This chapter provides an overview of the burden of cancer on individuals, health systems and societies. Based on the results of the OECD Strategic Public Health Planning for non-communicable diseases (SPHeP-NCDs) model, the chapter presents the cancer burden for 51 countries, including OECD, European Union (EU27) and Group of 20 (G20) member countries. The burden is calculated as the impact of cancer on population health and mortality; quality of life in terms of mental health and personal financial security; health expenditure; and labour force productivity and participation.
Tackling the Impact of Cancer on Health, the Economy and Society
OECD Health Policy Studies
Abstract
Cancer places a large and growing burden on individuals, health systems and society
Cancer has a profound impact on individual health, wealth and well-being, but its effects extend beyond the individual toll. The growing cancer burden also has far-reaching consequences for population health, the economy and society. Understanding these wider impacts of cancer is helpful to support a whole‑of-government approach to improve cancer prevention and treatment. The OECD uses its Strategic Public Health Planning for Non-Communicable Diseases (SPHeP-NCDs – see Box 3.1) model to estimate the impact of cancer on individuals, health systems and society.
It is estimated that 11 people will be diagnosed with cancer every minute in the OECD over the next 30 years. The impact that this has on the quality of life and income for affected individuals is severe. The emotional toll of cancer, coupled with symptoms and side effects from treatment, can lead to heightened stress, anxiety, and depression. Every year, across the OECD, cancer is estimated to be responsible for an additional 160 000 cases of depression (85 000 in the EU). Cancer also has a significant negative impact on a person’s work life, leading to part-time work, unemployment and early retirement. In addition, cancer reduces the opportunities for continuous education and training. As a result, cancer is assessed to reduce the average annual wage of people in employment by EUR PPP 2 955 – roughly three weeks’ worth of income (EUR PPP 2 573 in the EU).
Cancer also places a considerable, and growing, strain on healthcare systems. Based on the results from the OECD SPHeP-NCDs model, health expenditure is estimated to be 6.0% higher in OECD countries relative to a situation where there is no cancer (4.7% in the EU). In total, cancer is estimated to increase health expenditure by EUR PPP 449 billion annually, on average, over the next three decades – more than the total annual health budget of France (EUR PPP 93 billion for the EU). Importantly, as populations age and the risk of developing cancer increases, the per capita health expenditure on cancer is expected to grow by 67% on average in the OECD between 2023 and 2050 (59% in the EU).
The impact of cancer extends beyond health and health expenditure. Cancer affects all facets of people’s lives, and this has consequences for the economy and society. Cancer reduces productivity and workforce participation of people with the disease, resulting in a loss equivalent to 3.1 million full-time workers across the OECD (1.1 million in the EU). This translates into a lost workforce output of EUR PPP 163 billion per year, broadly equivalent to the annual GDP of Hungary (EUR PPP 49 billion for the EU).
Cancer has a profound impact on individual health, economic prosperity and well-being, leading to pain, suffering, disability, and even death. But the impact of cancer extends beyond the individual toll, affecting many different parts of society. Cancer places a strain on healthcare systems, and productivity losses affect the economy.
In recent years, there has been a growing interest among governments and international organisations to measure the wider societal impacts of issues and policies (Stiglitz, Fitoussi and Durand, 2018[1]). For example, in 2018 New Zealand Government introduced the Living Standards Framework (LSF), to support the Treasury in considering the wide range of things that matter to New Zealanders (New Zealand Treasury, 2021[2]). Similarly, in 2021 the Canadian Government launched its Quality of Life Framework to better define and measure success (Government of Canada, 2021[3]). The 2019 conclusion of the Council of the European Union invites member states and the European Commission to include an economy of well-being perspective across all policies and to put people and their well-being at the centre of policy design (Council of the European Union, 2019[4]). The OECD has developed a Well-being framework to understand the full societal impact of policies (OECD, 2020[5]).
To understand the full impact that cancer has on health, the economy and society, this report uses the OECD Strategic Public Health Planning for Non-Communicable Diseases (SPHeP-NCDs) model (Box 3.1). The model quantifies the impact of cancer on population health, health expenditure, workforce, skills, wages, and on quality of life. This creates a comprehensive picture of the effects of cancer, going beyond the health sector. Using this information, policy makers can make the case for a whole‑of-government approach to better cancer prevention and treatment.
