What will the cardiovascular disease slowdown cost? Modelling the impact of CVD trends on dementia, disability, and economic costs in England and Wales from 2020-2029.

BACKGROUND: There is uncertainty around the health impact and economic costs of the recent slowing of the historical decline in cardiovascular disease (CVD) incidence and the future impact on dementia and disability.
METHODS: Previously validated IMPACT Better Ageing Markov model for England and Wales, integrating English Longitudinal Study of Ageing (ELSA) data for 17,906 ELSA participants followed from 1998 to 2012, linked to NHS Hospital Episode Statistics. Counterfactual design comparing two scenarios: Scenario 1. CVD Plateau-age-specific CVD incidence remains at 2011 levels, thus continuing recent trends. Scenario 2. CVD Fall-age-specific CVD incidence goes on declining, following longer-term trends. The main outcome measures were age-related healthcare costs, social care costs, opportunity costs of informal care, and quality adjusted life years (valued at £60,000 per QALY).
FINDINGS: The total 10 year cumulative incremental net monetary cost associated with a persistent plateauing of CVD would be approximately £54 billion (95% uncertainty interval £14.3-£96.2 billion), made up of some £13 billion (£8.8-£16.7 billion) healthcare costs, £1.5 billion (-£0.9-£4.0 billion) social care costs, £8 billion (£3.4-£12.8 billion) informal care and £32 billion (£0.3-£67.6 billion) value of lost QALYs.
INTERPRETATION: After previous, dramatic falls, CVD incidence has recently plateaued. That slowdown could substantially increase health and social care costs over the next ten years. Healthcare costs are likely to increase more than social care costs in absolute terms, but social care costs will increase more in relative terms. Given the links between COVID-19 and cardiovascular health, effective cardiovascular prevention policies need to be revitalised urgently.

Introduction

The dramatic declines in cardiovascular disease (CVD) mortality in high income countries were a great success story of the late 20th century. However, since about 2011, that fall in CVD mortality stalled, with consequent slowing of improvements in life expectancy in England, Wales, United States [1] and elsewhere [2, 3]. This plateauing of CVD mortality appears to be mainly due to slowing in CVD incidence declines, rather than slowing in case fatality improvements among patients treated for CVD [4]. The underlying reasons for the mortality slowdown are disputed. However, adverse risk factor trends in obesity and type 2 diabetes [5, 6], may now cancel out the benefit of declines in smoking and hypertension prevalence. Furthermore, although these trends have been repeatedly documented, their overall effects on health care spending are uncertain.

Reducing CVD and dementia incidence are key goals of the English National Health Service [7]. Because smoking, diabetes, hypertension and obesity are shared risk factors for CVD and dementia, trends in their incidence are strongly related. Furthermore, CVD is a major risk factor for disability as well as dementia, and thus impacts both health care costs and social care costs [4, 8]. However, whether these changes in disease incidence will increase or decrease future health and social care costs is unclear. Increased disease incidence might raise survivor numbers and associated costs; but, conversely, increased mortality might reduce prevalence so that fewer people require care [9]. However, understanding how these costs evolve will be crucial for healthcare planning.

This paper thus aims to fill a key gap in the literature. by modelling and forecasting the health impact and economic costs of the recent slowing of the decline in CVD incidence in England and Wales. We have therefore linked individual-level health care and social care costs for participants in the English Longitudinal Study of Ageing (ELSA) [10]. Health care cost estimates have long existed according to disease [11]; however disaggregated social care cost estimates have only recently become available. We use a probabilistic health transition Markov model to forecast trends in diseases and how these epidemiological trends are likely to impact future spending. Our objective is to estimate inclusive economic costs, health and social care costs and quality-adjusted life years (QALYs) for the population in England and Wales from 2020 to 2029, and to estimate costs specifically attributable to CVD and dementia, as a consequence of the recent plateauing in CVD incidence rate. We have therefore compared two scenarios: 1. Assuming age-specific CVD incidence remains plateaued at 2011 levels, (continuing recent trends), or Scenario 2. Assuming age-specific CVD incidence continues to decline, following the longer-term trends, but only likely if CVD prevention policies are re-energised.

Methods

IMPACT BAM’s epidemiological methods have been validated and reported in detail previously [4, 12]. In this study we therefore focus on the additional economic developments. Table 1 lists the model inputs which are explained in more detail in Appendices 1 and 2 in S1 File.

Table 1

Summary of model inputs, with data sources, regression methods and distributions.

Full regression equations are shown in Appendices 1 and 2 in S1 File.

Model input	Source	Regression methods	Distributions
Prevalence of initial states	ELSA data fitted using curve fitting tool in MATLAB	N/A
Transition probabilities	ELSA data	Logistic regression
Healthcare costs
Hospital costs	ELSA-HES linkage	OLS regression	Beta with +/- 20%
Prescribing costs	ELSA combined with BNF	Two part Probit + OLS regression	Beta with +/- 20%
Social care costs (Cleaner/Homecare/Daycare)	ELSA data combined with PSSRU Reference costs	Two part Probit + OLS regression	Beta with +/- 20%
Residential care costs	ELSA data combined with PSSRU Reference costs	Probit	Beta with +/- 20%
Informal care costs	ELSA data combined with ONS GVA data	Two part Probit + OLS regression	Beta with +/- 20%
Utility index (EQ-5D) values	ELSA data combined with UK reference values from Janssen & Szende (2014), disease multipliers from Sullivan (2011) and Health Survey for England data on ADL deficits and EQ-5D index values.	Linear regression of HSfE	Fixed values only

We used a simulation modelling approach to forecast future healthcare, social care and informal care costs, and QALYs across the population under two diverging scenarios of future CVD incidence.

Simulations of health transitions for people aged 35–100 in England and Wales were carried out using the previously validated IMPACT Better Aging Model (BAM). This open-cohort, stochastic Markov model synthesises observed trends in incidence of CVD, dementia, disability and mortality, based on data from the English Longitudinal Study of Ageing (ELSA) [10] and Office for National Statistics (ONS). Model inputs for Wales were estimated using English ELSA and National Health Service Hospital Episode Statistics (HES) data, and ONS data that included Wales. The IMPACT BAM model uses ELSA data for information on health transition probabilities, and projects to the future using ONS demographic and mortality data.

Transition probabilities

Transition probabilities were obtained as a function of age and sex from incident cases between wave n and n+1 in ELSA. As with estimates of prevalence values, the transition probabilities obtained from pooling ELSA epochs were attributed to the mid-point of the data collection period. A new cohort of those reaching age 35 each year enters through the disease-free state (see model figure in Appendix in S1 File). The prevalence of cardiovascular disease and functional impairment is very low in this 35-year old cohort (<2% in total) therefore, the resulting error in misclassification is negligible. Movements between states occur every year in the model based on transition probabilities. The transition probabilities between states were calculated using logistic regression of 2-year incidence of CVD, cognitive impairment, functional impairment, and recovery from functional impairment, using the ELSA data with age, sex and current health state as coefficients (where dementia was classed as concurrent cognitive and functional impairment). A calendar effect was added where CVD incidence trends mirrored CVD mortality trends and cognitive impairment was set to decrease by 2.7% per year, based on trends in the ELSA data.

