in 2018. 14 These costs are growing due to the labor shortages exacerbated by the COVID-19 pandemic. 

Reducing U.S. health care spending by reducing labor costs could, theoretically, be achieved by reducing wages or eliminating positions; however, both of those policies would be problematic, with potential unintended consequences, such as driving clinicians away from the workforce at a time of growing need. 

Wage reductions, particularly for clinicians, would require a vastly expanded labor pool that would take years to achieve (and even then, lower per person wages for nonphysicians may not decrease total spending related to health care labor). 15 Reducing or replacing clinical workers over time would require major changes to policy (both public and private) and major shifts in how health care is provided — neither of which has occurred rapidly, even since the implementation of the Affordable Care Act. 

What’s a Policymaker to Do?

Nearly one in five Americans has medical debt, 16 and affordability is still an issue for a large proportion of the population, whether uninsured or insured, which suggests that policymakers should focus on patients’ costs. This may prove more impactful to the individual than reducing total health care spending. 

A majority of the country agrees that the federal government should ensure some basic health insurance for all citizens. 17,18 Although most Americans consider reducing costs to individuals and expanding insurance coverage to be important, no clear consensus about who should bear any associated increased costs exists among patients or policymakers. Half of insured adults currently report difficulty affording medical or dental care, even when they are insured, because of the rising total costs of care and the increasing absolute amount of out-of-pocket spending. 19 Out-of-pocket spending for health care has doubled in the past 20 years, from $193.5 billion in 2000 to $388.6 billion in 2020. 1 These rising health care costs have disproportionately fallen on those with the fewest resources, including people who are uninsured, Black people, Hispanic people, and families with low incomes. 19 Increased cost sharing through copays and coinsurance may force difficult spending choices for even solidly middle-class families. 

The severity and burden of out-of-pocket spending are hidden by the use of data averages; on average, U.S. residents have twice the average household net adjusted disposable income 20 of many other comparable nations and spend more than twice 21 as much per capita on health care. Yet, for those who fall outside these averages — average income, average costs, or both — the financial pain felt at the hospital, clinic, and pharmacy is very real. 

In any given year, a small number of patients account for a disproportionate amount of health care spending because of the complexity and severity of their illnesses. Even careful international comparisons of end-of-life care for cancer patients demonstrate costs in the United States are similar to those in many comparable nations (although U.S. patients are more likely to receive chemotherapy, they spend fewer days in the hospital during the last 6 months of life than patients in other countries). 22 Similarly, although prevention efforts may delay or avoid the onset of illness in targeted populations, such efforts would not significantly reduce the number of services delivered for many years and may lead to an increase in care delivered over the course of an extended life span.

To the average person in the United States, immediate cost-control efforts might best be focused on reducing the cost burden for families and patients. Policymakers should continue to seek ways to promote better health care quality at lower costs rather than try to achieve unrealistic, drastic reductions in national health care spending. Investing in prevention, seeking to avoid preventable admissions or readmissions, and otherwise improving the quality of care are desirable, but these improvements are not quick solutions to lowering the national health care costs in the near term. Long-term policy actions could incrementally address health care spending but should clearly articulate the problem to be solved, the desired outcomes, and the trade-offs the nation is willing to make (as discussed in two companion pieces). 

The U.S. health care system continues to place a disproportionate cost burden on the patients who can least afford it. In the short term, policymakers could focus on targeted subsidies to specific populations — the families and individuals whose household incomes fall outside the average or who have health care expenses that fall outside the average — whose health care costs are unmanageable. Such subsidies could expand existing premium subsidies or triggers that increase support for costs that exceed target amounts. Targeted subsidies are likely to increase total health care spending (especially public spending) but would address the problem of cost from the average consumer, or patient, perspective. Broader policies to ease costs for patients could also be considered by category of service; for instance, consumers have been largely shielded from the increased costs of care related to COVID-19 by the waiving of copays for patients and families. These policies would likely increase national spending as well, but they would make medical care more affordable to some families.

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Cite this source: Grover A, Orgera K, Pincus L. Health Care Costs: What's The Problem? Washington, DC: AAMC; 2022. https://doi.org/10.15766/rai_dozyvvh2

• Centers for Medicare & Medicaid Services. National Health Expenditure Data. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet . Published Dec. 1, 2021. Accessed Feb. 24, 2022.
• Schneider EC, Shah A, Doty MM, Tikkanen R, Fields K, Williams RD II. Mirror, Mirror 2021 — Reflecting Poorly: Health Care in the U.S. Compared to Other High-Income Countries. Washington, DC: The Commonwealth Fund. https://doi.org/10.26099/01DV-H208 . Published August 2021. Accessed April 21, 2022.
• Centers for Medicare & Medicaid Services. National Health Expenditure Data: Historical. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical . Published Dec. 15, 2021. Accessed April 22, 2022. 
• Berkowitz S, Ricks KB, Wang J, Parker M, Rimal R, DeWalt D. Evaluating a nonemergency medical transportation benefit for accountable care organization members. Health Affairs. 2022;41(3):406-413. doi:10.1377/hlthaff.2021.00449.
• Organisation for Economic Co-operation and Development. Health Care Utilisation. Paris, France: Organisation for Economic Co-operation and Development. https://stats.oecd.org/index.aspx?queryid=30166# . Published Nov. 9, 2021. Accessed Feb. 24, 2022.
• Abelson R. Harris G. Critics question study cited in health debate. New York Times. June 2, 2010. https://www.nytimes.com/2010/06/03/business/03dartmouth.html?ref=business&pagewanted=all . Accessed Feb. 24, 2022.
• The Commonwealth Fund. Selected Health & System Statistics: Average Annual Number of Physician Visits per Capita. https://www.commonwealthfund.org/international-health-policy-center/system-stats/annual-physician-visits-per-capita . Published June 5, 2020. Accessed April 21, 2022.
• Tolbert J, Orgera K. Key Facts About the Uninsured Population. San Francisco, CA: KFF. https://www.kff.org/uninsured/issue-brief/key-facts-about-the-uninsured-population/ . Published Nov. 6, 2020. Accessed April 21, 2022.
• Tolbert J, Orgera K, Damico A. What Does the CPS Tell Us About Health Insurance Coverage in 2020? San Francisco, CA: KFF. https://www.kff.org/uninsured/issue-brief/what-does-the-cps-tell-us-about-health-insurance-coverage… . Published Sept. 23, 2021. Accessed April 21, 2022.
• Hargraves J, Bloschichak A. International Comparisons of Health Care Prices From the 2017 iFHP Survey. Washington DC: Health Care Cost Institute. https://healthcostinstitute.org/hcci-research/international-comparisons-of-health-care-prices-2017-ifhp-survey . Published Dec. 2019. Accessed April 21, 2022.
• Bai G. Anderson G. Extreme markup: The fifty US hospitals with the highest charge-to-cost ratios. Health Affairs. 2015;34(6):922-928. doi:10.1377/hlthaff.2014.1414.
• Congressional Budget Office. The Prices That Commercial Health Insurers and Medicare Pay for Hospitals’ and Physicians’ Services. Washington, DC: Congressional Budget Office. https://www.cbo.gov/system/files/2022-01/57422-medical-prices.pdf . Published January 2022. Accessed April 21, 2022.
• Lopez E, Neuman T, Jacobson G, Levitt L. How Much More Than Medicare Do Private Insurers Pay? A Review of the Literature. San Francisco, CA: The Henry J. Kaiser Family Foundation. https://www.kff.org/medicare/issue-brief/how-much-more-than-medicare-do-private-insurers-pay-a-review-of-the-literature/ . Published April 15, 2020. Accessed March 22, 2022.
• Daly R. Hospitals Innovate to Control Labor Costs. Westchester, IL: Healthcare Financial Management Association. https://www.hfma.org/topics/hfm/2019/october/hospitals-innovate-to-control-labor-costs.html . Published Oct. 1, 2019. Accessed Feb. 24, 2022. 
• Batson BN, Crosby SN, Fitzpatrick, JM. Mississippi frontline: Targeting value-based care with physician-led care teams. J Miss State Med Assoc. 2022;63(1):19-21. https://ejournal.msmaonline.com/publication/?m=63060&i=735364&p=20&ver=html5 .
• Kluender R, Mahoney N, Wong F, et al. Medical debt in the US, 2009-2020. JAMA. 2021;326(3):250-256. doi:10.1001/jama.2021.8694.
• Jones B. Increasing Share of Americans Favor a Single Government Program to Provide Health Care Coverage. Washington, DC: Pew Research Center. https://www.pewresearch.org/fact-tank/2020/09/29/increasing-share-of-americans-favor-a-single-government-program-to-provide-health-care-coverage/ . Published Sept. 29, 2020. Accessed April 21, 2022.
• Bialik K. More Americans Say Government Should Ensure Health Care Coverage. Washington, DC: Pew Research Center. https://www.pewresearch.org/fact-tank/2017/01/13/more-americans-say-government-should-ensure-health-care-coverage/ . Published Jan. 13, 2017. Accessed March 22, 2022.
• Kearney A, Hamel L, Stokes M, Brodie M. Americans’ Challenges With Health Care Costs. San Francisco, CA: The Henry J. Kaiser Family Foundation. https://www.kff.org/health-costs/issue-brief/americans-challenges-with-health-care-costs/ . Published Dec. 14, 2021. Accessed Feb. 24, 2022.
• Organisation for Economic Co-operation and Development. Income. Better Life Index. Paris, France: Organisation for Economic Co-operation and Development. https://www.oecdbetterlifeindex.org/topics/income/ . Accessed April 21, 2022.
• Wager E, Ortaliza J, Cox C; The Henry J. Kaiser Family Foundation. Health System Tracker. How Does Health Spending in the U.S. Compare to Other Countries? San Francisco, CA: The Henry J. Kaiser Family Foundation. https://www.healthsystemtracker.org/chart-collection/health-spending-u-s-compare-countries-2/ . Published Jan. 21, 2022. Accessed April 21, 2022.
• Bekelman JE, Halpern SD, Blankart CR, et al. Comparison of site of death, health care utilization, and hospital expenditures for patients dying with cancer in 7 developed countries. JAMA. 2016;315(3):272-283. doi:10.1001/jama.2015.18603.

Does High Cost-Sharing Slow the Long-term Growth Rate of Health Spending? Evidence from the States

Research has shown that higher cost-sharing lowers health care spending levels but less is known about whether cost-sharing also affects spending growth. From 2002 to 2016, private insurance deductibles more than tripled in magnitude. We use data from the Centers for Medicare and Medicaid Services and the Agency for Healthcare Research and Quality to estimate whether areas with relatively higher deductibles experienced lower spending growth during this period. We leverage panel variation in private deductibles across states and over time and address the potential endogeneity of deductibles using instrumental variables. We find that spending growth is significantly lower in states with higher average deductibles and observe this relationship with regard to both private insurance benefits and total spending (including Medicare and Medicaid), suggestive of potential spillovers. We hypothesize that the impact on spending growth happens because deductibles affect the diffusion of costly new technology.

We thank Claudio Lucarelli and Sarah Dykstra for thoughtful comments and suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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• October 9, 2018

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Healthcare Expenditure and Economic Performance: Insights From the United States Data

Viju raghupathi.

1 Koppelman School of Business, Brooklyn College of the City University of New York, Brooklyn, NY, United States

Wullianallur Raghupathi

2 Gabelli School of Business, Fordham University, New York, NY, United States

Associated Data

Publicly available datasets were analyzed in this study. These can be found here: CMS; BEA; BLS; https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData ; https://www.bea.gov/iTable/iTable.cfm?reqid=70&step=1&isuri=1&acrdn=2#reqid=70&step=1&isuri=1 ; https://www.bls.gov/lpc/data.htm ; https://www.bls.gov/webapps/legacy/tusa_1tab1.htm .

This research explores the association of public health expenditure with economic performance across the United States. Healthcare expenditure can result in better provision of health opportunities, which can strengthen human capital and improve the productivity, thereby contributing to economic performance. It is therefore important to assess the phenomenon of healthcare spending in a country. Using visual analytics, we collected economic and health data from the Bureau of Economic Analysis and the Bureau of Labor Statistics for the years 2003–2014. The overall results strongly suggest a positive correlation between healthcare expenditure and the economic indicators of income, GDP, and labor productivity. While healthcare expenditure is negatively associated with multi-factor productivity, it is positively associated with the indicators of labor productivity, personal spending, and GDP. The study shows that an increase in healthcare expenditure has a positive relationship with economic performance. There are also variations across states that justify further research. Building on this and prior research, policy implications include that the good health of citizens indeed results in overall better economy. Therefore, investing carefully in various healthcare aspects would boost income, GDP, and productivity, and alleviate poverty. In light of these potential benefits, universal access to healthcare is something that warrants further research. Also, research can be done in countries with single-payer systems to see if a link to productivity exists there. The results support arguments against our current healthcare system's structure in a limited way.