The OECD Strategic Public Health Planning for Non-Communicable Diseases (SPHeP-NCD) model is an advanced systems modelling tool for public health policy and strategic planning. The model is used to predict the health and economic outcomes of the population of a country up to 2050. The model consolidates previous OECD modelling work into a single platform to produce a comprehensive set of key risk factors (e.g. obesity, alcohol, tobacco, diet, pollution, physical activity) and their associated NCDs. The model covers 51 countries, including OECD member countries, G20 countries, EU27 countries and OECD accession and selected partner countries (Country coverage).
The model covers 14 types of cancer, selected based on their burden and amenability to public health interventions (e.g. action on risk factors, screening, vaccination): lung cancer, colorectal cancer, breast cancer, liver cancer, oesophageal cancer, pancreatic cancer, prostate cancer, stomach cancer, cervical cancer, malignant skin melanoma, larynx cancer, other pharynx cancer, nasopharynx cancer, and lip and oral cavity cancer. Together they account for around 75% of total cancer disability-adjusted life years (DALYs) and cancer deaths in OECD countries in 2019 (IHME, 2019[6]).
For each of the 51 countries, the model uses demographic and risk factor characteristics by age‑ and sex-specific population groups from international databases (Figure 3.1). For each cancer type, incidence and mortality data were obtained from the Global Burden of Disease (GBD) study (Murray et al., 2020[7]), and survival rates at 1, 3 and 5 years post diagnosis from IARC (IARC, n.d.[8]). These inputs were used to generate synthetic populations, in which each individual is assigned demographic characteristics and a risk factor profile. Based on these characteristics, an individual has a certain risk of developing a disease each year. These relative risks are based on the GBD study, amongst others, and only consider the direct relation between the risk factor and the disease. For example, the impact of low physical activity on cancer only takes into account the direct link between physical activity and cancer, and does not capture any of the impact that physical activity has on weight, obesity and, consequently, cancer.
Note: This schematic is highly simplified and focuses on the disease component – it does not reflect some other components of the model (including births, immigration, emigration, death, remission and fatality).
For each year modelled, a cross-sectional representation of the population can be obtained, to calculate health status indicators such as life expectancy, disease prevalence, mortality, and DALYs. The disease and demographic profile of the population also form the basis for the healthcare cost, labour market and other well-being outputs. Note that the model uses population predictions to adjust the size and demographic profile of country populations in the future, but maintains current (age‑ and gender-specific) rates for risk factors. In other words, it does not predict any future trends in risk factor prevalence with the exception of those caused by demographic changes.
Healthcare costs of disease treatment are estimated based on a per-case annual cost, which is extrapolated from national health-related expenditure data. The additional cost of multimorbidity is also calculated and applied, as is the extra cost of end-of-life care. In the model, people not dying from cancer can continue to consume medical care for other conditions (e.g. diabetes) and so continue to incur medical costs (for more details, see Box 3.5).
The labour market module uses relative risks to relate disease status to the risk of absenteeism, presenteeism (where sick individuals, even if physically present at work, are not fully productive), early retirement, employment, and contracted hours (e.g. working part-time or full-time). All these changes are combined using full-time equivalents. Average annual 2022 wages, adjusted for purchasing power parities (PPPs), are used to estimate the lost economic output.
To quantify the health, economic and the broader societal burden of cancer, a status-quo scenario (e.g. the current situation, with current cancer incidence rates) is compared to a hypothetical scenario in which there is no cancer (e.g. cancer incidence is set at zero). The difference between the two scenarios is the burden of cancer. Other chapters look at the impact of interventions (such as achieving certain risk factor targets), by comparing those scenarios to the status-quo.
For more information on the OECD SPHeP-NCDs model, see the SPHeP-NCDs Technical Documentation, available at: http://oecdpublichealthexplorer.org/ncd-doc.
Cancer is one of the main causes of death and disability in OECD countries (IHME, 2020[9]). Based on estimates from the OECD SPHeP NCDs model, there will be an estimated 5.6 million cases of cancer per year over the next three decades across the OECD – approximately one every 11 minutes -, and 2.0 million in the EU (Figure 3.2). Larger countries have a larger burden of cancer, with the United States facing 1.6 million new cases of cancer every year.