Probability of death was estimated using a three step model; for the first step, CVD and non-CVD mortality probabilities of CVD up 2025 in 5-year age bands were calculated using the Bayesian Age Period Cohort (BAPC) model, with ONS mortality and population estimates from 1982–2012 for England and Wales as inputs. In the second step, we calculated mortality rates from ELSA for the age groups 50–59, 60–69, 70–79, and 80–89 and fitted two logistic regression models, first including only sex, gender, and interactions, and secondly also including the model health state. The results of the first step gave probability of CVD and non-CVD death by sex, single year of age and calendar year, which were adjusted by the results of the second step to also give probability of CVD and non-CVD death by health state in the model. These methods were chosen to favour the population-level data from ONS but adjusting for the ELSA-specific data to estimate mortality risk by health state in the model.

The synthesised trends were projected from 2011 to 2029 (with outcomes measured for the remaining ten years of the NHS Plan from 2020–29) [13] based on trend data from ELSA waves 1–6 (2002/03 to 2012/13) and mortality trends from 1990–2016. There have been subsequent ELSA waves but these ones are the only ones that have been linked with resource use data. IMPACT BAM has eight health states: free of cardiovascular disease (CVD), cognitive impairment (CI) or functional impairment (FI); CVD; CVD and FI; CVD and CI; CVD and dementia; dementia; CI and FI; and two additional absorbing states of CVD death and non-CVD death. The main model outcomes were health and social care costs, value of informal care, and QALYs experienced. QALYs were valued at £60,000 based on UK Treasury Green Book [14] but with a sensitivity analysis using £30,000 which is often quoted as the threshold used by the National Institute for Health and Care Excellence (NICE).

Healthcare costs

Healthcare costs included all recorded costs for individuals, so were not specific to CVD and dementia, and include future healthcare costs, which is why the model is useful for answering whether preventing CVD saves costs in the longer term if it increases survival. The ELSA data was linked with Hospital Episode Statistics (HES) data (inpatient, outpatient, A&E) which was costed using NHS Healthcare Resource Groups (HRGs) for 2018/19 financial year. 80% of ELSA participants (14,789 of 18,529) gave consent for their records to be linked. Ordinary Least Squares (OLS) regression using data from the consenting sample was used to estimate the relationship between total hospital costs and health state, age and gender. These regression results were used to impute costs for those also in the non-consenting sample. ELSA respondents provide information on prescribed medications currently being taken. For each prescription, we use the British National Formulary (BNF) paragraph, section, and chapter number, and match this to the Net Ingredient Costs (NICs) contained in Prescription Cost Analysis compiled by the NHS Health and Social Care Information Centre. Total healthcare costs were then calibrated to estimates of total healthcare costs by age for the UK reported by the Office for Budget Responsibility [15] to account for missing costs like primary care, community, and other underreporting of costs in the linked ELSA-HES data. We do not assume any changes in costs over time due to new technologies, price or wage inflation, or other causes, so the modelling assumes that costs for each health state remain constant over time.

Social care and informal care costs

Age-related social care costs were estimated using reported social care contact hours from ELSA combined with Personal Social Service Research Unit (PSSRU) unit costs [16]. These were for five resources; cleaner, care/nursing home staff, other formal help, Local Authority-provided home care worker/ home help, and non-Local Authority home care worker/ home help. We added residential care costs to IMPACT-BAM from a logit regression of whether the ELSA member is currently living in institutional care with a representative sample of people living in institutions, but we ran the institutional care costs regressions by having the dependant variable as dummy of whether they were living in an institution at the time of the interview. We then assumed an average yearly cost of living in institutional care of £39,156 based on PSSRU reference costs (This is based on a 50:50 split between residential and nursing beds). The social care resource use does not include some costs such as costs of home improvements, and respite care. To account for the many ELSA respondents who report zero hours of social care use, we estimated a two-part model: (i) a probit for the presence of any social care use, and (ii) OLS regression for the amount of resource use for people who report non-zero hours of social care receipt. For daycare, data on receipt of daycare are available but not number of hours. We therefore used a probit model and applied an average annual cost from PSSRU reference costs (£7,280 in 2016/17 prices). The PSSRU estimates do not contain recent estimates for the costs of cleaners so we assumed an hourly cost of 1.5 times the national living wage.

We calculated informal care based on the number of hours of help that ELSA respondents reported they had received in the last week from up to 25 different people, ranging from spouses to neighbours. We assumed an average cost per hour of informal help of £7.76, based on data from ONS on gross value added of informal care less household inputs, and total number of hours of informal care. All health, social and informal care costs were inflated or deflated to 2019 prices using Treasury GDP Deflator (October 2018). The modelling was undertaken in real terms (i.e. in current prices) in line with the suggestion of the UK Treasury Green Book but with an additional sensitivity analysis where costs were discounted at 3.5% per annum. For probabilistic sensitivity analyses, costs were fitted to a beta distribution where the 95% uncertainty intervals represented +/- 20% of the median, which was applied in addition to epidemiological uncertainty around the proportion of the population in each health state in the model. The modelled costs for England were used as inputs for the England and Wales population in IMPACT BAM.

Calculating quality adjusted life years

Utility weights for QALYs were taken from the EQ-5D MEPS (Medical Expenditure Panel Survey) catalogue [17] and Health Survey for England [18]. Based on a linear regression of Health Survey for England 2012 (the most recent year that included all of these variables), the coefficient of EQ-5D index score for number of limitations in activities of daily living (ADLs) was -0.042 after controlling for health, age and gender. For each health state/age combination, we multiplied the population norm EQ-5D index score from Jannsen and Tzende [19] by an EQ-5D multiplier for cognitive impairment, dementia, or CVD (from the MEPS) and by the ADL decrements for the distribution of number of ADLs in that state.

QALYs were not discounted in the main scenario, but an additional scenario has QALYs discounted by 1.5% per annum and by 3.5% per annum, in line with NICE and UK Treasury Green Book. There was no probabilistic distribution added to the QALY weights because the uncertainty on QALY weights is very low, so any difference in QALYs in the results is driven only by epidemiological uncertainty.

Please see Technical Appendix for further details of our economic methods.

Scenarios modelled

We modelled undiscounted health and social care costs and QALYs for 2020–2029 under two scenarios:

Scenario 1. CVD Plateau–Assuming age-specific CVD incidence remains at 2011 levels, continuing recent trends.

Scenario 2. CVD Fall—Assuming age-specific CVD incidence continued to decline, following the long-term trends from 1991 to 2011.

The CVD plateau is the most likely of these two scenarios which were selected to give a comparison of the potential future trajectory for CVD trends. There are several countries such as Spain and France [20] where CVD has continued to decline beyond what has been achieved in England and Wales–although improvements have slowed across Europe—so it was felt that this comparison would be useful in understanding the costs of the slowdown in CVD improvements and the potential economic value of improvements that might be achieved in the NHS plan for England, if it was to produce a return to an improvement of the CVD trajectory. Fig 1 shows what the two scenarios mean in terms of CVD incidence, prevalence and mortality trends.

Fig 1

Modelled CVD incidence per 100,000 population aged 35–100, prevalence (% of people aged 35–100), and mortality per 100,000 population aged 35–100, from 2005 to 2030, comparing Scenario 2 (Continuing decline in CVD incidence) with Scenario 1 (plateaued CVD incidence).

Forecasting future costs

We calculated total costs for the whole England and Wales population as well as the specific excess costs of dementia and CVD. We estimated specific excess costs of dementia and CVD by comparing costs of individuals with dementia or CVD with the costs of individuals who were identical in age, gender and other disabilities who did not have dementia or CVD. Dementia was defined as the presence of both cognitive and functional impairment and the excess costs of dementia were estimated by comparing the same people as if they only had functional impairment.