Introduction and Background

Healthcare spending and the impact that it has on economic performance are important considerations in an economy. Some studies have shown that improvements in health can lead to an increase in Gross Domestic Product (GDP) and vice versa ( 1 – 3 ). Healthcare holds a significant place in the quality of human capital. The increased expenditure in healthcare increases the productivity of human capital, thus making a positive contribution to economic growth ( 4 , 5 ). However, there is ongoing debate on what kinds of healthcare spending and what level of optimal spending is beneficial for economic development ( 6 – 8 ).

The theory of welfare economics is relevant to the current research. Welfare economics is a branch that deals with economic and social welfare by analyzing how the resources of the economy are allocated among the social agents ( 9 , 10 ). Here, we analyze the allocation of resources in terms of spending within the healthcare sector and assess its influence on economic welfare. In addition to this, we draw from several related studies in laying a strong foundation for our research. The relationship between health and economic growth has been examined extensively across multiple studies ( 11 – 16 ). Based on a study that examined the impact of health on economic growth in developing countries, it was evident that a decrease in birth rates positively affected economic growth ( 17 ). During the period of study, health expenditures rose threefold, from $83M to $286M, and outpaced growth in GDP. The study showed that health and income mutually affected each other and concluded that problems affecting healthcare delivery caused negative impact on economic growth ( 18 ). Arora ( 19 ) investigated the effects of health on economic growth for industrialized countries and found a strong association. In a study of the impact of health indicators for the period 1965–1990 for developed and developing countries, economic performance in developing countries increased significantly with an improvement in public health ( 20 ). Studies have proposed that an annual improvement of 1 year in life expectancy increases economic growth by 4% ( 1 , 21 ). Similarly, another study in 2001 emphasized that the existence of a healthy population may be more important than education, for human capital in the long term ( 22 ). Examining 21 African countries for the 1961–1995 period and 23 Organization for Economic Cooperation and Development (OECD) countries for the 1975–1994 period with the extended Solow growth model, authors found that 23 OECD health stocks affect growth rate of per capita income ( 23 ). Muysken ( 24 ) also investigated whether health is one of the determinants of economic growth and concluded that an iterative relationship exists between economic growth and health—high economic growth leads to investments in human capital and to health advancement, and good population health leads to more labor productivity and economic growth. Aghion et al. ( 25 ) utilized the Schumpeterian growth theory to analyze channels associated with the influence of national health on economic growth. The theory emphasizes the importance of maternal and child health on the critical dimensions of human capital. Another element that has been shown to be a critical element for sustainable economic growth is high life expectancy ( 26 ). Aghion et al. ( 27 ) applied the endogenous growth theory, which proposes that a better life expectancy enhances growth, to analyze the relationship between health and economic growth. The study examined life expectancy for various ages in OECD countries and concluded that a decline in mortality rates for the age groups below 40 has the effect of increasing economic growth Aghion et al. ( 27 ).

Based on the above-mentioned studies, we surmise that higher income per capita is associated not only with life expectancy, but also with numerous other measures of health status. While health is not the only indicator of economic development—indeed, we need to consider the impact of other factors, such as education, political freedom, gender, and many other social attributes ( 1 , 3 , 28 )—health is definitely an integral non-income component that should be considered in a measure of economic development. People generally give high priority and value to a long and healthy life ( 2 , 25 ). Secondly, the rate of achievement of this goal to aspire for a long and healthy life differs widely across countries ( 11 , 13 , 29 ). The Human Development Index, in addition to suggesting a correlation between income and health, also expresses a strong correlation between an individual's place in the income distribution and his or her health outcomes within a country ( 2 , 30 ). This within-country correlation is particularly strong in developing countries. In comparing the growth of income with improvements in health outcomes, it is common to account for simultaneous causation. As an example, people who are healthy have the ability to be more productive in school and at work, reflecting that good health can be a precursor for better economic development ( 4 ). Additionally, a higher income allows individuals or governments to make investments that yield better health ( 28 ). Finally, differences in the quality of education, government, health, and other institutions across countries, in human capital, or in the level of technology can induce correlated movements in health and income ( 16 ). One also needs to account for the dynamic effects built into many of the potential causal outlets. For example, improvements in health may only result in increased worker productivity after a lag of several decades. Similarly, when life expectancy rises, there can be increases in population growth that may temporarily reduce income per capita ( 31 ).

The per capita health expenditures of countries vary in terms of economic development.

Whereas, high-income countries spend, on average on healthcare, $3,000 on each citizen, low-income countries only spend up to $30 per capita. It is also important to consider healthcare expenditure expressed as a percentage of GDP ( 5 , 14 ). While some countries spend higher than 12% of GDP on healthcare, others spend as little as 3% ( 32 ). There are at least two methods that can explain the association between a country's healthcare expenditure and economic performance. In the first scenario, healthcare expenditure is considered an investment in human capital. Human capital accumulation is then perceived to be a source of economic growth (e.g., via increased productivity). Therefore, an increase in healthcare expenditure is likely to be associated with a higher GDP ( 30 , 33 ). In the second scenario, an increase in healthcare expenditure can lead to regular health interventions (e.g., annual medical-checkups, preventive screening, etc.), which are likely to improve labor and productivity; this, in turn, will increase the GDP ( 34 ). Both these mechanisms reflect an iterative phenomenon between healthcare and GDP. Nevertheless, the relationship needs to be checked for endogeneity—which we aim to study in this research.

An important dimension in the relationship between health expenditure and economic performance is the factor of the productivity of workers. In developed countries, labor is scarce, and capital is abundant as a factor of production ( 2 , 31 , 35 ). But this situation is reversed in developing countries where economic growth and economies are based on labor. Here, an increase in individuals' poor health will likely lead to a loss in labor workforce and productivity ( 4 , 16 ). Therefore, addressing public health and health expenditures, though important for both developed and developing countries, is more critical for the latter ( 3 , 4 , 11 , 13 , 16 , 36 ). It is generally assumed from common knowledge that individuals who are healthier are able to work more effectively, in terms of physical and mental workload. Also, adults who were healthier as children will have acquired more human capital in the form of education, which is explained by the proximate effect of health on the level of income ( 37 ). Simultaneously, the impact of individual income on health is also important ( 38 , 39 ). Higher income can result in better health by facilitating access to better nutrition, preventative treatment, good sanitation, safe water, and affordable quality healthcare. Additionally, health can also be a cause of high income, by allowing individuals to work more, be more productive and earn higher income during the lifetime ( 35 ).

The impact of health on education is an important factor that plays a role in healthcare expenditure and economic performance ( 30 , 33 ). Children who enjoy good health can attend school regularly and have the potential of high learning ability and cognitive development. Also, if good health continues through adulthood, it will enable the population to recover the investments in education ( 30 , 33 , 39 ).

Another significant dimension in the relationship that healthcare spending has with economic development is the impact of health on savings. Good health can increase the life expectancy and encourage an individual's motivation to have savings (such as for retirement) and to make more business investments, both of which are beneficial activities for economic performance ( 1 ). Population health is an important healthcare component whose impact should be considered. A healthy population can reduce the expense on national healthcare and increase the potential for earnings. In this manner, the economic impact of population health can occur at the micro and macro levels ( 1 , 2 , 4 , 5 ). It is no surprise that some countries assign a higher value to gains from health than gains from income ( 36 , 40 – 43 ). Additionally, most countries have witnessed an increase in life expectancy despite a persistent income gap over the last 50 years ( 44 ), reflecting the monetary benefits that can accrue from investing in healthcare ( 2 , 44 ).

In this research, we acknowledge the significance of healthcare expenditure and analyze its association with the economic performance. We conduct the analysis at a national level for the United States using the data from the Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS). We incorporate the techniques of visual and descriptive analytics ( 45 – 47 ). Our findings provide insight on the differences in health spending and economic performance across the various states of the U.S. The research offers implications for governments 2008; and national policy makers to identify dimensions of healthcare that contribute to national economic performance. It is especially important for policy that addresses population health issues of a nation.

The rest of the paper is organized as follows: section Research 2 describes the methodology; section 3 presents the analyses and results; section 4 contains a discussion of results with implications; section 5 offers the scope and limitations of the research; and finally, section 6 presents the conclusions.

Research Methodology

Data collection and variables.

We analyze state-level data and ascertain patterns that offer insight into the healthcare spending and economic performance of various states in the United States. Our methodology includes the stages of data collection and variable selection, data preparation, analytics platform and tool selection, and analytics implementation. We collected economic and health data from the Centers for Medicare and Medicaid Services (CMS) ( https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData ), Bureau of Economic Analysis (BEA) ( https://www.bea.gov/iTable/iTable.cfm?reqid=70&step=1&isuri=1&acrdn=2#reqid=70&step=1&isuri=1 ), and the Bureau of Labor Statistics (BLS) ( https://www.bls.gov/lpc/data.htm ; https://www.bls.gov/webapps/legacy/tusa_1tab1.htm ) for a period of 12 years (2003–2014). The variables relate to various economic performance and healthcare spending indicators. Table 1 shows the variables in the research.

List of variables.

The data was analyzed using the business intelligence tool Tableau for visualization, R programming language for regression analysis, and SPSS Modeler for neural network analysis.

Visual Analytics Method

We utilize visual analytics to analyze healthcare spending and economic performance data. With visual analytics, one can discover patterns and relationships that are unexpected, and get timely and rational assessments of the phenomenon that is being analyzed ( 46 , 48 ). Descriptive analytics, as a technique in visual analytics, helps one understand past and current trends and make informed decisions in a domain ( 48 ). By deploying this approach, we take a more data-driven approach to understanding the trends and associations between healthcare expenditure and economic performance scenario.

The technology of analytics is used increasingly in the domain of healthcare. As a business intelligence component, analytics allows statistical and quantitative analyses of large data repositories, enabling evidenced-based decision making ( 49 ). Specifically, in the domain of healthcare, analytics offers timely, relevant and quality information that can help healthcare entities and governments optimize health resource allocation goals effectively ( 50 ).

We deploy visual analytics based on the belief that it offers an effective tool to comprehend healthcare expenditure at a national level and analyze its impact on economic performance. We now discuss the results of our analyses in the following section.

Analyses and Results

We analyzed the data for patterns and relationships between the indicators of healthcare spending and economic performance. Healthcare expenditure refers to aggregate healthcare spending in an economy, including expenditure relating to hospitals, home health agencies, prescription drugs, nursing facilities, and personal healthcare.

Distribution of Hospital Expenditure Per Capita by Hospitals

To get an idea of the state of hospital expenditure we looked at the distribution of expenditure by hospitals in the country ( Figure 1 ). Hospital expenditure includes all service provided to patients, including room, ancillary charges, physician services, in-patient pharmacy services, and nursing home and home care. In Figure 1 , the intensity of color of the bars depicts the number of hospitals such that the darker the color, the higher the number of hospitals with the expenditure. Clearly, the distribution is right-skewed. While the majority of the hospital expenditures per capita rank between $1,600 and $3,500, there are several outliers on the right side. Additionally, even though per capita hospital expenditure on average is within $3,500, there are still some hospitals where the average cost is higher.

Per capita hospital expenditure distribution.

Hospital Expenditure Per Capita and GDP Per Capita by State

We now looked to see if there was any association between the hospital expenditure per capita and the GDP rank of the state ( Figure 2 ). The figure depicts the per capita hospital expenditures by the intensity of the color (the darker the color, the higher the expenditures), and the state rank in terms of GDP per capita as a label in the state. We see that progressive states such as California with a high GDP rank have lower per person hospital expenditure; Nevada has a higher GDP rank than South Dakota but has a lower per capita hospital expenditure. In fact, the hospital expenditure in South Dakota is almost double that of Nevada. This suggests that the states that have higher economic performance (GDP) have legislative and innovative measures that support healthcare research, thereby resulting in lowered costs to the patients.

Per capita hospital expenditures and per capita GDP rank by state.

Population and Per Capita Healthcare Expenditure

Having compared the healthcare expenditure of a state with its GDP, we now wanted to see if there was any association with the population of a state ( Figure 3 ). In the bubble chart the size depicts the population of the state and the color depicts the healthcare expenditure (darker colors represent higher expenditures). Interestingly, we see that sparsely populated states such as District of Columbia (DC) have higher healthcare spending than densely populated states like Texas. On the other hand, states like New York have high population and high expenditure. Therefore, there appears to be no correlation between population size and total average per capita expenditure, proving that population qualifies as a control variable in our dataset.

Overview of population size and total per capita healthcare expenditure.