Number of cases of cancer (thousands) per year, per country, average over 2023‑50
Note: Rest for OECD includes GRC (47); CHL (47); PRT (44); SWE (44); CZE (43); CHE (41); HUN (39); AUT (38); DNK (29); NZL (27); NOR (26); FIN (25); ISR (25); IRL (24); SVK (22); CRI (15); LTU (9); SVN (9); LVA (6); EST (6); LUX (3); ISL (1). Rest for EU includes BGR (23); SVK (22); HRV (17); LTU (9); SVN (9); LVA (6); EST (6); CYP (5); LUX (3); MLT (2). Number of cases is limited to the 14 cancer types included in the OECD SPHeP NCDs model.
Source: OECD SPHeP NCDs model, 2024.
The vast majority of cases and deaths occur in older people. Nevertheless, people of all ages are at risk of dying from cancer. The OECD and Eurostat define premature or avoidable mortality as deaths occurring before the age of 75 (OECD/Eurostat, 2022[10]). Overall, cancer is estimated to account for 25% of all premature mortality in the OECD (26% in the EU) (Figure 3.3). The premature mortality rate from cancer is particularly high in Central and Eastern EU MS. High cancer premature mortality rates result from a combination of high cancer incidence (due to high risk factor prevalence for example, see also Chapter 5), low survival rates (due to less effective prevention and treatment, see also Chapter 4), and fewer competing causes of death (deaths from other non-communicable diseases (NCDs), infectious diseases and injuries). Central and Eastern EU MS tend to have relatively high levels of tobacco and alcohol use, lower than average survival rates, and a lower burden of infectious diseases and injuries than some other countries such as India, Indonesia, Mexico and Peru (OECD, 2023[11]; IHME, 2020[9]).
Premature mortality (deaths in people under the age of 75) due to cancer, per 100 000 population and as a percentage of total premature mortality, per year, average over 2023‑50
Note: Digestive includes liver, oesophageal, pancreatic, and stomach cancer; head and neck includes lip and oral cavity, larynx, other pharynx, and nasopharynx cancer; and other includes prostate, cervical cancer and malignant skin melanoma.
Source: OECD SPHeP NCDs model, 2024.
Across the 51 countries included in this report, 3.1 million people will die prematurely from cancer every year, over the next 30 years – of which 920 000 in OECD countries and 361 000 in EU countries. Lung cancer is by far the largest cause of premature deaths, accounting for 35% of all premature cancer mortality in OECD and EU countries, followed by colorectal and breast cancer (Figure 3.4).
Premature mortality (deaths in people under the age of 75) by cancer type in OECD and EU countries, total number of deaths and as a percentage of overall cancer premature mortality, per year, average over 2023‑50
Note: Number of cases is limited to the cancers included in the OECD SPHeP NCDs model, which covers an estimated 75% of DALYs.
Source: OECD SPHeP NCDs model, 2024.
In the OECD, 46% of cancer deaths are attributable to risk factors, and 47% of cancer deaths in the EU (IHME, 2019[6]). Tobacco is by far the most important risk factor for cancer, responsible for 27% of cancer deaths in the OECD and the EU (Figure 3.5). Occupational risks, diet, high fasting plasma glucose, high body-mass index (BMI) and alcohol use all account for around 5 to 6% of cancer deaths.
Percentage of cancer deaths attributable to risk factors, 2019
Note: Due to overlap between risk factors, their sum is greater than the total proportion of cancer deaths attributable to risk factors. Air pollution refers to ambient particulate matter pollution.
Source: IHME (2019[6]), Global Burden of Disease 2019, http://ghdx.healthdata.org/gbd-results-tool.
Resulting from the increased mortality, the overall life expectancy of OECD and EU populations is nearly 2 years lower than if there had been no cancer (Figure 3.6). This is more than a decade of progress in life expectancy: prior to the pandemic, life expectancy in OECD member countries increased on average 1.7 years between 2010 and 2019 (OECD, 2023[11]). In countries with a higher overall life expectancy, people are more likely to live to an older age and thus more likely to develop cancer. Therefore, cancer has a greater impact on mortality and, consequently, life expectancy.
The impact of cancer on the average population life expectancy in years, average over 2023‑50
Source: OECD SPHeP NCDs model, 2024.
The impact of cancer on the quality of life for affected individuals is severe. Simple tasks such as eating, sleeping, and engaging in leisure activities may become arduous due to symptoms like fatigue, pain, or nausea. This leads to significant disability, and consequently a reduction in healthy life expectancy (the number of years that a person lives in “full health”, which takes into account years lived in less than full health due to disease, see also Box 3.2). In the OECD and EU, cancer reduces healthy life expectancy by 1.6 years on average, but in some countries this is as high as 2 years (Figure 3.7).