Results

A continuing CVD plateau (Scenario 1) would mean that annual CVD incidence remains at around 1,200 per 100,000 people aged 35–100. CVD prevalence would increase slightly over time to around 9% of 35–100 year olds in 2029, reflecting demographic aging (Fig 1). Conversely, a further fall in CVD (Scenario2)-–would see CVD incidence decline to below 800 per 100,000. CVD prevalence would correspondingly fall to approximately 6% of 35–100 year olds (Fig 1).

Tables 2 and 3 show the healthcare costs per year from scenario 2 (CVD Fall)–in practice these are very similar in scenario 1 as well as they do not vary with changes in prevalence. The model suggests that in 2020 (the base year), total healthcare costs were approximately £5.3billion for CVD and some £1.7billion for dementia, while total social care costs were approximately £1billion for CVD and £5billion for dementia. The value of informal care was approximately £3.2billion for CVD and £3.5billion for dementia in 2020 (Table 2).

Table 2

Total cost of illness for CVD and dementia in 2020.

£billions (2019 prices).

Disease	Healthcare	Social care	Value of informal care	Value of Disease-Related QALYs lost	Total value of healthcare costs and QALY losses
CVD
All ages	5.29 (4.17 to 6.37)	1.03 (0.81 to 1.24)	3.25 (2.56 to 3.91)	6.51 (6.77 to 6.32)	16.08 (14.30 to 17.83)
Age 35–64	1.97 (1.53 to 2.41)	0.11 (0.08 to 0.13)	0.98 (0.77 to 1.21)	2.53 (2.79 to 2.35)	5.59 (5.17 to 6.11)
Age 65–79	2.18 (1.72 to 2.61)	0.34 (0.27 to 0.41)	1.32 (1.04 to 1.58)	2.65 (2.70 to 2.61)	6.50 (5.73 to 7.22)
Age 80–100	1.14 (0.90 to 1.37)	0.58 (0.46 to 0.70)	0.95 (0.75 to 1.14)	1.32 (1.34 to 1.30)	4.00 (3.45 to 4.52)
Dementia
All ages	1.71 (1.34 to 2.06)	5.06 (3.97 to 6.06)	3.51 (2.74 to 4.23)	4.20 (4.45 to 3.97)	14.48 (12.49 to 16.32)
Age 35–64	0.21 (0.15 to 0.28)	0.28 (0.20 to 0.37)	0.38 (0.27 to 0.52)	0.55 (0.69 to 0.43)	1.42 (1.32 to 1.61)
Age 65–79	0.85 (0.66 to 1.02)	1.62 (1.26 to 1.96)	1.60 (1.25 to 1.94)	2.08 (2.24 to 1.94)	6.15 (5.40 to 6.86)
Age 80–100	0.66 (0.52 to 0.79)	3.15 (2.48 to 3.79)	1.51 (1.19 to 1.82)	1.56 (1.62 to 1.51)	6.89 (5.81 to 7.90)

(95% uncertainty intervals in brackets). Results shown are from scenario 2 (CVD fall)–however results are broadly similar for both scenarios.

Healthcare costs are total NHS costs based on ELSA data linked with NHS England HES data. Social care costs are based on ELSA and include cleaner, care/nursing home staff, other formal help, Local Authority-provided home care worker/ home help, and non-Local Authority home care worker/ home help, as well as residential care. Informal care costs are based ELSA data multiplied by ONS estimates of gross value added per hour of care. QALYs are quality adjusted life years and are valued at £60,000 per QALY. Note that QALYs reflect only the uncertainty in the epidemiology, not uncertainty around the QALY impacts of disease, which is reflected in very tight uncertainty intervals.

Table 3

Excess cost (£, 2019 prices) per person, per year with CVD and dementia, 2020 (compared to if the same people did not have CVD and/or dementia).

Disease	Healthcare	Social care	Value of informal care	Value of Disease-Related QALYs lost	Total value of healthcare costs and QALY losses
CVD
All ages	2,330 (1,840 to 2,790)	454 (357 to 545)	1,433 (1,132 to 1,717)	2,868 (2,862 to 2,876)	7,087 (6,197 to 7,916)
Age 35–64	2,395 (1,891 to 2,869)	130 (102 to 158)	1,196 (943 to 1,434)	3,094 (3,090 to 3,099)	6,813 (6,029 to 7,552)
Age 65–79	2,309 (1,823 to 2,764)	362 (284 to 436)	1,395 (1,099 to 1,674)	2,806 (2,803 to 2,809)	6,868 (6,008 to 7,677)
Age 80–100	2,265 (1,788 to 2,711)	1,155 (907 to 1,389)	1,890 (1,487 to 2,262)	2,618 (2,616 to 2,621)	7,928 (6,799 to 8,978)
Dementia
All ages	4,209 (3,323 to 5,041)	12,417 (9,843 to 14,944)	8,626 (6,828 to 10,332)	10,360 (10,324 to 10,398)	35,602 (30,368 to 40,652)
Age 35–64	4,282 (3,382 to 5,139)	5,763 (4,558 to 7,007)	7,976 (6,264 to 9,598)	11,404 (11,383 to 11,427)	29,411 (25,566 to 33,137)
Age 65–79	4,229 (3,339 to 5,071)	8,137 (6,456 to 9,762)	8,044 (6,375 to 9,614)	10,493 (10,473 to 10,513)	30,901 (26,684 to 34,917)
Age 80–100	4,159 (3,282 to 4,981)	19,913 (15,720 to 23,863)	9,579 (7,556 to 11,448)	9,873 (9,872 to 9,874)	43,535 (36,441 to 50,132)

Results shown are from scenario 2 (CVD fall)–however results are broadly similar for both scenarios. Note some people have both CVD and dementia.

Data sources same as Table 1. QALYs are quality adjusted life years and are valued at £60,000 per QALY.

Compared with Scenario 2, a plateaued CVD incidence (Scenario 1) could result in approximately 16% higher average healthcare costs from 2020 to 2029, 1.1% higher social care costs, 2.8% higher costs of informal care, and 0.2% fewer QALYs experienced (Fig 2).

Fig 2

Modelled healthcare costs, social care costs, value of informal care, and QALYs, from 2020–2029, comparing Scenario 2 (Continuing decline in CVD incidence) with Scenario 1 (plateaued CVD incidence).

Healthcare costs are total NHS costs based on ELSA data linked with NHS England HES data. Social care costs are based on ELSA and include cleaner, care/nursing home staff, other formal help, Local Authority-provided home care worker/ home help, and non-Local Authority home care worker/ home help, as well as residential care. Informal care costs are based ELSA data multiplied by ONS estimates of gross value added per hour of care. QALYs are quality adjusted life years experienced per year, across the whole population, aged 35–100.

Excess healthcare costs of CVD and dementia per person-year (the costs compared to a counterfactual where an individual in the same age group did not have CVD, or dementia) would be similar across age groups (approximately £2,300 for CVD and £4,200 for dementia). However, social care costs would increase across age groups from approximately £130 per person in 35–64 year olds to around £1,155 per person-year in 80–100 year olds for CVD, and from approximately £5,763 to £19,913 for dementia. Informal care costs would increase from approximately £1,196 in 35–64 year olds to £1,890 for 80–100 year olds for CVD and increase from approximately £7,976 in 35–64 year olds to around £9,579 in 80–100 years old with dementia (Table 3).