Association of Hospital Expenditure With GDP Per Capita and Changes in Multifactor Productivity Over Time

We wanted to study the pattern of growth of hospital expenditure with GDP and with changes in multifactor productivity, from 2003 to 2014 ( Figure 4 ). Both associations are shown side by side in Figure 4 .

Relationship of hospital expenditures with per capita GDP, and changes in multifactor productivity.

In Figure 4 , the circles represent the performance for a year, with the intensity of the color indicating the recency of the year. In terms of the graph showing average per capita GDP and average per capita hospital expenditure, we see that since 2003, as the average per capita GDP increases, so does the per capita hospital expenditure. The positive correlation between the average per capita GDP and average per capita hospital expenditure implies that, by proxy, healthcare has a positive effect on GDP (economic performance).

The other graph in Figure 4 shows the relationship of Multifactor Productivity (MFP) with hospital expenditure. MFP is a measure of economic performance that reflects the overall efficiency with which inputs are used to produce outputs. Figure 4 shows that since 2003, the average per capita hospital expenditure has been increasing, but there is no obvious pattern in association with the changes in multifactor productivity. Also, it is worth noting that the trend line shows that there is a slight negative correlation between the changes in multifactor productivity and average per capita hospital expenditure.

Association of Personal Healthcare Costs With Average Hours Per Day Spent on Purchasing Goods and Services, and With Changes in Multifactor Productivity (MFP)

Personal healthcare expenditure determines the out-of-pocket costs incurred by the population. Figure 5 represents two associations of hospital expenditure side by side—with general purchases of the population, and with changes in MFP. In the association of hospital expenditure with general purchases of the population, we estimated the purchasing power of the population using the average hours spent per day on purchasing goods and services. The figure shows a negative relationship such that as personal healthcare costs increase, the average time spent on purchases declines. This is because as personal healthcare costs increase, the amount of available money for spending decreases, affecting the time spent on buying goods and services. Figure 5 also shows the association between hospital expenditure and changes in MFP. The line chart/trend line in the figure indicates that there is no obvious correlation between personal healthcare costs and percent change in MFP. This is consistent with the analysis of hospital expenditure which also had no association with MFP. One can infer that that a change in healthcare costs does not affect the economic cycle.

Relationship of personal healthcare costs with average hours per day spent on purchasing goods and services, and changes in multifactor productivity.

Association of Healthcare Expenditure With Per Capita Personal Income

In looking for associations between healthcare expenditure and personal income ( Figure 6 ) we see that between 2003 and 2014, personal income mostly increased while total healthcare spending has increased as a percentage of income. This confirms two trends—Americans spend more on healthcare over time; and personal income increases faster than that of healthcare expenditure in terms of dollar amount.

Association between per capita healthcare spending and personal income.

Association of Hospital and Physician Expenditures With Labor Productivity

Physician expenditure and hospital expenditure are components of overall healthcare costs of a state. We wanted to analyze if there was any association of labor productivity with physician expenditure and hospital expenditure ( Figure 7 ). The scatterplot in the figure shows that spending in physician or hospital costs is positively correlated with an increase in labor productivity. It appears that healthcare spending has a positive relationship with labor productivity in the United States.

Correlation between labor productivity and hospital and physician expenditures.

Association of Per Capita Healthcare Expenditure With Labor Productivity and With GDP

In terms of healthcare expenditure, the above analysis revealed that physician and hospital expenditure were positively associated with labor productivity. We next explored if total healthcare expenditure which is an aggregate of all components is also associated with labor productivity, and with per capita GDP, both shown side by side ( Figure 8 ). The figure shows that as the total healthcare expenditure increases, labor productivity also increases. There is a positive correlation between total per capita healthcare expenditure and labor productivity. Thus, by increasing healthcare expenditure, the health status of Americans will improve, increasing labor productivity. Figure 8 also shows the association of total healthcare expenditure with an alternate measure of economic performance, namely the GDP. The figure depicts a chart with a trend line that shows that as total healthcare expenditures increase, GDP also increases. Healthcare expenditure of a state has a positive relationship with the GDP of the state.

Relationship between total per capita healthcare expenditures and labor productivity.

Associations Between Personal Healthcare Expenditure, Hospital Expenditure, Nursing Expenditure, and Average Weekly Hours Worked

It is important to see the relationship between average hours worked (weekly) as a measure of economic performance and healthcare expenditure comprising personal healthcare, nursing, and hospital costs ( Figure 9 ). From the figure we can see that as each of the health costs increases, there is no obvious change for average weekly hours. There appears to be no correlation between health costs and average weekly hours, which indicates there is no effect on productivity.

Relationship between personal health, hospital, nursing costs, and average weekly hours.

Association of Personal Healthcare Expenditure With Per Capita GDP

Figure 10 shows the association between personal healthcare expenditure and GDP per capita.

Correlation between per capita personal healthcare expenditure and per capita GDP.

In the figure the bar graph depicts the GDP and the trend line represents the personal healthcare expenditure. The last 2 years, which have a lighter color, represent the forecasted result. The chart shows that personal expenditure costs have steadily risen over the years, while the GDP does not show large fluctuations. A correlation is hard to establish between personal healthcare costs and GDP; it is possible that there may be extraneous types of healthcare expenditure that have an influence on the GDP.

Distribution of Various Types of Healthcare Expenditures Across Years

It is important to explore the different types of healthcare expenditure and their distribution over the years ( Figure 11 ). Personal healthcare expenditure (includes private and public insurance) has the highest average of the types of spending in the years 2003 to 2014. This is followed by hospital and physician expenditure. The rise in personal healthcare expenditure has led to a high demand for reasonably priced private health insurance across the United States. The government needs to increase the affordability of public insurance to increase the reach and benefit more people.

Distribution of various types of healthcare expenditures across years.

Association Between Personal Healthcare Expenditure Per Capita and Total Hours Worked

Figure 12 shows the relationship between personal healthcare expenditure and total hours worked for the years 2003 to 2014. The growth of expenditure costs is not proportional to the rate of change in working hours. There appears to be no correlation between expenditure and working hours; however, from the other analyses, we know that healthcare expenditure has a positive correlation with income.

Relationship between hours worked and per capita personal healthcare expenditure.

Association Between Personal Healthcare Expenditure and Other Personal Expenditure

The relationship between personal healthcare expenditure and other personal expenditure is shown in Figure 13 . The scatterplot shows the personal health expenditure having a positive correlation with the other personal expenditure. The ratio between them basically stays the same, which shows that an increase in personal health care expenditure does not impose a burden, significant enough to cause a reduction in other personal spending.

Relationship between personal health expenditure and other personal expenditure.

Important Healthcare Expenditure Predictors of Per Capita GDP

We wanted to explore which type of healthcare expenditure has the most significant influence on GDP. Figure 14 shows a machine learning based neural network model to analyze which type of healthcare spending affects the per capita GDP the most. The bars indicate to what extent the associated variable is determined by the target variable, namely per capita GDP. Among the different types of healthcare spending, hospital expenditure affects the per capita GDP the most, followed by personal healthcare. It confirms the fact that the effect of healthcare spending in the different care areas will have differential effects on the economy.

Importance of healthcare expenditure predictors for per capita GDP.

Our research offers several important findings that have implications for policy. While healthcare expenditure is negatively associated with multi-factor productivity, it is positively associated with labor productivity, personal spending, and GDP. However, this is not a causal relationship, and our inference is limited. Nevertheless, the research establishes, within the scope of the study, that an increase in healthcare expenditure has a positive relationship with economic performance. There are also variations across states that justify further research. Building on this and prior research, policy implications include that the good health of citizens indeed results in overall better economy. Therefore, investing carefully in various healthcare aspects would boost income, GDP, and productivity, and alleviate poverty. In light of these potential benefits, universal access to healthcare is something that warrants further research. Also, research can be done in countries with single-payer systems to see if a link to productivity exists there. Our results support arguments against our current healthcare system's structure in a limited way.

Scope And Limitations

Our research has a few limitations. First, economic events such as recession may affect the validity of our results. Also, this research uses several proxies for productivity. Ideally, we should also track the hours of time spent being sick, which will affect both attendance and productivity; however due to unavailability of data this was not feasible. This research studies the data at a state level while other studies may drill down further to county and city level. Our research uses secondary data and is therefore subject to the limitations posed by the secondary source in terms of availability and veracity. Finally, the effects of healthcare spending on a different group (such as varying age groups) within a state were not studied. Nevertheless, the study offers a window into the relevance of healthcare expenditure in overall economic performance at a national level.

Conclusions

Our findings suggest that, in general, there is a positive association between healthcare spending and the economic indicators of labor productivity, personal income, per capita GDP, and other spending. Also, personal healthcare spending adversely impacts time spent on purchases of goods and services. There is no association between healthcare spending and change in multi-factor productivity (MFP) or working hours. Different states require varied investment in personal health expenditure, even if they have the same level of labor productivity. Overall, the study contributes to the growing literature on healthcare expenditure and economic performance. It outlines how the government can allocate healthcare expenditure in key dimensions that can stimulate economic growth while also improving the well-being of the population. It is also critical that policy makers implement appropriate policies at the macroeconomic level—targeted at public health expenditure and economic development. Overall, in light of the potential benefits of healthcare to the economy, universal access to healthcare is an area that warrants further research.

Data Availability Statement

Ethics statement.

Since this study uses aggregated national data, both ethical approval and written informed consent from the participants were not required for this study in accordance with the local legislation and institutional requirements.

Author Contributions

VR and WR contributed equally to all parts of manuscript preparation and submission.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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What Contributes Most to High Health Care Costs? Health Care Spending in High Resource Patients

Affiliations.

• 1 1 Vice President, Science Policy, Personalized Medicine Coalition, Washington, DC.
• 2 2 Director, Health Economics and Outcomes Research, Avalere Health, Washington, DC.
• 3 3 Statistical Programmer, IMS Health, Alexandria, Virginia.
• 4 4 Associate Professor, Duke Sanford School of Public Policy, Durham, North Carolina.
• 5 5 Senior Research Scientist, Evidera, Bethesda, Maryland.
• 6 6 Chief Science Officer and Executive Vice President, National Pharmaceutical Council, Washington, DC.
• PMID: 27015249
• PMCID: PMC10397786
• DOI: 10.18553/jmcp.2016.22.2.102

Background: U.S. health care spending nearly doubled in the decade from 2000-2010. Although the pace of increase has moderated recently, the rate of growth of health care costs is expected to be higher than the growth in the economy for the near future. Previous studies have estimated that 5% of patients account for half of all health care costs, while the top 1% of spenders account for over 27% of costs. The distribution of health care expenditures by type of service and the prevalence of particular health conditions for these patients is not clear, and is likely to differ from the overall population.

Objective: To examine health care spending patterns and what contributes to costs for the top 5% of managed health care users based on total expenditures.

Methods: This retrospective observational study employed a large administrative claims database analysis of health care claims of managed care enrollees across the full age and care spectrum. Direct health care expenditures were compared during calendar year 2011 by place of service (outpatient, inpatient, and pharmacy), payer type (commercially insured, Medicare Advantage, and Medicaid managed care), and therapy area between the full population and high resource patients (HRP).

Results: The mean total expenditure per HRP during calendar year 2011 was $43,104 versus $3,955 per patient for the full population. Treatment of back disorders and osteoarthritis contributed the largest share of expenditures in both HRP and the full study population, while chronic renal failure, heart disease, and some oncology treatments accounted for disproportionately higher expenditures in HRP. The share of overall expenditures attributed to inpatient services was significantly higher for HRP (40.0%) compared with the full population (24.6%), while the share of expenditures attributed to pharmacy (HRP = 18.1%, full = 21.4%) and outpatient services (HRP = 41.9%, full = 54.1%) was reduced. This pattern was observed across payer type. While the use of physician-administered pharmaceuticals was slightly higher in HRP, their use did not alter this spending pattern.

Conclusions: Overall, expenditures in the HRP population are more than 10-fold higher compared with the full population. Managed care pharmacy can benefit from understanding what contributes to these higher costs, and managed care directors should consider an appropriately balanced assessment of the share of total spend by service and therapeutic category in HRP when devising drug usage and related cost-management strategies.

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Conflict of interest statement

Funding for this study was contributed by the National Pharmaceutical Council, an industry-funded health policy research organization that is not involved in advocacy or lobbying. Hallinan is employed with IMS Health, and at the time of this study, Petrilla and Schabert were employed with IMS Health, which received consulting fees from the National Pharmaceutical Council. Dubois is employed with the National Pharmaceutical Council, and Pritchard was employed with the National Pharmaceutical Council, at the time of this study. Taylor has nothing to disclose.

Study concept and design were contributed by Pritchard, Taylor, DuBois, and Schabert, with assistance from Petrilla. Petrilla, Hallinan, and Schabert collected the data, which were interpreted by Pritchard, Taylor, Schabert, and DuBois. The manuscript was primarily written by Pritchard, with assistance from Petrilla, Taylor, Schabert, and DuBois, and revised by Pritchard and Dubois, with assistance from Schabert and Petrilla.