Life expectancy is a measure of mortality, as it reflects the age at which individuals are expected to die. Healthy life expectancy combines mortality with morbidity. It uses disease‑specific disability weights to measure a disability-adjusted life expectancy.
For example, if someone is expected to die at the age of 60 years due to cancer, their life expectancy is 60 years. If they spend the last 2 years of their life with a cancer that has a disability weight of 0.5, their healthy life expectancy is 59 years (58 years in full health + 2 years at 50% reduced health).
Note that while healthy life expectancy is per definition lower than life expectancy, the change in life expectancy (as presented in Figure 3.6 and Figure 3.7) can be greater than the change in healthy life expectancy. This is because the disability weights of other diseases can “discount” life‑years gained.
Going back to the previous example, if the same person lives to 80 years in the “no cancer” scenario, this would be a gain of 20 life years. In the absence of other diseases, the gain in health life expectancy would be 21 years (80‑59). However, if at 70 they develop a different disease and live 10 out of the 20 additional years with a disability weight of 0.5, their new healthy life expectancy would be 75 (70 years in full health + 10 years at 50% reduced health). In this case, the gain in healthy life expectancy would be only 16 years (75‑59) – less than the gain in life expectancy. The presence of the other disease discounts the gain in life years.
The impact on the average population healthy life expectancy in years, average over 2023‑50
Note: Healthy life expectancy is the number of years that a person lives in “full health”. Years lived with disease are discounted based on a disability weighting specific for that disease.
Source: OECD SPHeP NCDs model, 2024.
Cancer and mental health
In addition to disability and death, cancer also affects patients’ mental health. The emotional toll of cancer, coupled with symptoms and side effects from treatment, can lead to heightened stress, anxiety, and depression. Evidence shows that depression is more common in cancer patients (Hartung et al., 2017[12]) and cancer survivors (Firkins et al., 2020[13]). A new diagnosis of cancer is associated with increased rates of depression for many reasons, including a fear of death, changes in social roles, physical health, life plans and work (Alwhaibi et al., 2017[14]). This higher risk of depression is persistent for more than five years after diagnosis (Maass et al., 2015[15]) (Firkins et al., 2020[13]).
It is estimated that cancer causes an additional 160 000 cases of depression annually in the OECD. The EU sees 85 000 additional cases of depression due to cancer per year. In the OECD, this equates to an age‑standardised rate of 13 cases per 100 000 people, per year (Figure 3.8). This rate varies significantly across countries, from roughly 1 per 100 000 in Indonesia and Colombia to more than 30 in the Slovak Republic and Portugal.
The impact of cancer on the number of depression cases (age‑standardised to OECD population, rate per 100 000 population) per year, average 2023‑50
Source: OECD SPHeP NCDs model, 2024.
Improving the mental health and quality of life of people living with cancer is a policy priority for many countries. This includes palliative and end-of-life care (Box 3.3), as well as follow-up care for cancer survivors. Cancer survivors face a number of challenges, including unmet psychosocial needs, emotional distress and obstacles to return to work among others (see also Box 4.6 in Chapter 4).
Palliative care aims to relieve suffering and improve the quality of life of patients with life‑threatening illness, without addressing the causes of the condition (WHO, 2020[16]). End-of-life care (EOLC) refers to the provision of palliative care in the last stages of life, while also including curative care (OECD, 2023[17]). Both are essential to provide physical, emotional, social, and spiritual support to those suffering serious illness. With ageing population and the rising prevalence of NCDs, the number of people requiring EOLC in the OECD is expected to grow from 7 million in 2019 to 10 million by 2050 (OECD, 2023[11]).
Despite the importance of EOLC, many countries face challenges in delivering accessible, people‑centred, high quality and appropriately financed services (OECD, 2023[17]). Less than 40% of those who need EOLC receive it in OECD countries. Despite a preference to die at home, half of all deaths happen in hospital, often due to a lack of in-home and community-based support. Quality of care is often suboptimal as many people receive undertreatment of their symptoms, insufficient psychological care despite high levels of distress, anxiety and depression, or aggressive treatment that is not likely to provide comfort, prolong life or be cost-effective (OECD, 2023[17]).