Table 4 shows the cumulative costs of CVD, dementia and total costs across the whole population (including people without CVD or dementia) for the two scenarios. Compared with Scenario 2, the plateau in CVD mortality since 2011 (Scenario 1) is projected to produce a cumulative net monetary cost of around £54 billion (95% uncertainty interval £14.3-£96.2 billion), made up of approximately £13 billion (£8.8-£16.7 billion) healthcare costs, £1.5 billion (-£0.9-£4.0 billion) social care costs, £8 billion (£3.4-£12.8 billion) informal care and £32 billion (£0.3-£67.6 billion) value of lost QALYs in the ten years from 2020 to 2029. Of these costs, cumulative CVD-specific costs (including value of QALYs lost) are projected to be approximately £39 billion higher with the CVD plateau, whereas dementia-specific costs are projected to be actually slightly lower (£0.6billion), reflecting fewer patients surviving to old age. Sensitivity analyses with 1.5% (QALYs) and 3.5% (costs) discount rates, and lower QALY valuations of £30,000 per QALY, are shown in Appendix 3 in S1 File. Using discounted instead of undiscounted costs and QALYs only slightly reduced the difference between the scenarios, while valuing QALYs at £30,000 reduced the net monetary cost difference between the scenarios to around £37.7billion.

Table 4

Total cumulative undiscounted health and social care costs, value of informal care, and value of QALYs (where 1 QALY valued at £60,000) for adults aged 35–100 in England and Wales, over 10 years from 2020–2029.

Population	Scenario	Healthcare	Social care	Value of informal care	Total costs	Value of QALYs lost (billions)
	Scenario 1	62.9	12.1	38.3	113.2	77.2
		(49.8 to 75.5)	(9.5 to 14.5)	(30.3 to 46.0)	(89.6 to 136.0)	(79.5 to 75.5)
CVD	Scenario 2	49.7	10	30.9	90.6	61.2
		(39.2 to 60.0)	(7.8 to 12.0)	(24.3 to 37.1)	(71.2 to 109.0)	(63.5 to 59.5)
	Difference (1–2)	13.1	2.1	7.4	22.6	16.1
		(9.8 to 16.7)	(1.6 to 2.7)	(5.6 to 9.4)	(17.0 to 28.7)	(13.1 to 19.0)
	Scenario 1	17.4	52.7	36	106.1	42.8
		(13.8 to 21.1)	(41.7 to 63.4)	(28.6 to 43.5)	(84.1 to 128.1)	(45.3 to 40.4)
Dementia	Scenario 2	17.7	52.5	36.1	106.3	43.1
		(13.9 to 21.3)	(41.2 to 63.2)	(28.3 to 43.5)	(83.4 to 127.8)	(45.8 to 40.6)
	Difference (1–2)	-0.3	0.1	-0.1	-0.3	-0.3
		(-1.6 to 1.2)	(-2.5 to 2.8)	(-2.7 to 2.5)	(-6.7 to 6.3)	(-3.7 to 3.4)
	Scenario 1	800.6	141.9	285.1	1,228.00	-15,247.30
		(631.8 to 960.6)	(112.4 to 170.4)	(225.8 to 342.7)	(970.4 to 1,472.0)	(-15,222.2 to -15,270.9)
Whole population aged 35–100	Scenario 2	788.5	140.6	277.5	1,206.60	-15,279.40
		(623.1 to 943.6)	(111.1 to 168.2)	(219.4 to 332.1)	(952.6 to 1,443.0)	(-15,253.9 to -15,304.3)
	Difference (1–2)	12.5	1.5	7.7	21.6	32.3
		(8.9 to 16.7)	(-0.8 to 3.9)	(3.3 to 12.5)	(13.0 to 31.9)	(-3.5 to 69.6)

£billions in 2019 prices (95% uncertainty intervals in brackets). Comparing Scenario 1 –CVD Plateau, with Scenario 2- CVD Fall.

Note: QALYs are quality adjusted life years. QALYs for CVD and dementia are QALYs lost through disease; QALYs across the whole population is QALYs experienced, so is displayed as a negative value, as it is QALYs lived rather than lost. Data sources same as Table 1.

Discussion

Summary of results

After previous, dramatic falls, CVD incidence and mortality have recently plateaued in the UK. This slowdown could substantially increase health and social care costs over the next ten years, and cumulatively cost approximately £54 billion by 2029. The additional £22 billion in health, social and informal care costs, would represent about a 1.6% increased demand on NHS and social care budgets, which are already strained. However, the biggest costs would be approximately 540,000 lost QALYs (reflecting worse quality-of-life from higher rates of CVD and disability, and more life-years being lost through increased mortality). The immediate impact would particularly hurt the NHS, with a more distal and delayed impact on informal care and social care.

Comparison with other studies

Our results generally endorse and expand on previous studies of CVD and dementia costs. The MODEM study estimated the total (not excess) costs of people with dementia in England to be approximately £24 billion in 2015, made up of £10 billion unpaid care, £10 billion social care and £4 billion in health care costs [21]. Healthcare costs per person per year were £3,025 for people with mild dementia, up to £4,800 for severe dementia, and £4,800 for all care home residents with dementia. These costs are similar to our excess healthcare costs of approximately £4,400 for dementia. Luengo Fernandez et al. [22] reported a similar figure of 17,000 Euros for the combined health and social care cost per dementia patient for the UK in 2007.

Total healthcare costs in our study for people aged 35 and over were around £80billion per year, similar to NHS data in 2017/18 showing costs of approximately £108 billion in England and £7billion for Wales for all ages [23].

Our total costs of social care for 35–100 year olds were approximately £14 billion per year. This was slightly lower than the £22 billion quoted for England in 2017/18 [24]. (However, almost half that spend was on people aged under 65 [25]).

Our total cost of informal care for adults aged 35 and over was approximately £28 billion per year for England and Wales, slightly lower than the corresponding ONS household accounts figure of some £55billion; that however included all adults aged 18 and over [26].

Strengths and weaknesses

Our study uses a single source of data, from a large representative survey of older people in England linked to administrative healthcare records, to estimate both incidence of CVD and dementia, and associated health and social care use. This enhances both precision and internal consistency. The use of administrative data to cost healthcare use also provides more accurate estimates than sample data alone. Conversely, most previous studies relied on a patchwork of data sources. The IMPACT-BAM model accounts for complex epidemiological interactions between CVD, dementia and disability, notably lag times and competing risks.

Our model estimates of dementia and CVD prevalence were previously validated by comparisons with real life data from HES and Cognitive Function and Ageing Studies (CFAS), showing a good level of agreement [12]. Furthermore, our model formally accounts for the uncertainty of input parameters by using rigorous probabilistic sensitivity analyses. Furthermore, being able to quantify excess costs meant that we could isolate the true cost impact of dementia.

This study also has limitations, notably that our cost estimates are likely to be conservative because we a) did not include the effect of healthcare or social care on changing the QALYs experienced; b) assumed no change in healthcare services efficiency over time (either reduced costs, or increased health per unit of spend), nor changes in costs due to new technologies or inflation; c) based some cost estimates on self-reported resource use rather than direct data collection [27]. Finally, while ELSA is reasonably representative of the non-institutionalised population, it may predict residential care prevalence and costs less well. We have included some one-way sensitivity analyses (value of QALY, discount rate), as well as the probabilistic sensitivity analysis; but more one-way sensitivity analyses may highlight the drivers of differences in outcomes in more detail. We plan to produce a future study to explore these drivers more, using decomposition analysis.