Breakdown of Prescription Expenditures

Percentage of Total Expenditures by…

Percentage of Total Expenditures by Payer Type

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Rising health care prices are driving unemployment and job losses.

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Rising health care prices in the U.S. are leading employers outside the health care sector to reduce their payroll and decrease their number of employees, according to a new study co-authored by Yale economist Zack Cooper.

The study, published June 24 as a working paper by the National Bureau of Economic Research (NBER), found that when health care prices increased, non-health care employers responded by reducing their payroll and cutting the jobs of middle-class workers. For the average county, a 1% increase in health care prices would reduce aggregate income in the area by approximately $8 million annually.

The study was conducted by a team of leading economists from Yale, the University of Chicago, the University of Wisconsin-Madison, Harvard University, the U.S. Internal Revenue Service (IRS), and the U.S. Department of the Treasury.

“ When health care prices go up, jobs outside the health care sector go down,” said Cooper, an associate professor of health policy at the Yale School of Public Health and of economics in the Faculty of Arts and Sciences. “It’s broadly understood that employer-sponsored health insurance creates a link between health care markets and labor markets. Our research shows that middle- and lower-income workers are shouldering rising health care prices, and in many cases, it's costing them their jobs. Bottom line: Rising health care costs are increasing economic inequality.”

To better understand how rising health care prices affect labor market outcomes, the researchers brought together insurance claims data on approximately a third of adults with employer-sponsored insurance, health insurance premium data from the U.S. Department of Labor, and IRS data from every income tax return filed in the United States between 2008 and 2017. They then used these data to trace out how an increase in health care prices — such as a $2,000 increase on a $20,000 hospital bill — flows through to health spending, insurance premiums, employer payrolls, income and unemployment in counties, and the tax revenue collected by the federal government. 

“ Many think that it’s insurers or employers who bear the burden of rising health care prices. We show that it’s really the workers themselves who are impacted,” said Zarek Brot-Goldberg, an assistant professor at the Harris School of Public Policy at the University of Chicago. “It’s vital to understand that rising health care prices aren’t just impacting patients. Rising prices are hurting the employment outcomes for workers who never went to the hospital.”

For the new study, the authors used hospital mergers as a vehicle to assess the effect of price increases. From 2000 to 2020, there were over 1,000 hospital mergers among the approximately 5,000 U.S. hospitals. In past work , the authors found that approximately 20% of hospital mergers should have been expected to raise prices by lessening competition, according to merger guidelines from the Department of Justice and the Federal Trade Commission. These mergers, on average, raised prices by 5%.

“ We can use our analysis to estimate the effect of hospital mergers,” said Stuart Craig, an assistant professor at the University of Wisconsin-Madison Business School. “Our results show that a hospital merger that raised prices by 5% would result in $32 million in lost wages, 203 lost jobs, a $6.8 million reduction in federal tax revenue, and a death from suicide or overdose of a worker outside the health sector.”

The study also showed that because rising health care prices leads firms to let go of workers, a knock-on effect of hospital mergers is that they lead to increases in government spending on unemployment insurance and reductions in the tax revenue collected by the federal government.

“ It’s vital to point out that hospital mergers raise spending by the federal government and lower tax revenue at the same time,” said Cooper. “When prices in the U.S health sector rise, it’s actually a net negative for the economy. It’s leading to fewer jobs and precipitating all the consequences we associate with workers becoming unemployed.”

Other authors of the study were Lev Klarnet from Harvard University, Ithai Lurie from U.S. Department of Treasury, and Corbin Miller from the U.S. Internal Revenue Service.

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Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study

• Related content
• Peer review
• Geert Molenberghs , professor 4 ,
• Geert Verbeke , professor 4 ,
• Francesco Zaccardi , associate professor 5 ,
• Claire Lawson , associate professor 5 ,
• Jocelyn M Friday , data scientist 1 ,
• Huimin Su , PhD student 2 ,
• Pardeep S Jhund , professor 1 ,
• Naveed Sattar , professor 6 ,
• Kazem Rahimi , professor 3 ,
• John G Cleland , professor 1 ,
• Kamlesh Khunti , professor 5 ,
• Werner Budts , professor 1 7 ,
• John J V McMurray , professor 1
• 1 School of Cardiovascular and Metabolic Health, British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
• 2 Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
• 3 Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, UK
• 4 Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University and KU Leuven, Belgium
• 5 Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
• 6 College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
• 7 Congenital and Structural Cardiology, University Hospitals Leuven, Belgium
• Correspondence to: N Conrad nathalie.conrad{at}kuleuven.be (or @nathalie_conrad on X)
• Accepted 1 May 2024

Objective To investigate the incidence of cardiovascular disease (CVD) overall and by age, sex, and socioeconomic status, and its variation over time, in the UK during 2000-19.

Design Population based study.

Setting UK.

Participants 1 650 052 individuals registered with a general practice contributing to Clinical Practice Research Datalink and newly diagnosed with at least one CVD from 1 January 2000 to 30 June 2019.

Main outcome measures The primary outcome was incident diagnosis of CVD, comprising acute coronary syndrome, aortic aneurysm, aortic stenosis, atrial fibrillation or flutter, chronic ischaemic heart disease, heart failure, peripheral artery disease, second or third degree heart block, stroke (ischaemic, haemorrhagic, and unspecified), and venous thromboembolism (deep vein thrombosis or pulmonary embolism). Disease incidence rates were calculated individually and as a composite outcome of all 10 CVDs combined and were standardised for age and sex using the 2013 European standard population. Negative binomial regression models investigated temporal trends and variation by age, sex, and socioeconomic status.

Results The mean age of the population was 70.5 years and 47.6% (n=784 904) were women. The age and sex standardised incidence of all 10 prespecified CVDs declined by 19% during 2000-19 (incidence rate ratio 2017-19 v 2000-02: 0.80, 95% confidence interval 0.73 to 0.88). The incidence of coronary heart disease and stroke decreased by about 30% (incidence rate ratios for acute coronary syndrome, chronic ischaemic heart disease, and stroke were 0.70 (0.69 to 0.70), 0.67 (0.66 to 0.67), and 0.75 (0.67 to 0.83), respectively). In parallel, an increasing number of diagnoses of cardiac arrhythmias, valve disease, and thromboembolic diseases were observed. As a result, the overall incidence of CVDs across the 10 conditions remained relatively stable from the mid-2000s. Age stratified analyses further showed that the observed decline in coronary heart disease incidence was largely restricted to age groups older than 60 years, with little or no improvement in younger age groups. Trends were generally similar between men and women. A socioeconomic gradient was observed for almost every CVD investigated. The gradient did not decrease over time and was most noticeable for peripheral artery disease (incidence rate ratio most deprived v least deprived: 1.98 (1.87 to 2.09)), acute coronary syndrome (1.55 (1.54 to 1.57)), and heart failure (1.50 (1.41 to 1.59)).

Conclusions Despite substantial improvements in the prevention of atherosclerotic diseases in the UK, the overall burden of CVDs remained high during 2000-19. For CVDs to decrease further, future prevention strategies might need to consider a broader spectrum of conditions, including arrhythmias, valve diseases, and thromboembolism, and examine the specific needs of younger age groups and socioeconomically deprived populations.

Introduction

Since the 1970s, the prevention of coronary disease, both primary and secondary, has improved considerably, largely attributable to public health efforts to control risk factors, such as antismoking legislation, and the widespread use of drugs such as statins. 1 2

Improvements in mortality due to heart disease have, however, stalled in several high income countries, 3 and reports suggest that the incidence of heart disease might even be increasing among younger people. 4 5 6 Conversely, along with coronary heart disease, other cardiovascular conditions are becoming relatively more prominent in older people, altering the profile of cardiovascular disease (CVD) in ageing societies. The importance of non-traditional risk factors for atherosclerotic diseases, such as socioeconomic deprivation, has also been increasingly recognised. Whether socioeconomic deprivation is as strongly associated with other CVDs as with atherosclerosis is uncertain, but it is important to understand as many countries have reported an increase in socioeconomic inequalities. 7

Large scale epidemiological studies are therefore needed to investigate secular trends in CVDs to target future preventive efforts, highlight the focus for future clinical trials, and identify healthcare resources required to manage emerging problems. Existing comprehensive efforts, such as statistics on CVD from leading medical societies or the Global Burden of Diseases studies, have helped toward this goal, but reliable age standardised incidence rates for all CVDs, how these vary by population subgroups, and changes over time are currently not available. 8 9 10

We used a large longitudinal database of linked primary care, secondary care, and death registry records from a representative sample of the UK population 11 12 to assess trends in the incidence of 10 of the most common CVDs in the UK during 2000-19, and how these differed by sex, age, socioeconomic status, and region.

Data source and study population

We used anonymised electronic health records from the GOLD and AURUM datasets of Clinical Practice Research Datalink (CPRD). CPRD contains information on about 20% of the UK population and is broadly representative of age, sex, ethnicity, geographical spread, and socioeconomic deprivation. 11 12 It is also one of the largest databases of longitudinal medical records from primary care in the world and has been validated for epidemiological research for a wide range of conditions. 11 We used the subset of CPRD records that linked information from primary care, secondary care from Hospital Episodes Statistics (HES admitted patient care and HES outpatient) data, and death certificates from the Office for National Statistics (ONS). Linkage was possible for a subset of English practices, covering about 50% of the CPRD records. Data coverage dates were 1 January 1985 to 31 December 2019 for primary care data (including drug prescription data), 1 April 1997 to 30 June 2019 for secondary care data, and 2 January 1998 to 30 May 2019 for death certificates.

Included in the study were men and women registered with a general practice for at least one year during the study period (1 January 2000 to 30 June 2019) whose records were classified by CPRD as acceptable for use in research and approved for HES and ONS linkage.

Study endpoints

The primary endpoint was the first presentation of CVD as recorded in primary or secondary care. We investigated 10 CVDs: acute coronary syndrome, aortic aneurysm, aortic stenosis, atrial fibrillation or flutter, chronic ischaemic heart disease, heart failure, peripheral artery disease, second or third degree heart block, stroke (ischaemic, haemorrhagic, or unspecified), and venous thromboembolism (deep vein thrombosis or pulmonary embolism). We defined incident diagnoses as the first record of that condition in primary care or secondary care regardless of its order in the patient’s record.

Diseases were considered individually and as a composite outcome of all 10 CVDs combined. For the combined analyses, we calculated the primary incidence (considering only the first recorded CVD in each patient, reflecting the number of patients affected by CVDs) and the total incidence (considering all incident CVD diagnoses in each patient, reflecting the cumulative number of CVD diagnoses). We performed sensitivity analyses including diagnoses recorded on death certificates.

To identify diagnoses, we compiled a list of diagnostic codes based on the coding schemes in use in each data source following previously established methods. 13 14 15 We used ICD-10 (international classification of diseases, 10th revision) codes for diagnoses recorded in secondary care, ICD-9 (international classification of diseases, ninth revision) (in use until 31 December 2000) and ICD-10 codes for diagnoses recorded on death certificates (used in sensitivity analyses only), the UK Office of Population Censuses and Surveys classification (OPCS-4) for procedures performed in secondary care settings, and a combination of Read, SNOMED, and local EMIS codes for diagnoses recorded in primary care records (see supplementary table S1). 16 Supplementary texts S1, S2, and S3 describe our approach to the generation of the diagnostic code list as well as considerations and sensitivity analyses into the validity of diagnoses recorded in UK electronic health records.

We selected covariates to represent a range of known cardiovascular risk factors. For clinical data, including systolic and diastolic blood pressure, smoking status, cholesterol (total:high density lipoprotein ratio), and body mass index (BMI), we abstracted data from primary care records as the most recent measurement within two years before the incident CVD diagnosis. BMI was categorised as underweight (<18.5), normal (18.5-24.9), overweight (25-29.9), and obesity (≥30). Information on the prevalence of chronic kidney disease, dyslipidaemia, hypertension, and type 2 diabetes was obtained as the percentage of patients with a diagnosis recorded in their primary care or secondary care record at any time up to and including the date of a first CVD diagnosis. Patients’ socioeconomic status was described using the index of multiple deprivation 2015, 17 a composite measure of seven dimensions (income, employment, education, health, crime, housing, living environment) and provided by CPRD. Measures of deprivation are calculated at small area level, covering an average population of 1500 people, and are presented in fifths, with the first 20% and last 20% representing the least and most deprived areas, respectively. We extracted information on ethnicity from both primary and secondary care records, and we used secondary care data when records differed. Ethnicity was grouped into four categories: African/Caribbean, Asian, white, and mixed/other. Finally, we extracted information on cardiovascular treatments (ie, aspirin and other antiplatelets, alpha adrenoceptor antagonists, aldosterone antagonists/mineralocorticoid receptor antagonists, angiotensin converting enzyme inhibitors, angiotensin II receptor antagonists, beta blockers, calcium channel blockers, diuretics, nitrates, oral anticoagulants, and statins) as the number of patients with at least two prescriptions of each drug class within six months after incident CVD, among patients alive and registered with a general practitioner 30 days after the diagnosis. Supplementary table S2 provides a list of substances included in each drug class. Prescriptions were extracted from primary care records up to 31 December 2019.