To address these issues, it is crucial that countries invest in training on EOLC across different professionals and care settings. (OECD, 2023[17]). This can help improve access to palliative care as well as its quality, as healthcare professionals who received training on end-of-life care are less likely to provide overtreatment and aggressive care at the end of life.
Improving payment systems to encourage the provision of more cost-effective services is paramount to ensure that end-of-life care is high-quality and financially sustainable. Evidence from Belgium, Canada and the United States shows that access to palliative care out of hospital settings reduced the use of intensive care units, medication, and overall health expenditures (OECD, 2023[17]).
Several countries have reformed financing to better align with patient wishes on their preferred location of care and death. For example, England introduced personal health budgets for end-of-life in five areas, which has resulted in 82% of people dying their preferred places, while being cost-neutral and even cost-saving compared to the normal approach of EOLC. Australia announced in 2021 an investment of AUD 56 million to improve the provision of palliative care at home, and France announced in 2022 a reinforcement of mobile teams to promote palliative care at home, as part of its action plan on end of life (OECD, 2023[17])
Finally, more research into EOLC at a local and national level is needed. This includes identifying workforce capacity needs, training needs, and linking data to give a fuller picture of end-of-life care across services. This could lead to better co‑ordinated and more evidence‑based services.
Source: OECD (2023[17]), Time for Better Care at the End of Life, https://doi.org/10.1787/722b927a-en.
A key focus of Europe’s Beating Cancer plan is the quality of life of cancer survivors. It aims to address the holistic needs of cancer survivors and promote their well-being by providing comprehensive survivorship care, supporting their social and economic integration, and advancing research and innovation in survivorship care (European Commission, 2021[18]). In Norway, both labour market reintegration and psychological support for patients with a history of cancer are important aspects of the cancer strategy Living with Cancer (2019‑22) (OECD, 2023[19]). In England, cancer alliances work with hospitals and primary care to offer health and well-being information and support before, during and after cancer treatment (NHS England, 2024[20]).
Cancer, work and earnings
Employment and income are crucial to the quality of life of an individual. The financial distress associated with low or no income can have detrimental effects on mental health and on quality of life. It also has knock-on effects on other essential needs, like housing and healthy food. Moreover, being unemployed can lead to social isolation, and can halt personal and professional skill development.
Unfortunately, cancer can have a significant negative impact on a person’s work life. Individuals diagnosed with cancer often require time off work for treatment, recovery, and medical appointments. Moreover, individuals may experience fatigue, cognitive difficulties, and other side effects that can affect their ability to perform at their usual level. The emotional and mental toll of cancer can have an impact on a person’s ability to cope with work-related stress and interpersonal relationships in the workplace. All of these factors can impact job performance and attendance, skills acquisition, career progression and income (potentially compounding the financial impacts of cancer-related out-of-pocket expenditures (Box 3.4)).
Health systems provide adequate financial protection when payments for healthcare do not expose people to financial hardship. A lack of financial protection can reduce access to healthcare, undermine health status, deepen poverty, and exacerbate health and socio‑economic inequalities. It can also lead to catastrophic health spending (CHE), where out-of-pocket payments exceed a predefined percentage of the resources available to a household to pay for healthcare (OECD, 2023[11]).
In addition to out-of-pocket spending on medical care, cancer patients can be faced with cost related to home care tasks, such as cleaning; making necessary home modifications for ease of living; as well as transport costs, parking fees, and accommodation for overnight stays and meals when health services are located far from a patient’s home (Alzehr et al., 2022[21]) (Bygrave et al., 2021[22]).
A substantial proportion of cancer patients and survivors experience financial hardship due to CHE (Gordon et al., 2016[23]). A systematic review found that an average of 23% of cancer patients incurred CHE in countries with a very high Human Development Index (Korea and the United States) (Doshmangir et al., 2021[24]). A survey of US cancer patients found that financial hardship was reported by 49% of participants (Khera et al., 2022[25]); while a Canadian survey found that high levels of financial burden exist for 33% of cancer patients (Longo et al., 2021[26]).
The consequences of financial hardship associated with cancer are both psychological (increased anxiety, depression, fatigue, and reduced quality of life) and medical, with high treatment costs adversely affecting patient adherence (Khan, Ramsey and Shankaran, 2023[27]).