Implications for policy makers

The Covid-19 pandemic has clearly changed the overall mortality and health and social care costs trajectory for England and Wales. Given that CVD is a risk factor for many conditions and disabilities, including excess risk of covid-19 death [28] it is important for policy makers to prevent as much CVD as possible in the coming decades. If the recent plateau in CVD incidence and mortality is allowed to persist, that could substantially increase healthcare and social care costs, and the opportunity costs of informal care. Conversely, introducing CVD prevention policies of proven effectiveness could continue the historical decline in CVD and decrease healthcare and social care requirements. Such investments would actually be cost-saving [29].

There is ample room for further reductions in CVD incidence and mortality. Populations similar to ours have CVD rates 50% lower (Singapore) and even 80% lower (Qatar) as highlighted by the Global Burden of Disease studies [30]. The recent UK increases in obesity, diabetes and associated hypertension must therefore be considered key targets for a renewed CVD and dementia prevention strategy. Indeed, as 90% of CVD can be explained by dietary and behavioural risk factors [31], the focus on primordial and primary prevention is now more urgently needed than ever [13]. Improving diet by increasing fruit and veg intake, reducing salt and processed food intake, as well as tobacco control interventions, and reducing sedentary behaviour [3], are examples of cost effective interventions to reduce CVD. There are also clinical interventions that can reduce the case fatality ratio such as risk stratification, blood pressure and lipid control, and revascularisation [32]. Persistent policy stasis will likely worsen the already large inequalities in CVD and dementia [33]. It will also add further pressure to our strained healthcare system. Dementia is now the leading cause of death for women in England and Wales and since it shares risk factors with CVD, it may be that future dementia mortality will be even higher than predicted [34, 35].

Our study may help inform resource allocation decisions. Regardless of scenario, investments in social care may need to increase even more quickly than investments in healthcare. We hope our evidence regarding epidemiological shifts and future health and social care costs might also prove useful for policymakers planning greater integration of health and social care.

Future research

Future research could use our IMPACT-BAM model to look further at health inequalities, and produce more granular estimates at regional or at local authority levels, especially given the increasingly local input to resource allocation [36]. Future modelling studies could estimate the potential of policy and clinical interventions that may change future prevalence of CVD and dementia, for instance around diet, physical activity, or cognitive training, to provide policy makers with comparative options to more comprehensively inform their actions [37].

The excess costs of CVD and dementia by age used here may be of interest to health economists as potential model input parameters. For instance, an intervention to prevent CVD in people aged 65–79 might save approximately £2,300 per person-year in healthcare costs; or an intervention to prevent dementia in people aged 80–100 might save around £9,900 per person-year in QALY losses (Table 2).

It might also be useful to look further at productivity impacts. A high proportion of the ELSA sample are above typical working age so there may not be significant lost earnings through ill health. However, there are still household productivity impacts, and friends or relatives providing informal care who lose potential earnings. Modelling these dynamic relationships between social care and informal care in more detail, using time-use survey data might be useful.

Conclusions

Our analysis suggests that the recent slowdown in CVD improvements could generate substantial additional human, health and social care costs over the next decade. Furthermore, social care costs for older adults may grow twice as fast as healthcare costs over the next decade, regardless of future improvements. Living with CVD also means a greater risk of other age-related disabilities.

Though challenging, funding policy for health and social care needs to be urgently addressed. Finally, while addressing the existing burden of CVD, dietary and tobacco control policies to achieve substantially better CVD prevention will need to be intensified.

Technical appendix.

(DOCX)

Click here for additional data file.

Info on model R markdown file.

(MD)

Click here for additional data file.

Initial distribution.

(CSV)

Click here for additional data file.

Initial population size.

(CSV)

Click here for additional data file.

Population projections.

(CSV)

Click here for additional data file.

Transition probabilities CVD fall 2006–18.

(CSV)

Click here for additional data file.

Transition probabilities CVD fall 2019–30.

(CSV)

Click here for additional data file.

Transition probabilities CVD plateau 2006–18.

(CSV)

Click here for additional data file.

Transition probabilities CVD plateau 2019–30.

(CSV)

Click here for additional data file.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

5 Nov 2021

3) Please justify the beta distribution type and OLS for modeling costs. Describe also here how uncertainty was modeled within PSA. Which parameters contributed to the uncertainty and which were assumed to be fixed? How was correlation between parameters incorporated?

4) Please include more details about the 2 modeled scenarios as indicated by Reviewer 1 and what the implications of assumptions are in the model. Also please clarify whether and which costs depend on gains in life expectancy.

5) Better justify use/non-use of discounting, time horizon and monetary WTP value for a QALY.

6) Please add one-way sensitivity analyses of key parameters as suggested by Reviewer 2.

In addition, several more textual suggestions were made by both reviewers.

Please submit your revised manuscript by Dec 20 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Bart Ferket

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please amend the manuscript submission data (via Edit Submission) to include authors: Piotr Bandosz, Maria Guzman-Castillo, Jonathan Pearson-Stuttard, George Stoye, Jeremy McCauley, Sara Ahmadi-Abhari, Marzieh Araghi, Martin J Shipley, Simon Capewell, Eric French, Eric J Brunne, Martin O’Flaherty

3. Thank you for stating the following in the Competing Interests/Financial Disclosure section:

“All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: 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. Dr Collins is secondment as Head of Health Economics in Welsh Government, this paper does not represent any views of Welsh Government. Dr Pearson-Stuttard is also Head of Health Analytics at Lane Clark & Peacock LLP, vice-chair of the Royal Society for Public Health and reports personal fees from Novo Nordisk A/S, all outside of the submitted work.”

We note that one or more of the authors are employed by a commercial company:Health Analytics at Lane Clark & Peacock LLP

a. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study is a model-based analysis of the economic implications of the recent trend in CVD incidence in England and Wales compared to a counterfactual of persisting long-term trend. Using a probabilistic Markov model (IMPACT-BAM), the study simulated and compared two scenarios: 1) CVD Plateau, where CVD incidence remains at 2011 levels, and 2) CVD Fall, where CVD incidence follows the long-term trends and continues to decline. It concludes that the slowdown in the decline of CVD incidence is associated with substantial costs.

The topic is potentially interesting, and the findings could help guide priority setting in the local setting. Unfortunately, key methodological details were not sufficiently described in the text to determine the validity of the study. I have included some major issues mostly around the methods and other comments and questions in the following section.

Major issues:

1. The methods section lacks essential details. I understand that the study used a previously published simulation model, but it was unclear how the two scenarios were implemented exactly in this particular study. For example, how was the trend of CVD incidence in the two scenarios fitted statistically? What is the risk profile of those who develop CVD in scenario 1 compared to scenario 2? Considering that the comparison between these two scenarios is the central question of this study, it was very difficult to determine its methodological validity.

2. It was also unclear how CVD and dementia were correlated in this analysis, i.e., how a change in CVD incidence trend between two scenarios affects the health and cost outcomes of dementia, and how this effect was operationalized in the simulation model.

3. A decrease in CVD incidence is associated with longer life expectancy, but this prolonged life expectancy also comes with some future unrelated healthcare costs: if we prevent individuals from developing CVD, they may still seek care and incur health spending because of other conditions. Not accounting for such costs may bias the results in favor of the CVD Fall scenario. I could not find whether (and if yes, how) this study includes such costs from the description of the methods.