Statistical analyses

Categorical data for patient characteristics are presented as frequencies (percentages), and continuous data are presented as means and standard deviations (SDs) for symmetrically distributed data or medians and interquartile ranges (IQRs) for non-symmetrically distributed data, over the whole CVD cohort and stratified by age, sex, socioeconomic status, region, and calendar year of diagnosis. For variables with missing entries, we present numbers and percentages of records with missing data. For categorical variables, frequencies refer to complete cases.

Incidence rates of CVD were calculated by dividing the number of incident diagnoses by the number of patient years in the cohort. Category specific rates were computed separately for subgroups of age, sex, socioeconomic status, region, and calendar year of diagnosis. Age calculations were updated for each calendar year. To ensure calculations referred to incident diagnoses, we excluded individuals, from both the numerator and the denominator populations, with a disease of interest diagnosed before the study start date (1 January 2000), or within the first 12 months of registration with their general practice. Time at risk started at the latest of the patient’s registration date plus 12 months, 30 June of their birth year, or study start date; and stopped at the earliest of death, transfer out of practice, last collection date of the practice, incidence of the disease of interest, or linkage end date (30 June 2019). Disease incidence was standardised for age and sex 18 using the 2013 European standard population 19 in five year age bands up to age 90 years.

Negative binomial regression models were used to calculate overall and category specific incidence rate ratios and corresponding 95% confidence intervals (CIs). 20 Models were adjusted for calendar year of diagnosis, age (categorised into five years age bands), sex, socioeconomic status, and region. We chose negative binomial models over Poisson models to account for potential overdispersion in the data. Sensitivity analyses comparing Poisson and negative binomial models showed similar results.

Study findings are reported according to the RECORD (reporting of studies conducted using observational routinely collected health data) recommendations. 21 We performed statistical analyses in R, version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria).

Patient and public involvement

No patients or members of the public were directly involved in this study owing to constraints on funding and time.

A total of 22 009 375 individuals contributed data between 1 January 2000 and 30 June 2019, with 146 929 629 patient years of follow-up. Among those we identified 2 906 770 new CVD diagnoses, affecting 1 650 052 patients. Mean age at first CVD diagnosis was 70.5 (SD 15.0) years, 47.6% (n=784 904) of patients were women, and 11.6% (n=191 421), 18.0% (n=296 554), 49.7% (n=820 892), and 14.2% (n=233 833) of patients had a history of chronic kidney disease, dyslipidaemia, hypertension, and type 2 diabetes, respectively, at the time of their first CVD diagnosis ( table 1 ).

Characteristics of patients with a first diagnosis of CVD, 2000-19. Values are number (percentage) unless stated otherwise

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During 2017-19, the most common CVDs were atrial fibrillation or flutter (age-sex standardised incidence 478 per 100 000 person years), heart failure (367 per 100 000 person years), and chronic ischaemic heart disease (351 per 100 000 person years), followed by acute coronary syndrome (190 per 100 000 person years), venous thromboembolism (183 per 100 000 person years), and stroke (181 per 100 000 patient years) ( fig 1 ).

Incidence of a first diagnosis of cardiovascular disease per 100 000 person years, 2000-19. Incidence rates are age-sex standardised to the 2013 European standard population. Any cardiovascular disease refers to the primary incidence of cardiovascular disease across the10 conditions investigated (ie, number of patients with a first diagnosis of cardiovascular disease). See supplementary table S4 for crude incidence rates by age and sex groups. IRR=incidence rate ratio

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Temporal trends

The primary incidence of CVDs (ie, the number of patients with CVD) decreased by 20% during 2000-19 (age-sex standardised incidence rate ratio 2017-19 v 2000-02: 0.80 (95% CI 0.73 to 0.88)). However, the total incidence of CVD (ie, the total number of new CVD diagnoses) remained relatively stable owing to an increasing number of subsequent diagnoses among patients already affected by a first CVD (incidence rate ratio 2017-19 v 2000-02: 1.00 (0.91 to 1.10)).

The observed decline in CVD incidence was largely due to declining rates of atherosclerotic diseases, in particular acute coronary syndrome, chronic ischaemic heart disease, and stroke, which decreased by about 30% during 2000-19. The incidence of peripheral artery disease also declined, although more modestly (incidence rate ratio 2017-19 v 2000-02: 0.89 (0.80 to 0.98)) ( fig 1 ).

The incidence of non-atherosclerotic heart diseases increased at varying rates, with incidence of aortic stenosis and heart block more than doubling over the study period (2017-19 v 2000-02: 2.42 (2.13 to 2.74) and 2.22 (1.99 to 2.46), respectively) ( fig 1 ). These increasing rates of non-atherosclerotic heart diseases balanced the reductions in ischaemic diseases so that the overall incidence of CVD across the 10 conditions appeared to reach a plateau and to remain relatively stable from 2007-08 (incidence rate ratio 2017-19 v 2005-07: 1.00 (0.91 to 1.10)) ( fig 2 ).

Age standardised incidence of cardiovascular disease by sex, 2000-19. Any cardiovascular disease refers to the primary incidence of cardiovascular disease across the 10 conditions investigated (ie, number of patients with a first diagnosis of cardiovascular disease). IRR=incidence rate ratio

Age stratified analyses further showed that the observed decrease in incidence of chronic ischaemic heart disease, acute coronary syndrome, and stroke was largely due to a reduced incidence in those aged >60 years, whereas incidence rates in those aged <60 years remained relatively stable ( fig 3 and fig 4 ).

Sex standardised incidence of cardiovascular disease in all age groups. Any cardiovascular disease refers to the primary incidence of cardiovascular disease across the 10 conditions investigated (ie, number of patients with a first diagnosis of cardiovascular disease)

Sex standardised incidence of cardiovascular diseases by age subgroups <69 years. Any cardiovascular disease refers to the primary incidence of cardiovascular disease across the 10 conditions investigated (ie, number of patients with a first diagnosis of cardiovascular disease)

Age at diagnosis

CVD incidence was largely concentrated towards the end of the life span, with a median age at diagnosis generally between 65 and 80 years. Only venous thromboembolism was commonly diagnosed before age 45 years ( fig 5 ). Over the study period, age at first CVD diagnosis declined for several conditions, including stroke (on average diagnosed 1.9 years earlier in 2019 than in 2000), heart block (1.3 years earlier in 2019 than in 2000), and peripheral artery disease (1 year earlier in 2019 than in 2000) (see supplementary figure S1). Adults with a diagnosis before age 60 years were more likely to be from lower socioeconomic groups and to have a higher prevalence of several risk factors, including obesity, smoking, and high cholesterol levels (see supplementary table S3).

Incidence rates of cardiovascular diseases calculated by one year age bands and divided into a colour gradient of 20 quantiles to reflect incidence density by age. IQR=interquartile range

Incidence by sex

Age adjusted incidence of all CVDs combined was higher in men (incidence rate ratio for women v men: 1.46 (1.41 to 1.51)), with the notable exception of venous thromboembolism, which was similar between men and women. The incidence of aortic aneurysms was higher in men (3.49 (3.33 to 3.65)) ( fig 2 ). The crude incidence of CVD, however, was similar between men and women (1069 per 100 000 patient years and 1176 per 100 000 patient years, respectively), owing to the higher number of women in older age groups. Temporal trends in disease incidence were generally similar between men and women ( fig 2 ).

Incidence by socioeconomic status

The most deprived socioeconomic groups had a higher incidence of any CVDs (incidence rate ratio most deprived v least deprived: 1.37 (1.30 to 1.44)) ( fig 6 ). A socioeconomic gradient was observed across almost every condition investigated. That gradient did not decrease over time, and it was most noticeable for peripheral artery disease (incidence rate ratio most deprived v least deprived: 1.98 (1.87 to 2.09)), acute coronary syndrome (1.55 (1.54 to 1.57)), and heart failure (1.50 (1.41 to 1.59)). For aortic aneurysms, atrial fibrillation, heart failure, and aortic stenosis, socioeconomic inequalities in disease incidence appeared to increase over time.

Age-sex standardised incidence rates of cardiovascular diseases by socioeconomic status (index of multiple deprivation 2015). Any cardiovascular disease refers to the primary incidence of cardiovascular disease across the 10 conditions investigated (ie, number of patients with a first diagnosis of cardiovascular disease). Yearly incidence estimates were smoothed using loess (locally estimated scatterplot smoothing) regression lines

Regional differences

Higher incidence rates were seen in northern regions (north west, north east, Yorkshire and the Humber) of England for all 10 conditions investigated, even after adjusting for socioeconomic status. Aortic aneurysms and aortic stenosis had the strongest regional gradients, with incidence rates about 30% higher in northern regions compared with London. Geographical variations remained modest, however, and did not appear to change considerably over time (see supplementary figure S2).

Sensitivity analyses

In sensitivity analyses that used broader disease definitions, that included diagnoses recorded on death certificates, that relied on longer lookback periods for exclusion of potentially prevalent diagnoses, or that were restricted to diagnoses recorded during hospital admissions, temporal trends in disease incidence appeared similar (see supplementary figures S3-S6).

Secondary prevention treatments

The proportion of patients using statins and antihypertensive drugs after a first CVD diagnosis increased over time, whereas the use of non-dihydropyridines calcium channel blockers, nitrates, and diuretics decreased over time. Non-vitamin K antagonist oral anticoagulants increasingly replaced vitamin K anticoagulants (see supplementary figure S7).

The findings of this study suggest that important changes occurred in the distribution of CVDs during 2000-19 and that several areas are of concern. The incidence of non-atherosclerotic heart diseases was shown to increase, the decline in atherosclerotic disease in younger people was stalling, and socioeconomic inequalities had a substantial association across almost every CVD investigated.

Implications for clinical practice and policy

Although no causal inference can be made from our data, the decline in rates of ischaemic diseases coincided with reductions in the prevalence of risk factors such as smoking, hypertension, and raised cholesterol levels in the general population over the same period, 22 and this finding suggests that efforts in the primary and secondary prevention of atherosclerotic diseases have been successful. The decline in stroke was not as noticeable as that for coronary heart disease, which may reflect the rising incidence of atrial fibrillation. The variation in trends for peripheral artery disease could be due to differences in risk factors (eg, a stronger association with diabetes), the multifaceted presentations and causes, and the introduction of systematic leg examinations for people with diabetes. 23 24

All the non-atherosclerotic diseases, however, appeared to increase during 2000-19. For some conditions, such as heart failure, the observed increase remained modest, whereas for others, such as aortic stenosis and heart block, incidence rates doubled. All analyses in this study were standardised for age and sex, to illustrate changes in disease incidence independently of changes in population demographics. Whether these trends solely reflect increased awareness, access to diagnostic tests, or even screening (eg, for abdominal aortic aneurysm 25 ) and coding practices, is uncertain. Reductions in premature death from coronary heart disease may have contributed to the emergence of these other non-atherosclerotic CVDs. Regardless, the identification of increasing numbers of people with these problems has important implications for health services, especially the provision of more surgical and transcatheter valve replacement, pacemaker implantation, and catheter ablation for atrial fibrillation. Importantly, these findings highlight the fact that for many cardiovascular conditions such as heart block, aortic aneurysms, and non-rheumatic valvular diseases, current medical practice remains essentially focused on the management of symptoms and secondary prevention and that more research into underlying causes and possible primary prevention strategies is needed. 26 27

These varying trends also mean that the contribution of individual CVDs towards the overall burden has changed. For example, atrial fibrillation or flutter are now the most common CVDs in the UK. Atrial fibrillation is also a cause (and consequence) of heart failure, and these two increasingly common problems may amplify the incidence of each other. Venous thromboembolism and heart block also appeared as important contributors to overall CVD burden, with incidence rates similar to those of stroke and acute coronary syndrome, yet both receive less attention in terms of prevention efforts.

The stalling decline in the rate of coronary heart disease in younger age groups is of concern, has also been observed in several other high income countries, and may reflect rising rates of physical inactivity, obesity, and type 2 diabetes in young adults. 4 6 28 The stalled decline suggests prevention approaches may need to be expanded beyond antismoking legislation, blood pressure control, and lipid lowering interventions to include the promotion of physical activity, weight control, and use of new treatments shown to reduce cardiovascular risk in people with type 2 diabetes. 29 Although CVD incidence is generally low in people aged <60 years, identifying those at high risk of developing CVD at a young age and intervening before problems occur could reduce premature morbidity and mortality and have important economic implications.