Evidence shows that people with cancer are significantly less likely to be employed (Blinder and Gany, 2020[28]; Barnay et al., 2015[29]; Jeon, 2016[30]; de Boer et al., 2009[31]; Thandrayen et al., 2021[32]). In addition, even when a person remains in employment, cancer increases the likelihood of choosing part-time work and an early retirement (Thandrayen et al., 2021[32]; Mehnert, 2011[33]). Another study found that, within the five years following diagnosis, cancer patients had more absenteeism and more presenteeism (Soejima and Kamibeppu, 2016[34]). In France, 20% of people aged 18 to 54 who were employed at the time of their diagnosis were not in employment five years later (Institut National du Cancer, 2018[35]).
These impacts can be worse among people of minority or lower socio‑economic backgrounds. A study in the United States found that African American patients, and publicly insured or uninsured patients with breast cancer were more likely to experience diminished employment after 2 years of follow-up (Ekenga et al., 2018[36]). For breast cancer patients in Canada, a lower level of education was shown to be statistically significantly associated with a higher loss in wages (Lauzier et al., 2008[37]).
OECD analysis of the SHARE dataset – a survey covering over 20 European countries – and other longitudinal datasets shows that, adjusted for confounders, men and women with cancer are 7% and 10% less likely to be employed compared to people without cancer, respectively. Moreover, men and women with cancer who are in employment work on average 92% and 81% of the full-time equivalent, respectively, compared to 95% and 83% for men and women without any NCDs.
A reduction or loss of employment has a direct impact on a person’s income. A study of women with breast cancer in Canada found that women with unemployment, retirement and reduced working hours due to their cancer lost on average 27% of their annual income, even after taking into account various types of compensation (e.g. salary insurance, paid sick leave) (Lauzier et al., 2008[37]).
Even if they are employed, earnings of people with cancer are lower than those of people without cancer (Jeon, 2016[30]; Syse, Tretli and Kravdal, 2008[38]; Vaalavuo, 2021[39]). In the first post-diagnosis year, cancer survivors in employment were found to earn 10% less than their counterparts (Jeon, 2016[30]). The effect is stronger in people with low education, which can exacerbate existing financial inequalities (Syse, Tretli and Kravdal, 2008[38]; Vaalavuo, 2021[39]). While the bulk of the effect appears to only last two to three years after diagnosis (Vaalavuo, 2021[39]) (Jeon, 2016[30]), there is evidence showing that income remains slightly below that of non-cancer survivors for up to five years (Vaalavuo, 2021[39]).
It is difficult to say exactly how cancer reduces income. One way could be through its effect on participation in continuous education and training, and the associated skills acquisition. Participation in work-related training has been shown to be associated with higher wages (Goerlitz, 2010[40]; Denzler, Ruhose and Wolter, 2022[41]). As many cancer patients will experience a leave of absence (Kang et al., 2022[42]; Ferrier et al., 2021[43]), they may be less likely to participate in training compared to their peers. In addition, the psychological distress associated with cancer, as well as the impact of certain treatments, can diminish cognitive performance, such as impaired attention, processing speed, memory or executive functions (Kaiser et al., 2019[44]; Fleming, Edison and Kenny, 2023[45]). This could result in some cancer patients changing roles or missing out on promotions.
It is estimated that the impact of poor health on skills reduces the average annual wage by EUR PPP 2 955 for people with cancer (EUR PPP 2 573 in the EU), and by EUR PPP 12 on average for all people in employment (EUR 11 in the EU) (Figure 3.9)(see Annex 3.B for details on the methodology). This is a reduction in the annual wage received by individuals, not theoretical lost wage due to lower productivity or participation (for this, see Figure 3.12). At the population level this has noticeable consequences: across the OECD, nearly EUR PPP 7 billion in personal income is lost annually due to the impact of cancer on skills, and consequently wages. Countries with higher life expectancy and higher average wages see greater impacts of cancer on wages.
The impact of skills lost due to cancer on the average annual wage, EUR PPP per capita, average 2023‑50
Note: Average wages are calculated only for people in employment – the theoretical lower wages of people in very poor health who are unemployed do not contribute. For more details on the analysis, please see Methodology linking health and cancer to skills, work and income
Source: OECD SPHeP NCDs model, 2024.