4. Since this study projects into the next 10 years to calculate the economic impact of CVD trends, one would think that discounting is crucial to account for time preferences. However, it was not clear why discounting was not performed, and I think it would help to see both discounted and undiscounted results.

5. The manuscript requires significant copyediting. References are sometimes misaligned (e.g., Introduction -> paragraph 3 -> line 3, the ELSA study is referenced to #8, but should be #11 instead). Many sections/paragraphs lack structure and focus -- the “Implications for policy makers” section is a prime example consisting of scattered paragraphs of just 1-2 sentences; they should be combined to indicate a clear flow of logic. Another example: the “Social care and informal care costs” section includes some methodological descriptions of the QALYs, which should belong to the next section. Some abbreviations are used before they are defined (e.g., “HES”). Additionally, there are grammar errors (incorrect punctuations and capitalizations, etc.) throughout the manuscript.

Other comments:

1. The study uses £60,000/QALY as the threshold to value QALYs and references the UK Treasury Green Book. However, the most often used threshold we see from the UK is £30,000/QALY recommended by NICE, substantially different from £60,000/QALY. So, what is the rationale for using £60,000/QALY? I also wonder how the results would change if alternative thresholds were used.

2. Why were costs fitted to beta distributions? Beta distributions are more commonly used for probabilities and utility weights in probabilistic sensitivity analysis, and Gamma or Log-normal are more appropriate for costs.

3. I’m having a hard time understanding what this sentence means: “The QALYs reflect only the uncertainty in the epidemiology, not uncertainty around the QALY impact of disease, which is reflected in very tight confidence intervals.” Is this essentially saying that there’s no probabilistic distribution added to the QALY weights because the uncertainty on QALY weights is very low? In any case, this sentence should be reworded to improve clarity.

4. Tables 1 and 2: These tables present results for a “base case scenario.” It’s confusing because a base case scenario is not described and defined in the methods section.

5. Scenario 2 is a hypothetical scenario where CVD incidence declines at a rate close to historical levels. Even though the discussion section touches briefly on the aspects CVD prevention could focus on, scenario 2 seems like a very hypothetical and unrealistic scenario without some justification on *how* this decline could be achieved. More and deeper discussion on this could be helpful, and maybe a somewhat more realistic scenario could be of more policy interest.

6. The “Future research” section claims that “Future research could use our IMPACT-BAM model to look further at health inequalities, and produce more granular estimates at regional or at local authority levels, especially given the increasingly local input to resource allocation.” It was unclear, though, whether the authors have made the IMPACT-BAM model publicly available for such uses. I could not find a user interface for the model from an online search.

Reviewer #2: This is a well-conducted and important study that quantifies the costs and QALYs associated with persistent plateauing versus continued decline of cardiovascular disease rates in England and Wales.

Below I provide some comments on each section of the abstract. While most of these are minor recommendations, I strongly recommend that the simulation procedure is more clearly explained in the Methods section and that sensitivity analysis is conducted to better quantify the contribution of individual model parameters to overall uncertainty.

Abstract

The abstract provides a detailed description of the study.

Introduction

The Introduction details declining CVD mortality rates during the late twentieth century and their subsequent plateauing. The respective causes of declines in CVD and CHD mortality in the U.K. have been discussed in prior literature (e.g., Bajekal et al., PLoS Med, 2012 and O’Flaherty, Buchan, and Capewell, Heart, 2012). Some discussion of the relative impact of different risk factor exposures and novel treatments would be informative.

The Introduction clearly sets out the important relationship between CVD and dementia, explains the need for a study to estimate future health and cost consequences associated with the ‘cardiovascular disease slowdown’, and summarises how this will be achieved.

Methods

The methods are well-described and are appropriate for the research question, employing a previously validated Markov model. Previous publications which employed the model should be cited when the IMPACT BAM model is introduced in the Methods section. Further information on the basis for the U.K. treasury’s decision to value QALYs at £60,000 may provide context to this model parameter (i.e., determined based on revealed preference studies which aim to quantify the statistical value of a life). A short justification for the study time horizon would also be helpful.

Generally, the Methods section would be improved with a subsection which clearly describes the simulation approach. The manuscript refers to ‘probabilistic sensitivity analysis’ once in the Methods and once in the Strengths and Limitations sections. It would be useful to have a short section of the Methods which describes how cohorts transition through the model (including a model figure, as recommended by Consolidated Health Economic Evaluation Reporting Standards guideline), how many cycles are included in each run, the process by which parameters are stochastically sampled, the approach to defining probability distributions for each of the model parameters, and the way summary statistics from probabilistic runs are reported. I would strongly advise inclusion of a ‘Table 1’ which describes all the model inputs, and their mean values, ranges, distributions for probabilistic analysis, and sources. The suggested figure and table could be included in the supplement material.

The probabilistic simulation approach helps to quantify the uncertainty inherent in this modelling study. The contribution of individual model parameters to this uncertainty is unclear. I recommend conducting ‘traditional’ sensitivity analysis, whereby most parameters are held constant while some parameters are systematically varied (either deterministically or probabilistically). The impact of these results on important outcomes (e.g., incremental health, social and care and total costs) could be presented in tornado diagrams.

Results

The results section is well-written, containing all relevant results. The number of QALYs and life years accumulated in both scenarios are important intermediate outcomes that could also be reported.

Discussion

The discussion provides a useful summary of the results, sets the manuscript in the context of similar literature, and outlines strengths and limitations. The section ‘Implications for Policy Makers’ may be improved by citing examples of cost-effective cardiovascular prevention policies which could be implemented by policy-makers to arrest the plateauing of CVD rates.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Ciaran Kohli-Lynch

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

11 Mar 2022

Response to reviewers

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

To better appreciate your interesting analysis, could you please particularly pay attention to the following excellent points raised by the two reviewers:

1) Please include few paragraphs in the methods section that summarize with sufficient amount of detail how probabilities were defined for transitioning across health states. It is well understood the model simulates an open cohort aged 35-100 over 10 year time horizon. However it is unclear to the reader how new cohorts enter the model (those that turn 35, and would this be on an annual basis), what the initial distributions were for each health state, and how transitions were modelled from disease-free states to disease/death states and from disease to death states (e.g., were transitions based on age and other demographic variables)?

Thank you. We have added some paragraphs to describe this (p.4) and have added a more description in the Appendix detailing how transition probabilities were calculated.

We have also added a new Table 1 with model inputs, as requested below.

We have also attached a file with full model inputs. Model inputs vary by single year of age and calendar year so would be very bulky to include in the main body of the paper.

We have added more clarity around the new cohort of 35 year olds: A new cohort of those reaching age 35 each year enters through the disease free state as displayed in the arrow in the Model figures. Prevalence of cardiovascular disease and functional impairment is very low in this cohort (<2% in total). Therefore, the resulting error in misclassification is negligible.

2) Please include a Table 1 summarizing the model parameters and distributions used for transition probabilities, costs and utility weights together with data sources. When a regression equation was used, could you please state this in the table and refer to its source?

Thank you. We have added a file with all of the model inputs. We have added a new Table 1. Most of the regression equations are quite complex (e.g. lots of age groups); however, all are now included in the Appendix.