Our study further found that socioeconomic inequalities may contribute to CVD burden, and that this association is not restricted to selected conditions but is visible across most CVDs. The reasons behind the observed increase in risk in relation to socioeconomic inequalities are likely to be multifactorial and to include environmental, occupational, psychosocial, and behavioural risk factors, including established cardiovascular risk factors such as smoking, obesity, nutrition, air pollution, substance misuse, and access to care. 30 How these findings apply to different countries is likely to be influenced by socioeconomic structures and healthcare systems, although health inequalities have been reported in numerous countries. 30 One important factor in the present study is that access to care is free at the point of care in the UK, 31 and yet socioeconomic inequalities persist despite universal health coverage and they did not appear to improve over time. Independently of the specificities of individual countries, our findings highlight the importance of measuring and considering health inequalities and suggest that dealing with the social determinants of health—the conditions under which people are born, live, work, and age—could potentially bring substantial health improvements across a broad range of chronic conditions.

Finally, our results reflect disease incidence based on diagnostic criteria, screening practices, availability, and accuracy of diagnostic tests in place at a particular time and therefore must be interpreted within this context. 32 Several of the health conditions investigated are likely to being sought and detected with increased intensity over the study period. For example, during the study period the definition of myocardial infarction was revised several times, 33 34 35 and high sensitivity troponins were progressively introduced in the UK from 2010. These more sensitive markers of cardiac injury are thought to have increased the detection rates for less severe disease. 36 37 Similarly, increased availability of computed tomography may have increased detection rates for stroke. 38 These changes could have masked an even greater decline in these conditions than observed in the present study. Conversely, increased use of other biochemical tests (such as natriuretic peptides) and more sensitive imaging techniques might have increased the detection of other conditions. 39 40 41 The implementation of a screening programme for aortic aneurysm and incentive programmes aimed at improving coding practices, including the documentation of CVD, associated risk factors and comorbidities, and treatment of these, are also likely to have contributed to the observed trends. 25 42 43 As a result, the difference in incidence estimates and prevalence of comorbidities over time may not reflect solely changes in the true incidence but also differences in ascertainment of people with CVD. 44 Nonetheless, long term trends in large and unconstrained populations offer valuable insights for healthcare resource planning and for the design of more targeted prevention strategies that could otherwise not be answered by using smaller cohorts, cross sectional surveys, or clinical trials; and precisely because they are based on routinely reported diagnoses they are more likely to capture the burden of disease as experienced by doctors and health services.

Strengths and limitations of this study

A key strength of this study is its statistical power, with >140 million person years of data. The large size of the cohort allowed us to perform incidence calculations for a broad spectrum of conditions, and to examine the influence of age, sex, and socioeconomic status as well as trends over 20 years. One important limitation of our study was the modest ethnic diversity in our cohort and the lack of information on ethnicity for the denominator population, which precluded us from stratifying incidence estimates by ethnic group. Our analyses were also limited by the unavailability or considerable missingness of additional variables potentially relevant to the development of CVD, such as smoking, body mass index, imaging data, women specific cardiovascular risk factors (eg, pregnancy associated hypertension and gestational diabetes), and blood biomarkers. Further research may also need to consider an even wider spectrum of CVDs, including individual types of valve disease, pregnancy related conditions, and infection related heart diseases. Research using databases with electronic health records is also reliant on the accuracy of clinical coding input by doctors in primary care as part of a consultation, or in secondary care as part of a hospital admission. We therefore assessed the validity of diagnoses in UK electronic health records data and considered it to be appropriate in accordance with the >200 independent validation studies reporting an average positive predictive value of about 90% for recorded diagnoses. 45 Observed age distributions were also consistent with previous studies and added to the validity of our approach. Nevertheless, our results must be interpreted within the context and limitations of routinely collected data from health records, diagnostic criteria, screening practices, the availability and accuracy of diagnostic tests in place at that time, and the possibility that some level of miscoding is present or that some bias could have been introduced by restricting the cohort to those patients with at least 12 months of continuous data.

Conclusions

Efforts to challenge the notion of the inevitability of vascular events with ageing, and evidence based recommendations for coronary heart disease prevention, have been successful and can serve as a model for other non-communicable diseases. Our findings show that it is time to expand efforts to improve the prevention of CVDs. Broadening research and implementation efforts in both primary and secondary prevention to non-atherosclerotic diseases, tackling socioeconomic inequalities, and introducing better risk prediction and management among younger people appear to be important opportunities to tackle CVDs.

What is already known on this topic

Recent data show that despite decades of declining rates of cardiovascular mortality, the burden from cardiovascular disease (CVD) appears to have stalled in several high income countries

What this study adds

This observational study of a representative sample of 22 million people from the UK during 2000-19 found reductions in CVD incidence to have been largely restricted to ischaemic heart disease and stroke, and were paralleled by a rising number of diagnoses of cardiac arrhythmias, valve disease, and thromboembolic events

Venous thromboembolism and heart block were important contributors to the overall burden of CVDs, with incidence rates similar to stroke and acute coronary syndromes

Improvements in rates of coronary heart disease almost exclusively appeared to benefit those aged >60 years, and the CVD burden in younger age groups appeared not to improve

Ethics statements

Ethical approval.

This study was approved by the Clinical Practice Research Datalink Independent Scientific Advisory Committee.

Data availability statement

Access to Clinical Practice Research Datalink (CPRD) data is subject to a license agreement and protocol approval process that is overseen by CPRD’s research data governance process. A guide to access is provided on the CPRD website ( https://www.cprd.com/data-access ) To facilitate the subsequent use and replication of the findings from this study, aggregated data tables are provided with number of events and person years at risk by individual condition and by calendar year, age (by five year age band), sex, socioeconomic status, and region (masking field with fewer than five events, as per CPRD data security and privacy regulations) on our GitHub repository ( https://github.com/nathalieconrad/CVD_incidence ).

Acknowledgments

We thank Hilary Shepherd, Sonia Coton, and Eleanor L Axson from the Clinical Practice Research Datalink for their support and expertise in preparing the dataset underlying these analyses.

Contributors: NC and JJVM conceived and designed the study. NC, JJVM, GM, and GV designed the statistical analysis plan and NC performed the statistical analysis. All authors contributed to interpreting the results, drafting the manuscript, and the revisions. NC, GM, and GV had permission to access the raw data and NC and GM verified the raw data. All authors gave final approval of the version to be published and accept responsibility to submit the manuscript for publication. NC and JJVM accept full responsibility for the conduct of the study, had access to aggregated data, and controlled the decision to publish. They are the guarantors. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This study was funded by a personal fellowship from the Research Foundation Flanders (grant No 12ZU922N), a research grant from the European Society of Cardiology (grant No App000037070), and the British Heart Foundation Centre of Research Excellence (grant No RE/18/6/34217). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: NC is funded by a personal fellowship from the Research Foundation Flanders and a research grant from the European Society of Cardiology. JMF, PSJ, JGC, NS, and JJVM are supported by British Heart Foundation Centre of Research Excellence. PSJ and JJVM are further supported by the Vera Melrose Heart Failure Research Fund. JJVM has received funding to his institution from Amgen and Cytokinetics for his participation in the steering sommittee for the ATOMIC-HF, COSMIC-HF, and GALACTIC-HF trials and meetings and other activities related to these trials; has received payments through Glasgow University from work on clinical trials, consulting, and other activities from Alnylam, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardurion, Dal-Cor, GlaxoSmithKline, Ionis, KBP Biosciences, Novartis, Pfizer, and Theracos; and has received personal lecture fees from the Corpus, Abbott, Hikma, Sun Pharmaceuticals, Medscape/Heart.Org, Radcliffe Cardiology, Alkem Metabolics, Eris Lifesciences, Lupin, ProAdWise Communications, Servier Director, and Global Clinical Trial Partners. NS declares consulting fees or speaker honorariums, or both, from Abbott Laboratories, Afimmune, Amgen, AstraZeneca, Boehringer Ingelheim, Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and grant support paid to his university from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics. KK has acted as a consultant or speaker or received grants for investigator initiated studies for Astra Zeneca, Bayer, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp & Dohme, Boehringer Ingelheim, Oramed Pharmaceuticals, Roche, and Applied Therapeutics. KK is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and the NIHR Leicester Biomedical Research Centre (BRC). CL is funded by an NIHR Advanced Research Fellowship (NIHR-300111) and supported by the Leicester BRC. PSJ has received speaker fees from AstraZeneca, Novartis, Alkem Metabolics, ProAdWise Communications, Sun Pharmaceuticals, and Intas Pharmaceuticals; has received advisory board fees from AstraZeneca, Boehringer Ingelheim, and Novartis; has received research funding from AstraZeneca, Boehringer Ingelheim, Analog Devices; his employer, the University of Glasgow, has been remunerated for clinical trial work from AstraZeneca, Bayer, Novartis, and Novo Nordisk; and is the Director of Global Clinical Trial Partners. HS is supported by the China Scholarship Council. Other authors report no support from any organisation for the submitted work, no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, and no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (NC) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: Results from this study will be shared with patient associations and foundations dedicated to preventing cardiovascular diseases, such as the European Heart Network and the American Heart Association. To reach the public, findings will also be press released alongside publication of this manuscript. Social media (eg, X) will be used to draw attention to the work and stimulate debate about its findings. Finally, the underlying developed algorithms will be freely available for academic use at https://github.com/nathalieconrad/CVD_incidence .

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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06-25-2024 NEWS

Economists say rising healthcare costs could lead to you losing your job

According to collaborative research from Yale, the University of Chicago, and the IRS, among others, expensive medical care doesn’t just impact patients. It could result in layoffs for workers who never even went to the hospital.

[Photos: cottonbro studio /Pexels, moritz320 /Pixabay]

BY  Shalene Gupta 1 minute read

It turns out rising healthcare prices have a hidden cost: jobs. A new working paper, titled “ Who Pays for Rising Health Care Prices? Evidence from Hospital Mergers ” and published by the National Bureau of Economic Research, examines the link between increasing healthcare costs and job cuts.

The paper was written by economists from Yale, the University of Chicago, the University of Wisconsin-Madison, Harvard University, the U.S. Internal Revenue Service (IRS), and the U.S. Department of the Treasury.  The researchers found a 5% increase in healthcare costs is associated with 203 jobs eliminated, and $32 million in lost pay.

Not only are jobs lost, but tragically, 1 in 140 of the workers laid off after a healthcare cost increase ends up dying by suicide or a drug overdose.

The researchers analyzed data on hospital mergers and found that two years after a merger, healthcare prices go up by 1.2%. They also combined merger data with data from the Department of Labor on employer healthcare premiums and IRS data on income taxes from 2008 to 2017 to understand how increased healthcare costs move through the economy. They noted that when healthcare costs go up, health insurance premiums go up, and since employers cannot reduce wages, their only other choice is to cut jobs.

“Many think that it’s insurers or employers who bear the burden of rising health care prices. We show that it’s really the workers themselves who are impacted,” Zarek Brot-Goldberg, an assistant professor at the Harris School of Public Policy at the University of Chicago, said in a statement. “It’s vital to understand that rising health care prices aren’t just impacting patients. Rising prices are hurting the employment outcomes for workers who never went to the hospital.”

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ABOUT THE AUTHOR

Shalene Gupta is a frequent contributor to Fast Company, covering Gen Z in the workplace , the psychology of money , and health business news. She is the coauthor of The Power of Trust: How Companies Build It, Lose It, Regain It (Public Affairs, 2021) with Harvard Business School professor Sandra Sucher, and is currently working on a book about severe PMS, PMDD, and PME for Flatiron   More

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• DOI: 10.1093/ehjopen/oeae049
• Corpus ID: 270604192

Risk-Stratified Analysis of Long-Term Clinical Outcomes and Cumulative Costs in Finnish Patients with Recent Acute Coronary Syndrome or Coronary Revascularisation: A 5-Year Real-World Study Using Electronic Health Records

• M. Oksanen , Jenna Parviainen , +6 authors Janne Martikainen
• Published in European Heart Journal Open 18 June 2024
• Medicine, Economics

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Committee for a Responsible Federal Budget

Trump and biden: the national debt.

The national debt is on course to reach a record share of the economy under the next presidential administration, due in part to policies approved by Presidents Trump and Biden during their time in office, including executive actions and legislation passed by Congress. 