Addressing issues of unemployment, low wages and skill acquisition in people with cancer is crucial for promoting economic prosperity and security, at both individual and societal level. There are steps that employers and governments can take to reduce the impact of cancer on people’s work life and income. Workplace accommodations, such as modified workstations, modified schedules, and reduced hours have been shown to significantly increase continued employment after a cancer diagnosis (Alleaume et al., 2020[46]). Evidence suggests that psycho-social interventions, including workshops, training, or counselling, can also have a positive effect on employment status (Fong et al., 2015[47]). Physical activity interventions, which aim to help decrease fatigue and emotional distress levels, have been shown to increase the return-to-work rate of cancer survivors (Wilson et al., 2022[48]).
Cancer carries direct cost for societies in the form of health expenditure on cancer care. As the burden of cancer is set to increase with ageing populations, so are the treatment costs associated with it. In addition, cancer affects health expenditure through its links to other diseases (notably depression, which is more prevalent among people with NCDs, including cancer). Moreover, people who do not develop and die from cancer may develop other conditions that may also require treatment. These additional effects are taken into account in calculations of how the burden of cancer affects overall health expenditure, using the OECD SPHeP NCDs model (Box 3.5).
In the OECD SPHeP NCDs model, health expenditure includes curative care, rehabilitative care, preventative care, ancillary services and medical goods. Importantly, it does not include long-term care.
Total health expenditure is predicted for each patient, based on age, gender and disease status. The total cost is the sum of disease‑specific cost, residual cost (which captures costs unrelated to risk factors, for example, the costs of treating migraines or common colds), and, where relevant, the cost of comorbidities and end-of-life related cost. Patient-level cost data from France, Estonia and the Netherlands was used to create the cost prediction formula.
The cost from France, Estonia and the Netherlands (the “anchor countries”) were extrapolated to other countries based on OECD data on inpatient curative and rehabilitative care spending per capita; outpatient curative and rehabilitative care spending per capita; and medical goods spending per capita. These three factors were weighted for each diseases using weights based on the OECD System of Health Accounts (SHA) data on the expenditure by disease, as well as the relative spend across the three for the anchor countries.
The burden that cancer exerts on overall health expenditure is calculated by comparing the baseline scenario to a hypothetical scenario in which there is no cancer. In the hypothetical scenario, people who would have otherwise died from cancer will live on and incur cost for other diseases. It also takes into account the impact that cancer has on other diseases, such as depression, and the cost associated with their treatment, as well as macro-trends in demographics and diseases. For these reasons, the cost of cancer presented in this report is not directly comparable to estimates which rely on allocating historic health spending to different diseases, such as those estimated under the SHA Framework.
For scenarios that focus on the impact of risk factors on cancer, only the cost associated with treating cancer are considered. This way the impact of cancer can be isolated from other NCDs also associated with the risk factors.
For more information, please refer to the online documentation for the OECD SPHeP NCDs model: http://oecdpublichealthexplorer.org/ncd-doc/.
The increase in health expenditure on cancer care between 2023 and 2050
As populations age and the cancer burden grows, so does the health expenditure needed to treat cancer. The per capita health expenditure on cancer care is expected to grow by 67% on average in the OECD (59% in the EU) between 2023 and 2050 (Figure 3.10). This is assuming the current standard of care and cost per case of cancer remain the same. Some countries, with a relatively low current burden of cancer, or with high expected ageing, see cancer cost grow even sharper: by around 150% in Türkiye and Korea, and by 190% in Saudi Arabia.
The percentage increase in cancer-specific per capita health expenditure, in 2050 vs. 2023
Note: Health expenditure includes curative care, rehabilitative care, preventative care, ancillary services and medical goods; and does not include long-term care. This graph shows cancer-specific health expenditure, which only looks at the treatment cost of cancer, and not changing in health expenditure on other diseases.
Source: OECD SPHeP NCDs model, 2024.
The burden exerted by cancer on overall health expenditure
The burden of cancer on overall health expenditure not only includes the cost associated with treating cancer, it also takes into account cancer’s impact on other healthcare expenditures. As cancer negatively affects mental health, there are additional cost for the treatment of depression. On the other hand, people who die prematurely from cancer do not incur other medical expenses later on in life.
On average over the period 2023‑50, health expenditure in OECD countries is estimated to be 6.0% higher due to the presence of cancer, and 4.7% higher in the EU (Figure 3.11). Per person, this equates to EUR PPP 222 per year in the OECD (EUR PPP 170 in the EU). This adds up to a total of EUR PPP 449 billion per year – more than the total annual health budget of France (EUR PPP 93 billion for the EU). Countries with higher average health expenditure, like the United States, Norway, Switzerland, and the Netherlands, also see a high per capita spend on cancer.