3) Please justify the beta distribution type and OLS for modeling costs. Describe also here how uncertainty was modelled within PSA. Which parameters contributed to the uncertainty and which were assumed to be fixed? How was correlation between parameters incorporated?

Thank you. The costs were modelled with a two stage least squares regression for the relationship between cost, health state, gender and age. The overall costs were fitted to a beta around +/- 20% to account for other uncertainties that were not accounted for. We used beta based on this paper https://www.york.ac.uk/media/economics/documents/herc/wp/11_31.pdf - and to account for healthcare costs typically being right-skewed in shape.

4) Please include more details about the 2 modelled scenarios as indicated by Reviewer 1 and what the implications of assumptions are in the model. Also please clarify whether and which costs depend on gains in life expectancy.

Thank you. We have added more detail of the 2 modelled scenarios and justification for these scenarios.

5) Better justify use/non-use of discounting, time horizon and monetary WTP value for a QALY.

Have added discounted results (using 3.5% for costs and 1.5% QALYs in line with UK Treasury) and £30k valuation of QALYs in Appendix 3.

6) Please add one-way sensitivity analyses of key parameters as suggested by Reviewer 2.

We agree that one-way sensitivity analyses, often shown in the form of tornado charts are really useful in showing the key sources of uncertainty around incremental net monetary benefit between two or more interventions. However, this paper is not a traditional cost effectiveness analysis comparing an intervention with a comparator. It is comparing two scenarios where the sources of uncertainty are generally the same for both scenarios so would cancel each other out somewhat in a tornado diagram. We are planning to carry out more work in the future looking at the drivers in healthcare costs in different model scenarios using blinder-oaxaca decomposition but we feel it is beyond the scope of the present paper which is quite lengthy already.

In addition, several more textual suggestions were made by both reviewers.

Please submit your revised manuscript by Dec 20 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Bart Ferket

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please amend the manuscript submission data (via Edit Submission) to include authors: Piotr Bandosz, Maria Guzman-Castillo, Jonathan Pearson-Stuttard, George Stoye, Jeremy McCauley, Sara Ahmadi-Abhari, Marzieh Araghi, Martin J Shipley, Simon Capewell, Eric French, Eric J Brunne, Martin O’Flaherty

3. Thank you for stating the following in the Competing Interests/Financial Disclosure section:

“All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: 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. Dr Collins is secondment as Head of Health Economics in Welsh Government, this paper does not represent any views of Welsh Government. Dr Pearson-Stuttard is also Head of Health Analytics at Lane Clark & Peacock LLP, vice-chair of the Royal Society for Public Health and reports personal fees from Novo Nordisk A/S, all outside of the submitted work.”

We note that one or more of the authors are employed by a commercial company:Health Analytics at Lane Clark & Peacock LLP

a. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

Thanks have added this about the funder BHF.

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Thank you. We have now added this.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

We have included a file with a detailed set of model inputs. We plan to publish the complete model at some point in the future, but need to work through it with funders and authors.

Reviewer #1: No

Reviewer #2: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study is a model-based analysis of the economic implications of the recent trend in CVD incidence in England and Wales compared to a counterfactual of persisting long-term trend. Using a probabilistic Markov model (IMPACT-BAM), the study simulated and compared two scenarios: 1) CVD Plateau, where CVD incidence remains at 2011 levels, and 2) CVD Fall, where CVD incidence follows the long-term trends and continues to decline. It concludes that the slowdown in the decline of CVD incidence is associated with substantial costs.

The topic is potentially interesting, and the findings could help guide priority setting in the local setting. Unfortunately, key methodological details were not sufficiently described in the text to determine the validity of the study. I have included some major issues mostly around the methods and other comments and questions in the following section.

Major issues:

1. The methods section lacks essential details. I understand that the study used a previously published simulation model, but it was unclear how the two scenarios were implemented exactly in this particular study. For example, how was the trend of CVD incidence in the two scenarios fitted statistically? What is the risk profile of those who develop CVD in scenario 1 compared to scenario 2? Considering that the comparison between these two scenarios is the central question of this study, it was very difficult to determine its methodological validity.

Thank you, we have added more detail for the methods and for the two scenarios to the paper and the appendix.

2. It was also unclear how CVD and dementia were correlated in this analysis, i.e., how a change in CVD incidence trend between two scenarios affects the health and cost outcomes of dementia, and how this effect was operationalized in the simulation model.

Thank you. The model is built on ELSA data where having CVD increases the probability of transition to dementia vs. a no dementia state. We have included more detail of this in the paper and appendix.

3. A decrease in CVD incidence is associated with longer life expectancy, but this prolonged life expectancy also comes with some future unrelated healthcare costs: if we prevent individuals from developing CVD, they may still seek care and incur health spending because of other conditions. Not accounting for such costs may bias the results in favor of the CVD Fall scenario. I could not find whether (and if yes, how) this study includes such costs from the description of the methods.

Thank you. The study includes future unrelated healthcare costs. We have made this more clear in the description. This is one of the key questions we aim to answer: does a CVD reduction save money and QALYs even if it means people live longer. The answer suggested by our study is yes.

4. Since this study projects into the next 10 years to calculate the economic impact of CVD trends, one would think that discounting is crucial to account for time preferences. However, it was not clear why discounting was not performed, and I think it would help to see both discounted and undiscounted results.

Thank you. We have added a discounted sensitivity analysis – at 3.5% for costs, 1.5% for QALYs, , in line with NICE and UK Treasury. Discounting was not initially carried out because we are not looking at a specific intervention and wanted to give an idea of how health system costs will evolve year on year, which discounting may partially obscure.

5. The manuscript requires significant copyediting. References are sometimes misaligned (e.g., Introduction -> paragraph 3 -> line 3, the ELSA study is referenced to #8, but should be #11 instead). Many sections/paragraphs lack structure and focus -- the “Implications for policy makers” section is a prime example consisting of scattered paragraphs of just 1-2 sentences; they should be combined to indicate a clear flow of logic. Another example: the “Social care and informal care costs” section includes some methodological descriptions of the QALYs, which should belong to the next section. Some abbreviations are used before they are defined (e.g., “HES”). Additionally, there are grammar errors (incorrect punctuations and capitalizations, etc.) throughout the manuscript.

Thank you, we have corrected these examples and have conducted a further round of copy editing.

Other comments:

1. The study uses £60,000/QALY as the threshold to value QALYs and references the UK Treasury Green Book. However, the most often used threshold we see from the UK is £30,000/QALY recommended by NICE, substantially different from £60,000/QALY. So, what is the rationale for using £60,000/QALY? I also wonder how the results would change if alternative thresholds were used.

Thank you. We have added a sensitivity analysis using £30k per QALY. The NICE threshold is used for recommending technologies for the NHS which has a (typically) fixed budget. The UK Treasury valuation is used for considering policy interventions which may be more appropriate for the present study.

2. Why were costs fitted to beta distributions? Beta distributions are more commonly used for probabilities and utility weights in probabilistic sensitivity analysis, and Gamma or Log-normal are more appropriate for costs.

Thank you. We used beta based on this paper which suggests GB2 can be the best distribution for healthcare costs which are often right-skewed https://www.york.ac.uk/media/economics/documents/herc/wp/11_31.pdf

3. I’m having a hard time understanding what this sentence means: “The QALYs reflect only the uncertainty in the epidemiology, not uncertainty around the QALY impact of disease, which is reflected in very tight confidence intervals.” Is this essentially saying that there’s no probabilistic distribution added to the QALY weights because the uncertainty on QALY weights is very low? In any case, this sentence should be reworded to improve clarity.