While it is important to understand the fiscal impact of the promises candidates make on the campaign trail – particularly because they reflect the candidates’ own policy preferences and are not impacted by unexpected external events or the actions of Congress – the fact that both leading candidates have served as President also allows for a comparison of their actual fiscal records. This analysis focuses on the estimated ten-year debt impact of policies approved by Presidents Trump and Biden around the time of enactment. 1 In this analysis, we find:

• President Trump  approved $8.4 trillion of new ten-year borrowing during his full term in office, or $4.8 trillion excluding the CARES Act and other COVID relief.
• President Biden , in his first three years and five months in office, approved $4.3 trillion of new ten-year borrowing, or $2.2 trillion excluding the American Rescue Plan.
• President Trump approved $8.8 trillion  of gross new borrowing and $443 billion  of deficit reduction during his full presidential term. 
• President Biden has so far approved $6.2 trillion of gross new borrowing and $1.9 trillion of deficit reduction.

In companion analyses, we will show:

• Roughly 77 percent  of President Trump’s approved ten-year debt came from bipartisan legislation, and 29 percent  of the net ten-year debt President Biden has approved thus far came from bipartisan legislation. The rest was from partisan actions.
• President Trump approved $2.2 trillion of debt in his first two years in office and $6.2 trillion  ($2.6 trillion non-COVID) in his second two years. President Biden approved $4.9 trillion ($2.9 trillion non-COVID) in his first two years in office and has so far approved over $600 billion of net ten-year deficit reduction since. 
• President Trump approved $5.9 trillion of net spending increases including interest ($2.8 trillion non-COVID) and $2.5 trillion of net tax cuts ($2.0 trillion non-COVID). President Biden has approved $4.3 trillion of net spending increases including interest ($2.3 trillion non-COVID) and roughly $0 of net tax changes ($60 billion revenue increase non-COVID).
• Debt held by the public rose by $7.2 trillion during President Trump’s term including $5.9 trillion in the first three years and five months. Debt held by the public has grown by $6.0 trillion during President Biden’s term so far. 
• President Trump’s executive actions added less than $20 billion to ten-year debt on net. President Biden’s executive actions have added $1.2 trillion to ten-year debt so far. 
• The President’s budget was on average 39 days late under President Trump and 58 days late under President Biden. 

Summary Table: Executive Actions & Legislation Approved by Presidents Trump & Biden

Note: bipartisan indicates legislation passed with votes from both political parties in either chamber of Congress. *Includes $23 billion of executive actions in the form of student debt payment pauses. 

How Much Debt Did President Trump Approve?

During his four-year term in office, President Trump approved $8.4 trillion  of new ten-year borrowing above prior law, or $4.8 trillion  when excluding the bipartisan COVID relief bills and COVID-related executive actions. Looking at all legislation and executive actions with meaningful fiscal impact, the full amount of approved ten-year borrowing includes $8.8 trillion of deficit-increasing laws and actions offset by $443 billion of deficit-reducing actions. 2

These estimates are based on scores of legislation and executive actions rather than retrospective estimates. Scores are generally made on a conventional basis, though the Tax Cuts and Jobs Act (TCJA) is scored dynamically. The actual debt impact of the policies was likely somewhat higher than these scores. In particular, the TCJA likely reduced revenue more than projected and saved less from repealing the individual health care mandate penalty, 3 while the Employee Retention Credit was likely far more expensive than originally estimated.

Sources: CRFB estimates based on CBO and OMB projections.

The major actions approved by President Trump (and ten-year impact with interest) include:

• The Tax Cuts and Jobs Act of 2017 ( $1.9 trillion debt increase )
• The Bipartisan Budget Acts of 2018 and 2019 ( $2.1 trillion debt increase ) 
• ACA Tax Delays and Repeals ( $539 billion debt increase )
• Health Executive Actions ( $456 billion debt increase ) 
• Other Legislation ( $310 billion debt increase )  
• New and Increased Tariffs ( $443 billion debt reduction )
• The CARES Act ( $1.9 trillion debt increase ) 
• The Response & Relief Act ( $983 billion debt increase ) 
• Other COVID Relief ( $756 billion debt increase )

How Much Debt Has President Biden Approved?

Over his first three years and five months in office, President Biden has approved $4.3 trillion  of new ten-year borrowing, or $2.2 trillion  when excluding the American Rescue Plan Act. This includes $6.2 trillion of deficit-increasing legislation and actions, offset by $1.9 trillion of legislation and actions scored as reducing the deficit.

These estimates are based on scores of legislation and executive actions rather than retrospective estimates and do not include preliminary rules, unexecuted “side deals,” or actions ruled illegal by the Supreme Court. Updated scores and in-process actions would increase the total. For example, an updated estimate would likely wipe away the $252 billion of scored savings from the Inflation Reduction Act, 4 the informal FRA side deals would reduce its savings by  about $500 billion , and the new student debt cancellation plan could cost  $250 to $750 billion .

The major actions approved by President Biden so far (and ten-year impact with interest) include:

• Appropriations for FY 2022 and 2023 ( $1.4 trillion debt increase ) 
• The Honoring Our PACT Act ( $520 billion debt increase )
• The Bipartisan Infrastructure Law ( $439 billion debt increase ) 
• Other Legislation ( $422 billion debt increase )
• Student Debt Actions ( $620 billion debt increase )
• Other Executive Actions ( $548 billion debt increase ) 
• The Fiscal Responsibility Act ( $1.5 trillion debt reduction )
• The Inflation Reduction Act ( $252 billion debt reduction )
• Deficit-Reducing Executive Actions ( $129 billion debt reduction )
• The American Rescue Plan Act ( $2.1 trillion debt increase )

The next presidential term will present significant fiscal challenges. While past performance is not necessarily indicative of future actions, it is helpful to examine the fiscal performance from each President’s time in office for clues as to how they plan to confront these challenges or how high of a priority fiscal responsibility will be on their agendas.

Both candidates approved substantial amounts of new borrowing in their first term. President Trump approved $8.4 trillion in borrowing over a decade, while President Biden has approved $4.3 trillion so far in his first three years and five months in office. Of course, accountability also rests with Congress as a co-equal branch of government, which passed legislation constituting the majority of the fiscal impact under both presidents.

Some of this borrowing was clearly justified, particularly in the early parts of the COVID-19 pandemic when joblessness was rising rapidly and large parts of the economy were effectively shut down. However, funding classified as COVID relief explains less than half of the borrowing authorized by either President, and arguably, a meaningful portion of this COVID relief was either extraneous, excessive, poorly targeted, or otherwise unnecessary. 5

In supplemental analyses, we will compare a number of other aspects of the candidates’ fiscal records. 

During the next presidential term, the national debt is projected to reach a record share of the economy, interest costs are slated to surge, the debt limit will re-emerge, discretionary spending caps and major tax cuts are scheduled to expire, and major trust funds will be hurtling toward insolvency. 

Adding trillions more to the national debt will only worsen these challenges, just as both Presidents Trump and Biden did during their terms along with lawmakers in Congress. The country would be better served if the candidates put forward and stuck to plans to reduce the national debt, secure the trust funds, and put the budget on a sustainable long-term path.

Appendix I : Details of Policies Approved by President Trump

• Tax Cuts and Jobs Act of 2017 ( $1.9 trillion debt increase )   – The TCJA included several tax cuts and reforms. Among those changes, the law reduced individual and corporate income tax rates, virtually eliminated the alternative minimum taxes, repealed or limited numerous deductions and tax breaks, replaced personal and dependent exemptions with an expanded standard deduction and Child Tax Credit, established a new deduction for pass-through business income, shrunk the estate tax, offered full expensing of equipment purchases, and reformed the tax treatment of international income. Most individual and estate tax changes were temporary while most corporate changes were permanent. The legislation also repealed the Affordable Care Act’s individual mandate penalty. As a result of these policy changes, the Congressional Budget Office (CBO) projected the TCJA would boost output by roughly 1 percent at peak and 0.6 percent after a decade. The estimate incorporated in this analysis includes the dynamic feedback effects of this faster growth, based on CBO’s April 2018 analysis of the bill. While it is impossible to know exactly how the bill’s fiscal impact compared to this prospective estimate, a number of factors point towards it adding significantly more to the debt, including: higher-than-expected inflation and nominal incomes and profits leading to higher revenue loss; SALT cap workarounds; increased use of bonus depreciation; and lower than expected revenue from limiting the use of pass-through losses. As a reference point, CBO’s latest estimate for extending the expiring elements of the TCJA is almost  50 percent higher than its 2018 estimate. In addition, the budgetary savings from the individual mandate penalty repeal were likely less than originally projected.
• The Bipartisan Budget Acts of 2018 and 2019  ( $2.1 trillion debt increase )   – The Bipartisan Budget Acts (BBA) of 2018 and 2019 increased the caps on defense and nondefense discretionary spending set by the 2011 Budget Control Act (BCA) and further reduced through a ‘sequester’ activated after the failure of the Joint Select Committee on Deficit Reduction. BBA 2018  increased the caps in FY 2018 and 2019 by a combined $296 billion,  effectively repealing the $91 billion per year sequester and further increasing spending above the BCA caps. BBA 2019 essentially codified these increases by  boosting the FY 2020 and 2021 caps by a combined $320 billion. Because the 2021 cap was the final year of the BCA caps, BBA 2019 increased baseline discretionary spending levels beyond 2021 to the new 2021 level plus inflation. Both bills also included smaller additional policies, including some partial offsets. In total, BBA 2018 added $418 billion to the ten-year debt and BBA 2019 added $1.7 trillion.
• ACA Tax Delays and Repeals  ( $539 billion debt increase )   – Three taxes enacted by the 2010 Affordable Care Act (ACA) – the health insurer tax, the “Cadillac tax” on high-cost health insurance, and the medical device excise tax – were delayed in a 2018 continuing resolution. They were subsequently repealed in one of the full-year funding bills for FY 2020. The Joint Committee on Taxation (JCT) estimated that the health insurer tax would have raised about $150 billion over a decade, the Cadillac tax would have raised $200 billion, and the medical device excise tax would have raised $25 billion. In addition to these tax repeals, policymakers enacted roughly $70 billion of other unpaid-for policies related to health care, retirement savings, and other priorities in these two bills. Interest costs added $64 billion more.
• Health Executive Actions  ( $456 billion debt increase )   – President Trump approved two health-related executive actions with significant costs over his term. Ending federal appropriations for the  ACA’s cost-sharing reduction payments in 2017 led insurers to raise premiums on “silver” ACA plans to fund low-income cost sharing subsidies, ultimately increasing the cost of federal subsidies by an estimated $220 billion. Meanwhile, a  2020 rule to restrict prescription drug rebates paid to pharmacy benefit managers and insurer plans was estimated to cost $177 billion. Interest costs added $59 billion more. Importantly, the rebate rule was delayed and ultimately repealed by Congress under President Biden.
• Other Legislation ( $310 billion debt increase )   – President Trump signed a number of other deficit-increasing bills into law over the course of his term. This includes several appropriations bills for disaster relief as well as the changes to mandatory programs (CHIMPs) that boosted spending in the full-year appropriations bills enacted in his term. Additionally, President Trump signed a permanent extension of several tax “extenders,” which are tax policies that have been routinely extended for short periods. Finally, he signed the Great American Outdoors Act, which transferred certain offsetting receipts and authorized them to be spent without appropriation, and the permanent authorization of the 9/11 victims fund, which authorized funds to pay out claims to 9/11 victims.
• Tariffs  ( $443 billion debt reduction )   – Over the course of his presidency, President Trump used his authority under the Trade Act of 1974 and the International Emergency Economic Powers Act of 1978 to increase a number of import tariffs through executive action. Beginning in 2018, the Trump Administration announced the imposition or increase to a variety of tariffs, including on washing machines, solar panels, and steel and aluminum products. In 2019, the tariff rate on many Chinese imports was increased from 10 percent to 25 percent. Based on CBO’s estimates at the time, we estimate these tariffs will have generated over $440 billion of revenue and interest savings over a decade.
• The CARES Act  ( $1.9 trillion debt increase ) – Enacted in the wake of the COVID-19 pandemic in March 2020, the bipartisan CARES Act included expanded and extended unemployment benefits, economic relief checks of $1,200 per eligible adult and $500 per child, the Paycheck Protection Program (PPP) to provide support to small businesses to keep employees on payroll, and emergency disaster loans and grants to businesses, industries, health care facilities, educational institutions, state and local governments, and others, among many other provisions. Based on our ongoing  tracking , the actual fiscal impact of the CARES Act was likely similar to the initial score though perhaps slightly higher overall.
• The Response & Relief Act  ( $983 billion debt increase )   – Enacted in December 2020 as part of the omnibus appropriations bill for Fiscal Year (FY) 2021, the  Response & Relief Act included funding for a second tranche of PPP payments and small business grants, an extension of enhanced unemployment benefits, economic relief checks of $600 per eligible person, funding support for schools and higher education institutions, vaccine and testing funding, targeted support to industries greatly impacted by COVID-19, an extension and expansion of the Employee Retention Credit, and an extension of various other COVID-related tax and spending relief programs. Based on our ongoing  tracking , the actual fiscal impact of the Response & Relief Act was likely higher than the initial score due to the significantly higher-than-expected deficit increase from the  Employee Retention Credit .
• Other COVID Relief  ( $756 billion debt increase )   – President Trump approved several other measures related to the COVID-19 pandemic and recession. This includes the three other COVID relief laws enacted in March and April 2020: the  Coronavirus Preparedness and Response Supplemental Appropriations Act , the  Families First Coronavirus Response Act , and the  Paycheck Protection Program and Health Care Enhancement Act . It also includes the student loan repayment pauses enacted at the onset of COVID and extended after the CARES Act’s pause ended in October 2020. President Trump also approved  other executive actions that resulted in little deficit impact. Based on our ongoing  tracking , the actual fiscal impact of these bills were likely much higher than the initial score due to the significantly higher-than-expected revenue loss from the  Employee Retention Credit and the higher Medicaid and SNAP costs resulting from a longer-than-projected public health emergency.