The impact of cancer on overall health expenditure, in EUR PPP per capita and as a percentage of total health expenditure, per year, average over 2023‑50
Note: Health expenditure includes curative care, rehabilitative care, preventative care, ancillary services and medical goods; and does not include long-term care. The estimates are calculated by comparing the baseline scenario to a hypothetical scenario in which there is no cancer, and therefore take into account the cost of other diseases as well as population dynamics. This can lead to an overall increase in health expenditure if, for example, people who do not develop cancer live longer and develop other diseases.
Source: OECD SPHeP NCDs model, 2024.
Reducing cancer incidence and improving outcomes is not just a health issue: it benefits the wider economy and society. The impact of cancer on a person’s work life, as discussed above, also has a societal cost. Through its impact on unemployment, part-time work, absenteeism, presenteeism and early retirement, cancer reduces a country’s workforce participation and productivity. When these effects are combined, OECD and EU countries lose the equivalent of 3.1 million and 1.1 million full-time workers due to cancer, respectively. Based on the country average wage, adjusted for purchasing power parities (PPPs), this equates to a loss in workforce output of EUR PPP 163 billion per year for OECD countries, broadly equivalent to the annual GDP of Hungary (EUR PPP 49 billion for the EU).
On a per capita basis, OECD countries lose on average EUR PPP 180 per year (EUR PPP 161 in the EU) (Figure 3.12). Countries with high wages combined with a high cancer incidence due to a higher life expectancy see greater impacts, up to EUR PPP 347 per capita per year in New Zealand. Although cancer increases the chance that a person retires early, it actually reduces the overall rate of early retirement in the population. This happens because cancer can cause people to die earlier, meaning fewer people make it to the age where they would typically retire early.
The impact of cancer on the workforce output through absenteeism, early retirement, employment (combining unemployment and part-time work) and presenteeism, EUR PPP per capita (working age), average over 2023‑50
Source: OECD SPHeP NCDs model, 2024.
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Most of the literature on the human capital-health relationship looks at educational attainment in children and young people, for example years of schooling attended or educational qualifications. Few studies look at the effect of health on skill development that occurs throughout the life course due to formal and informal learning. However, cancer and other NCDs are much more likely to affect older people than children in school. Therefore, the SPHeP-NCD model analyses the impact of poor health on skills in adult.
Health is linked to skills based on an analysis of the German “Nationale Bildungspanel”/“National Education Panel Study” (NEPS) data (NEPS Network, 2022[49]). This survey follows a cohort of adults and collects data on their skills, job outcomes and self-rated health. In this analysis, skills were measured using Warm’s weighted mean Likelihood Estimate (WLE) reading scores – a measure of literacy skills. Self-rated health was measured using a Likert rating on a scale of 1 (very good) to 5 (very poor). Models were adjusted for age, gender, socio‑economic background and educational level. The analysis found that people with very poor self-reported health had 0.64 lower literacy scores compared to people in better health, even when adjusting for confounding factors.
People in the very poor health group accounted for approximately 1% of the total sampled German population in the NEPS Cohort 6. However, this is likely an underestimation of the actual proportion of people in very poor health, assuming that people in this group are less likely to participate in a survey study. In the OECD SPHeP-NCDs model, very poor health was defined as having any active cancer; or the first year of a stroke; or two or more NCDs. According to this definition, the proportion of people in very poor health in the working age population is around 2% in Germany for the baseline scenario.
An analysis of PIAAC data shows that, on average, a one‑standard-deviation increase in numeracy skills is associated with a 10% wage increase among prime‑age workers, when correcting for educational level (Hanushek et al., 2015[50]). The evidence suggests that numeracy and literacy scores are closely correlated, both in PIAAC and NEPS (Lechner et al., 2021[51]). Since the standard deviation of the literacy score is 1 (Lechner et al., 2021[51]), a difference of 0.64 is therefore assumed to results in a 6.4% drop in wages. This drop was applied to data on country-specific average monthly wages from the International Labor Organization.
As health is also linked to the likelihood of being employed, and the number of contracted hours, the change in wages can be combined with a change in the employment rate and the hours worked, to estimate the impact of health on total income (income = wages x time in employment).