- et al.
Summary
Background
Methods
Findings
Interpretation
Introduction
Public health initiatives and the development of cardioprotective medications have led to an increase in life expectancy in the past six decades, giving rise to an ageing population.
This ageing population is suffering from a different set of medical issues than the population a century ago, with cancer, coronary heart disease, dementia, and stroke being the four leading causes for mortality and morbidity in England.
In 2019, these four conditions accounted for 59% of all deaths and 5·1 million disability-adjusted life-years in England.
Research investment is essential to combat major public health challenges, facilitating the development of new treatments and interventions that can improve rates of prevention, treatment, or management of diseases, enhancing quality of life and reducing their economic burden. However, it is important that the distribution of research funding across diseases is proportionate to their respective impact on society. In 2008, a UK study (Dementia 2010) evaluated the economic costs of, and research investment into dementia, and compared these costs and investments with those for cancer, coronary heart disease, and stroke.
Such estimates are important to inform health policy and identify diseases in need of greater investment,
with successive UK Governments having placed a greater priority for research funding in dementia.
Methods
Analysis framework and data sources
CPRD Aurum is a large database of routinely recorded primary care electronic health records of patients from 738 general practices in England (10% of practices), covering 13% of the population.
The database contains information on symptoms, diagnoses, prescriptions, referrals, tests, immunisation, and medical staff. Primary care and secondary care diagnosis codes were used to identify the four conditions of interest. CPRD Aurum codes used to diagnose patients in primary care are reported in the appendix (pp 2–55). CPRD records were then linked to secondary care records contained in HES using Aurum (version 2.3) from August, 2019. In secondary care records, cancer was defined by ICD-10 category codes I00–I99, coronary heart disease by codes I20–I25, dementia by codes F00–F03 and G30, and stroke by codes I60–I69. The use of CPRD Aurum for this study was approved by the independent scientific advisory committee for CPRD research (protocol reference CPRD00120051). CPRD obtains annual research ethics approval from the UK’s Health Research Authority Research Ethics Committee (05/MRE04/87) to receive and supply patient data for public health research. No further ethical permissions were required for the analyses of these anonymised patient-level data. The analysis was based on 4 161 588 patients registered on Jan 1, 2018, in a CPRD general practice with HES-linked records, omitting all children younger than 1 year (appendix pp 56–57).
Informal and formal care information was obtained from the English Longitudinal Study on Ageing (ELSA).
ELSA collects data from people older than 50 years, with spouses from age 40 years also included, to understand all aspects of ageing in England. More than 18 000 people have taken part in the study since it started in 2002, with the same people re-interviewed every 2 years. For this study, we used information on wave 9 (2018–19; appendix pp 58–59). Access to ELSA, through the UK Data Service, was obtained as part of the UK Access Management Federation. ELSA has been approved by the National Research Ethics Service (London Multicentre Research Ethics Committee [MREC/01/2/91]).
Health-care resource costs
Nursing and residential care home costs
Of the more than 10 million people in England aged 65 years or older in 2018, 5% were living in a nursing or residential care home.
Using patient-level data from CPRD Aurum, we apportioned the proportion of people living in a nursing or residential care home in England due to cancer, coronary heart disease, dementia, and stroke (table 1; appendix pp 65–66). Nursing and residential home care home cost was valued at £837 per week,
taking into account the relative proportions of people living in nursing and residential homes,
and the local authority, not-for profit, and profit sector provision case mix.
Informal and formal care
Informal care costs were equivalent to the opportunity cost of unpaid care (ie, the time [work, leisure, or both] that carers forgo), valued in monetary terms, to provide unpaid care for relatives or friends with cancer, coronary heart disease, dementia, or stroke, and based on the conservative assumption that only patients limited in daily activities received care. We valued informal care using the proxy good method, in which an hour of informal care provided was valued using the labour market price of a close market substitute
(i,e. the mean hourly wage for a home care assistant [£7·85]).