Thank you, yes. We have reworded it as you suggest to make it clearer.

4. Tables 1 and 2: These tables present results for a “base case scenario.” It’s confusing because a base case scenario is not described and defined in the methods section.

Thank you for picking this up. We have changed this – it is actually Scenario 2, although the results for this are very similar for both scenarios.

5. Scenario 2 is a hypothetical scenario where CVD incidence declines at a rate close to historical levels. Even though the discussion section touches briefly on the aspects CVD prevention could focus on, scenario 2 seems like a very hypothetical and unrealistic scenario without some justification on *how* this decline could be achieved. More and deeper discussion on this could be helpful, and maybe a somewhat more realistic scenario could be of more policy interest.

Thank you. We give some examples of countries that have continued to show reductions in CVD incidence and that have much lower incidence than the UK so we do not believe the scenario we are presenting is unrealistic, however it may take a longer time to achieve, especially given the pandemic.

6. The “Future research” section claims that “Future research could use our IMPACT-BAM model to look further at health inequalities, and produce more granular estimates at regional or at local authority levels, especially given the increasingly local input to resource allocation.” It was unclear, though, whether the authors have made the IMPACT-BAM model publicly available for such uses. I could not find a user interface for the model from an online search.

Thank you. We aim to make the IMPACT-BAM model available in due course. Furthermore, we have a big group working on the model, so some future research may be carried out by people with previous experience of working with the model.

Reviewer #2: This is a well-conducted and important study that quantifies the costs and QALYs associated with persistent plateauing versus continued decline of cardiovascular disease rates in England and Wales.

Below I provide some comments on each section of the abstract. While most of these are minor recommendations, I strongly recommend that the simulation procedure is more clearly explained in the Methods section and that sensitivity analysis is conducted to better quantify the contribution of individual model parameters to overall uncertainty.

Abstract

The abstract provides a detailed description of the study.

Introduction

The Introduction details declining CVD mortality rates during the late twentieth century and their subsequent plateauing. The respective causes of declines in CVD and CHD mortality in the U.K. have been discussed in prior literature (e.g., Bajekal et al., PLoS Med, 2012 and O’Flaherty, Buchan, and Capewell, Heart, 2012). Some discussion of the relative impact of different risk factor exposures and novel treatments would be informative.

Thank you. We have added some reference to treatments that may reduce the CVD case fatality ratio; we are mainly concerned with CVD incidence in this study related to policy interventions, rather than tertiary prevention but agree tertiary prevention is important.

The Introduction clearly sets out the important relationship between CVD and dementia, explains the need for a study to estimate future health and cost consequences associated with the ‘cardiovascular disease slowdown’, and summarises how this will be achieved.

Methods

The methods are well-described and are appropriate for the research question, employing a previously validated Markov model. Previous publications which employed the model should be cited when the IMPACT BAM model is introduced in the Methods section. Further information on the basis for the U.K. treasury’s decision to value QALYs at £60,000 may provide context to this model parameter (i.e., determined based on revealed preference studies which aim to quantify the statistical value of a life). A short justification for the study time horizon would also be helpful.

Thank you. We have added reference to the study time horizon mirroring the NHS plan. We have also added sensitivity analysis with £30k / QALY valuation.

Generally, the Methods section would be improved with a subsection which clearly describes the simulation approach. The manuscript refers to ‘probabilistic sensitivity analysis’ once in the Methods and once in the Strengths and Limitations sections. It would be useful to have a short section of the Methods which describes how cohorts transition through the model (including a model figure, as recommended by Consolidated Health Economic Evaluation Reporting Standards guideline), how many cycles are included in each run, the process by which parameters are stochastically sampled, the approach to defining probability distributions for each of the model parameters, and the way summary statistics from probabilistic runs are reported. I would strongly advise inclusion of a ‘Table 1’ which describes all the model inputs, and their mean values, ranges, distributions for probabilistic analysis, and sources. The suggested figure and table could be included in the supplement material.

Thank you. We have added a Table 1, and model figure into the Appendix.

The probabilistic simulation approach helps to quantify the uncertainty inherent in this modelling study. The contribution of individual model parameters to this uncertainty is unclear. I recommend conducting ‘traditional’ sensitivity analysis, whereby most parameters are held constant while some parameters are systematically varied (either deterministically or probabilistically). The impact of these results on important outcomes (e.g., incremental health, social and care and total costs) could be presented in tornado diagrams.

Thank you. Please see comment above. We aim to carry out future work looking in more detail at the drivers of change and uncertainty in the model.

Results

The results section is well-written, containing all relevant results. The number of QALYs and life years accumulated in both scenarios are important intermediate outcomes that could also be reported.

Thank you. We have made more reference to the incremental QALY results.

Discussion

The discussion provides a useful summary of the results, sets the manuscript in the context of similar literature, and outlines strengths and limitations. The section ‘Implications for Policy Makers’ may be improved by citing examples of cost-effective cardiovascular prevention policies which could be implemented by policy-makers to arrest the plateauing of CVD rates.

Thank you. We have added more reference to cost effective prevention policies in this section.

Huge thanks to both reviewers and to the editors for providing such perspicacious comments. We hope that we have acted upon them to your satisfaction, and that our paper is now fit for publication in PLOS ONE.

Submitted filename: IMPACT BAM PLOS One response to reviewers sc (1).docx

Click here for additional data file.

27 Apr 2022

27 Apr 2022

Response to Reviewers

Dear PLOS One

Thank you for reviewing our study again. Please find rebuttal below;

Please perform the additional one-way sensitivity analyses as suggested or provide a better justification for not performing these in the discussion (strengths and weaknesses) section in response to the remaining concerns of the reviewer. Note that the sentence "Hence the value of our extensive sensitivity analyses." may not be supported well by your analyses in the current version based on the comments of the reviewer.

Thank you for this comment. We have taken out this sentence and replaced it with sentences below. We have included some one-way sensitivity analyses as suggested by reviewers. We have included probabilistic sensitivity analysis which is generally seen as the gold standard in terms of sensitivity analysis but the way the model is structured makes it more difficult to perform a series of one-way sensitivity analyses, but we agree that this would be useful and it is something we intend to do in future studies with this model, to explore the drivers of change over time. So we agree with the reviewer that this will be useful, but we want to explore it more in a potential future study.

“We have included some one-way sensitivity analyses (value of QALY, discount rate), as well as the probabilistic sensitivity analysis; but more one-way sensitivity analyses may highlight the drivers of differences in outcomes in more detail. We plan to produce a future study to explore these drivers more, using decomposition analysis.”

I hope this change in emphasis as well as explaining our future plans answers this to your satisfaction.

With best wishes

Dr Brendan Collins

Submitted filename: PLOS One response to reviewers 20220427.docx

Click here for additional data file.

9 May 2022

What will the cardiovascular disease slowdown cost?  Modelling the impact of CVD trends on dementia, disability, and economic costs in England and Wales from 2020-2029.

PONE-D-21-22992R2

Dear Dr. Collins,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Bart Ferket

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

12 May 2022

PONE-D-21-22992R2

What will the cardiovascular disease slowdown cost?  Modelling the impact of CVD trends on dementia, disability, and economic costs in England and Wales from 2020-2029.

Dear Dr. Collins:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Bart Ferket

Academic Editor

PLOS ONE