Appendix II : Details of Policies Approved by President Biden So Far

• Appropriations for FY 2022 and 2023  ( $1.4 trillion debt increase )   –President Biden signed full-year omnibus appropriations bills for  FY 2022 and  2023 , boosting nominal appropriations by 6 percent and then 9 percent. While those bills only set funding for those specific years, future-year projected levels are calculated by assuming continued inflation growth. This is consistent with the reality that appropriators generally work from the prior year’s spending levels. Based on CBO, we estimate the FY 2022 omnibus directly increased spending by $50 billion and indirectly by $519 billion above baseline, while the FY 2023 omnibus increased spending directly by $58 billion and base discretionary spending indirectly by $511 billion. Interest costs added $175 billion more. Both laws’ impacts on baseline deficits would be substantially smaller had they been scored against an updated CBO baseline that reflected actual inflation rather than projections – the bulk of the increases under both laws kept spending apace with the very-high rate of inflation for those years.
• The Honoring Our PACT Act ( $520 billion debt increase )   – Enacted in August 2022, the PACT Act created new benefits for veterans exposed to toxic substances during their tours of duty, expanded existing health and disability benefits, and modified eligibility tests that allowed more veterans to automatically qualify for benefits. Although veterans’ health spending is generally discretionary, the PACT Act allowed the cost of the expansion to be classified as mandatory spending and allowed lawmakers to  shift existing discretionary costs to the mandatory side of the budget. Based on CBO’s score, the PACT Act increased spending by between $277 billion and $667 billion, depending on how much funding was reclassified. Our estimate reflects the midpoint (plus interest), which policymakers effectively codified in the Fiscal Responsibility Act of 2023. 
• The Bipartisan Infrastructure Law  ( $439 billion debt increase )   – The 2021  Infrastructure Investment and Jobs Act authorized more than $500 billion of direct spending and tax breaks related to surface transportation, broadband, energy and water, transit, and other infrastructure. The law also increased baseline levels of highway spending, translating to more than $50 billion in indirect costs. While lawmakers claimed that it was fully paid for at the time of passage, CBO determined that it only contained $173 billion of scorable savings, leading to $439 billion of new borrowing when interest is included.
• Other Legislation  ( $422 billion debt increase )  –  President Biden signed several other bipartisan pieces of legislation during his first term. This includes  several   packages   of aid to Ukraine, Israel, and Gaza, additional emergency spending related to disaster relief and military readiness, $80 billion of investments and tax credits to encourage onshoring manufacturing facilities for semiconductors in the CHIPS and Science Act, and additional FY 2024 appropriations spending based on  “side deals” to the Fiscal Responsibility Act.
• Student Debt Actions ( $620 billion debt increase )   – The Biden Administration has instituted several changes to the federal student loan program through executive actions. Most significantly, the Education Department introduced the Savings on a Valuable Education (SAVE)  income-driven repayment (IDR) program , which reduced required payments and interest accrual for those enrolled, among other changes – estimated to cost $276 billion. In addition, President Biden extended the  pause of student debt repayments and cancellation of interest for 31 months at a cost of $146 billion. And finally, President Biden enacted a number of targeted debt cancellation measures, including expansions of the Public Service Loan Forgiveness program and cancellation of debt borrowed for institutions that closed or were found to be fraudulent, at a cost of $145 billion. President Biden also enacted a policy to cancel up to $20,000 per borrower of student debt that would have cost an additional $330 billion (after interactions with the SAVE plan), but this was ruled illegal by the Supreme Court. Recently, the Administration introduced  an alternative debt cancellation plan that could cost between $250 and $750 billion, though it has yet to be implemented and is not counted here because our estimates only include regulations that have been finalized through the full rulemaking process.
• Other Executive Actions  ( $548 billion debt increase )   – President Biden has also expanded deficits through other executive actions. Most significantly, he approved over $200 billion of borrowing by  changing the way Supplemental Nutrition Assistance Program (SNAP) benefits – also known as food stamps – are calculated and adjusted. More recently, the Administration announced a rule to limit vehicle emissions, which we estimate will add nearly $170 billion to the debt by boosting the cost of electric vehicle tax credits expanded under the IRA and reducing gas tax revenue. Other executive actions will add a combined $180 billion to the debt by expanding Medicaid enrollment, changing the way prescription drug price concessions are considered by Medicare plans, addressing the ACA’s “family glitch,” allowing states to boost Medicaid payments to managed care plans to pull in additional federal dollars, and an expansion of allowed income for Supplemental Security Income recipient households.
• Fiscal Responsibility Act ( $1.5 trillion debt reduction )   – In June 2023, President Biden signed the bipartisan Fiscal Responsibility Act (FRA), which capped discretionary spending for FY 2024 and 2025, among other changes. The FRA set 2024 nondefense discretionary levels to 5 percent below the 2023 level, set defense to be 3 percent higher, and set both to grow by 1 percent between 2024 and 2025. These  caps, along with other measures, were scored to generate over $250 billion of direct savings and also reduce the baseline for future spending to generate an additional $1.1 trillion of additional savings. With interest, the FRA was estimated to reduce deficits by $1.5 trillion over a decade. Importantly, negotiators at the time agreed to a number of  “side deals” mentioned above that would reduce the FRA’s savings to roughly $1 trillion if enacted in full in future appropriations bills. A different but similar set of side deals were enacted for FY 2024 and added about $85 billion to deficits – these are included in the “other legislation” category. Additional side deals will not be counted until enacted.
• Inflation Reduction Act  ( $252 billion debt reduction )   – In August 2022, President Biden signed the  Inflation Reduction Act (IRA ) into law, a reconciliation bill focused on energy, health care, and tax changes. The IRA established new and increased existing energy- and climate-related spending and tax credits, expanded ACA health insurance subsidies, required prescription drug negotiations and other drug pricing reforms, introduced a 15 percent corporate “book minimum tax,” established an excise tax on stock buybacks, increased funding to the IRS to close the tax gap, and made other changes. At the time of passage,  CBO and JCT estimated the IRA’s tax breaks and spending would reduce revenue and increase spending by about $500 billion, while its offsets would generate almost $740 billion. Recent estimates of the impact of repealing the IRA tax credits suggest these provisions will reduce revenue and increase spending by $260 billion higher than the official score; at the same time, the IRA’s offsets are also likely to raise more in revenue. On net, we expect a full re-estimate of the IRA would score as roughly budget neutral through 2031, excluding effects related to subsequent regulatory changes. This analysis attributes the additional cost of these regulations as executive actions.
• Deficit-Reducing Executive Actions  ( $129 billion debt reduction )   –President Biden approved two other executive actions that would result in savings over a decade, including changes to payments for Medicare Advantage plans and a temporary stay of the subsequently repealed Trump prescription drug rebate rule.
• American Rescue Plan Act  ( $2.1 trillion debt increase )  –  Enacted in the Spring of 2021, the American Rescue Plan Act was the final piece of legislation that contained many major components designed to provide COVID relief. It included several extensions of enhanced unemployment benefits, additional relief checks of $1,400 per person, and a slew of funding for state and local governments, educational institutions, health care providers, public health agencies, and others. The legislation also included  about $300 billion of policies that we have described as extraneous to the COVID crisis – including a pension bailout and expansions of the Child Tax Credit, Earned Income Tax Credit, health insurance subsidies, and child care tax credit – and roughly $100 billion of offsets.

Appendix III: Methodology 

This analysis estimates the additional borrowing approved by Presidents Trump and Biden through tax and spending changes passed by Congress or contained in executive actions from their administrations. It does not estimate the amount of debt that accumulated over their terms, which partially reflects actions taken prior to their time in office and does not account for the fiscal impact of the actions approved by the President but incurred outside of his four-year term. We will publish changes in debt during their terms in a supplemental analysis.

Our analysis incorporates all major pieces of legislation and executive actions – those with more than $10 billion of ten-year budget impact – approved by Presidents Trump and Biden. Estimates rely on ten-year budget scores, as under standard convention. In order to rely on official scores wherever possible, however, all estimates are based on the ten-year budget window at the time of enactment – meaning different policies cover different time frames and thus are not purely additive or comparable.

In general, estimates rely on official estimates from the Congressional Budget Office (CBO) and Joint Committee on Taxation (JCT) presented prospectively. When such scores are not available or not comprehensive, we may use estimates from the Office of Management and Budget, the regulatory agencies, or our own estimates. 

Estimates are not updated to incorporate data and results made available well after implementation; no legislation signed by either President Trump or President Biden has been re-estimated in full to incorporate observed costs or effects, and partial updates would bias the overall numbers. However, possible differences between initial scores and actual costs, including from the TCJA, the IRA, and COVID relief, are discussed throughout this paper.

Estimates incorporate impact on interest costs, which we calculate using the most recent CBO debt service tool at the time of enactment, unless interest impact is included in the estimate. Estimates are generally based on conventional scoring, but in the case of the Tax Cuts and Jobs Act, we incorporate macroeconomic impacts as estimated by CBO shortly after enactment.

All estimates are in nominal dollars at the time of approval, which means deficit impact from earlier budget windows generally represent a larger share of GDP per dollar due to higher price levels and output over time. 

Finally, the estimates are based on the policies as written and do not try to correct for arbitrary cliffs, side agreements, or other budget gimmicks that may create a misleading picture of the intended fiscal impact of the policy.

1 Our estimates compare ten-year estimates of each action before implementation, generally using prospective scores of policies and adding them together despite being over different windows. Although this is not a perfect apples-to-apples comparison for a variety of reasons, it allows us to rely on official numbers and continue to compare over time. See the methodology section for a more detailed explanation.

2 Many pieces of legislation with fiscal impact include tax and spending changes that both add to and reduce projected deficits. The $8.8 trillion figure is based on the net deficit impact of deficit-increasing bills, rather than the gross deficit increases within those bills. For example, the $1.9 trillion impact of the TCJA represents the combination of tax cuts, base broadening, lower spending as a result of repealing the individual mandate penalty, interest, and dynamic effects on revenue and spending.

3 The larger deficit impact from the TCJA is due to a combination of a larger nominal tax base, lower health savings from individual mandate repeal, the unexpected use of a SALT cap workaround, reduced revenue collection from the limit on pass-through losses, higher revenue loss related to bonus depreciation, and other factors.

4 Due to higher prices and output, greater demand for subsidized activities, and laxer-than-expected regulations, the IRA’s energy provisions are now expected to have a fiscal impact of  $660 billion – about two-thirds more than the original estimate of roughly $400 billion. This excludes the effects of the Administration’s vehicle emissions rule, which we’ve scored separately. At the same time, revenue collection under the IRA is also likely to be higher in light of  higher-than-projected nominal corporate profits , greater expected  voluntary tax compliance , and less-than-expected responsiveness to the buyback tax. Overall, we believe a re-estimate of the IRA would be roughly budget neutral. The emissions rule approved by President Biden would increase deficits by about $170 billion – mainly by further increasing the fiscal impact of the IRA tax credits – and is included in our tally of his executive actions.

5 In a previous analysis, we estimated that  $500 to 650 billion of COVID relief was extraneous – unrelated to the pandemic or subsequent economic fallout – including $300 to $335 billion enacted under President Trump and $200 to $315 billion under President Biden. These prior estimates are not perfectly comparable to estimates in this paper but give a sense of scale. In additional analyses, we estimated that the American Rescue plan likely  significantly overshot the output gap it was aiming to close while providing excessive relief to a number of sectors. There were also excesses and lack of targeting in earlier COVID relief packages, including as it related to  stimulus checks , the additional $600 of weekly  unemployment benefits , and the  Paycheck Protection Program.

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