Hence, for informal care, we multiplied the age-specific and gender-specific products of age-specific and gender-specific prevalence of cancer, coronary heart disease, dementia, and stroke in England;
the probability of living in the community (appendix p 66); the probability of being severely limited in daily activities as a result of each of the four conditions under study (appendix p 67); the probability of receiving informal care conditional on being limited in daily activities (appendix p 67); and the hours of informal care received, conditional on being limited in daily activities and receiving informal care (appendix p 67).
Formal care costs included the costs associated with paid care for patients living in the community, which was valued at £27·00 per h.
For formal care, we multiplied the age-specific and gender-specific products of age-specific and gender-specific prevalence of cancer, coronary heart disease, dementia, and stroke in England;
the probability of living in the community (appendix p 66); the probability of receiving formal care (appendix p 68); and the hours of formal care received, conditional on receiving formal care (appendix p 68).
Morbidity losses
Annual days off sick were obtained from the European Working Conditions Surveys.
To the total number of days of work due to sickness, we applied the proportion of absence that was attributable to cancer, coronary heart disease, dementia, and stroke, which was obtained from the UK Department of Works and Pensions (personal communication).
To calculate permanent absence from work due to sickness or disability, information on the numbers of working-age individuals receiving incapacity or disability benefits and not being able to work was obtained, including recipients of the disability living allowance, employment support allowance (ESA), and incapacity benefit by condition.
Given that recipients of ESA can work up to 45·82% of their time, we only included the proportion of time that was not worked.
Days of absence from work due to sickness or disability were multiplied by mean daily earnings.
Furthermore, for permanent absence, we used the friction period approach because absent workers are likely to be replaced, whereby only the first 90 days of work absence were counted.
Mortality losses
We assumed an initial working age of 15 years and a maximum age of retirement of 79 years. Age-specific and gender-specific deaths due to cancer, coronary heart disease, dementia, and stroke were obtained.
The number of potential working years lost was then estimated as the difference between the age at death and maximum age of retirement. Each lost year of working life was valued using average annual earnings.
However, not all of the population is economically active until age 79 years; hence, age-specific and gender-specific unemployment and activity rates
were applied to the potential foregone earnings. Following UK-recommended guidelines, future earnings lost due to mortality were discounted to present values using a 3·5% annual rate.
Statistical analysis
Finally, we projected the costs estimated for 2018 to 2050 based on future projections of the population alone,
excluding other factors such as epidemiological trends of the four conditions under investigation, risk factor prevalence rates, and life expectancy.
For this, we applied age-specific and gender-specific rates of resource use, prevalence, mortality, and disability observed in 2018 to the predicted distribution of the population in 2050. We valued resource use in 2050 using 2018 costs. For more details, see the appendix (pp 69–71).
Role of the funding source
Results
The population of England, excluding those younger than 1 year, is expected to increase from 55 million in 2018 to 65 million in 2050 (18% increase), with the population aged 65 years or older projected to increase by 49% (from 10 million to 15 million).
Assuming no changes in age-specific and gender-specific prevalence rates, this population increase will increase the number of people with cancer by 39% (2·0 million), coronary heart disease by 45% (2·3 million), dementia by 81% (1·2 million), and stroke by 41% (0·8 million; appendix p 69).
These increases in the overall disease prevalence will result in cost increases between 2018 and 2050 of 40% (95% CI 39–41) to £26·5 billion (25·7–27·3) for cancer, 54% (53–55) to £19·6 billion (18·9–20·2) for coronary heart disease, 100% (97–102) to £23·5 billion (19·3–25·3) for dementia, and 85% (84–86) to £16·0 billion (15·3–16·6) for stroke (table 3). Costs with the highest increases are those related to social care, which are projected to rise between 2018 and 2050 by 88% (95% CI 86–90) to £2·9 billion (2·7–3·3) for cancer, 91% (90–92) to £4·4 billion (4·1–4·6) for coronary heart disease, 110% (109–111) to £13·5 billion (12·1–14·8) for dementia, and 109% (107–108) to £7·1 billion (6·6–7·5) for stroke (figure 2).
Discussion
Whereas a previous study has assessed the overall costs of chronic conditions, our study made use of individual patient-level data to generate more precise cost estimates for cancer, coronary heart disease, dementia, and stroke, using the same methodology and sources across conditions. Previously the total costs of dementia in the UK were calculated as £23·4 billion, followed by cancer (£12·0 billion), coronary heart disease (£7·8 billion), and stroke (£5·0 billion).
These estimates are not comparable with the findings in this study, possibly due to methodologies and sources of data varying considerably across conditions.
Our results show that the areas of the economy bearing these costs differed substantially by disease area. For example, health-care costs of dementia accounted for 13% (£1·5 billion) of the total, with most costs being borne by the social care system (£6·4 billion, 55% of total costs). By contrast, in cancer, the majority of costs were borne by the labour market, with £8·3 billion in lost productivity (44% of total costs). These findings are notable in that they further emphasise the need for interventions designed to prevent or screen for early-stage disease. For cancer and, to a lesser extent, coronary heart disease, with so much of the cost borne by the labour market, interventions that prevent the disease will not only increase the health of the population and reduce health-care costs, but also improve labour productivity. However, these findings also raise important questions about perceived fairness and equality.
In the UK, about 90% of hospital cases, which according to our findings is where most of the care of patients with cancer or coronary heart disease takes place, is funded by the government (data are from the Eurostat database). By contrast, for dementia and, to a lesser extent, stroke, most of the care takes place in either the social care system, of which 60% is funded by the government, or by relatives and friends through informal care (data are from the Eurostat database). Therefore, patients with dementia and stroke are substantially at higher risk of having to fund their care themselves than those with cancer or coronary heart disease.
Our study also shows the effect of the projected population ageing over the coming decades. On the basis of demographic change alone, we project that the costs of cancer will increase by 40%, those of coronary heart disease by 54%, those of dementia by 100%, and those of stroke by 85%. With the population aged 65 years or older projected to increase by 49%, the costs with the fastest projected rise will be, averaged across all four conditions, for social care, with a 104% projected increase in costs, and informal care, with a projected increase of 78%. Therefore, research funding into interventions aimed to prevent, treat, and care for disease are required as a way to help to reduce or mitigate this projected increase in costs and improve health, especially in those conditions—ie, stroke and dementia—seeing the fastest increase in costs, and that historically have received the lowest levels of research funding.
The limitations of this study should be noted. Our results are based on diagnostic coding from both primary and secondary care records, rather than on careful ascertainment of patients through multiple and overlapping methods such as in population-based cohort studies. Therefore, our results might not reflect the absolute prevalence and costs of disease. Given that there is no single and simple diagnostic test for dementia, this under-ascertainment of disease in routinely collected health data or surveys might be most prevalent in dementia.
The failure to identify these undiagnosed cases might explain the relatively low levels of health-care resource use identified in CPRD Aurum due to dementia.
However, in ELSA, respondents were not explicitly asked for supervisory activities received, with our results likely to be an underestimate. We were unable to quantify the costs of formal and informal care in people younger than 40 years. This will, inevitably, have reduced our total estimates of costs, especially for cancer and stroke, where people younger than 40 years account for 6% (110 000) and 8% (60 000) of cases, respectively, compared with 2% (41 000) for coronary heart disease and less than 1% (5000) for dementia.
Finally, our projection of costs from 2018 to 2050 was based on future projections of the population alone, and might be considered simplistic. Our projections did not include other factors, such as epidemiological trends of the four conditions under investigation or the predicted rise in comorbidities predicted for England.
For example, analyses based on ELSA have projected the costs of dementia in the future based on current trends in cardiovascular disease incidence rates.
In addition, new treatments that prevent, slow progression, or successfully treat the four conditions under study, will undoubtedly affect the projected costs estimated in this study.
Source: https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(24)00108-9/fulltext