Socioeconomic per-case costs of stroke, myocardial infarction, and preterm birth attributable to air pollution in Sweden

Hedi Katre Kriit; Johan Nilsson Sommar; Stefan Åström

doi:10.1371/journal.pone.0290766

Abstract

Background

Incident cases of stroke, myocardial infarction, and preterm birth have established exposure-response functions associated with air pollution. However, there are no studies reporting detailed costs per case for these health outcomes that are adapted to the cost-benefit tools that guide the regulation of air pollution.

Objectives

The primary objective was to establish non-fatal per-case monetary estimates for stroke, myocardial infarction, and preterm birth attributable to air pollution in Sweden, and the secondary objective was to ease the economic evaluation process of air pollution morbidity effects and their inclusion in cost-benefit assessments.

Methods

Based on recommendations from the literature, the case-cost analysis considered direct and indirect medical costs, as well as production losses and informal costs relevant for the calculation of the net present value. A literature search was conducted to estimate the costs of each category for each incident case in Sweden. Informal costs were estimated using the quality-adjusted life-years approach and the corresponding willingness-to-pay in the Swedish population. The total average per-case cost was estimated based on specific health outcome durations and severity and was discounted by 3.5% per year. Sensitivity analysis included varying discount rates, severity of health outcome, and the range of societal willingness to pay for quality-adjusted life years.

Results

The average net present value cost estimate was €₂₀₁₆ 460k (185k–1M) for non-fatal stroke, €₂₀₁₆ 24k (16k–38k) for myocardial infarction, and €₂₀₁₆ 34k (19k–57k) for late preterm birth. The main drivers of the per-case total cost estimates were health outcome severity and societal willingness to pay for risk reduction. Varying the discount rate had the largest effect on preterm birth, with costs changing by ±30% for the discount rates analysed.

Recommendation

Because stroke, myocardial infarction, and preterm birth have established exposure-response functions linking these to air pollution, cost-benefit analyses should include the costs for these health outcomes in order to adequately guide future air pollution and climate change policies.

Citation: Kriit HK, Sommar JN, Åström S (2024) Socioeconomic per-case costs of stroke, myocardial infarction, and preterm birth attributable to air pollution in Sweden. PLoS ONE 19(1): e0290766. https://doi.org/10.1371/journal.pone.0290766

Editor: Ryan G. Wagner, University of the Witwatersrand Johannesburg, SOUTH AFRICA

Received: January 27, 2022; Accepted: August 15, 2023; Published: January 11, 2024

Copyright: © 2024 Kriit et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: This research was funded by the Swedish Environmental Protection Agency through the SCAC research programme (grant number NV-03730-1616/87) and by NordForsk under the Nordic Programme on Health and Welfare (Project 75007). This research was funded by the Swedish Environmental Protection Agency through the SCAC research programme (grant number NV-03730-1616/87) and by NordForsk under the Nordic Programme on Health and Welfare (Project 75007). The funders had no role in study design, data collection and analysis, decision to publich, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Air pollution is the number one environmental risk factor for human health according to the World Health Organization (WHO) [1, 2], and an increasing number of epidemiological studies have linked several chronic health outcomes with high air pollution concentrations [3]. Health outcomes such as cardiovascular diseases and preterm birth (PTB) have established exposure-response functions (ERFs) with regards to exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) [4, 5], including stroke and myocardial infarction (MI) with estimated risk increases of 19% and 13%, respectively, for each 10 μg/m³ increase in long-term exposure to PM_2.5 [5]. For PTB, a meta-analysis by Klepac et al. (2018) reported a 24% increase in risk for each 10 μg/m³ increase in PM_2.5 [6].

Despite the high prevalence and increasing trend of cardiovascular disease burden and PTB [7, 8], currently available health economic assessments of air pollution include only a narrow range of related health outcomes [9]. Important international air pollution policy support tools providing information on the socio-economic benefits of reducing air pollution, such as BenMap used by the Environmental Protection Agency [10] in the US and Alpha Riskpoll (ARP) [11] used by the European Commission, need to be continuously updated as new epidemiological knowledge is developed. Such updates are needed because cost-benefit analysis (CBA) is becoming more important for air pollution policy. As an example, in 2016 the EU air pollution policy goals for 2030 were decided with a large influence from the European Commissions impact assessment (SWD (2013) 531), which was made with a CBA that had not been updated with the latest epidemiological knowledge. Together, the use of outdated data and the omission of new knowledge risk leading to suboptimal decisions regarding policies that are meant to guide development towards sustainability in the coming decades.

Both ARP and BenMap include ERFs for morbidity effects and mortality, where mortality currently constitutes 90% of the monetized benefits of reduced air pollution [10]. However, the short-term morbidity effects included in the CBAs rely on outdated ERFs from the 1980s or 1990s, such as restricted activity days and respiratory hospital admissions [12], and are currently omitting new knowledge from systematic reviews and medical expert guidelines on long-term chronic health outcomes such as cardiovascular disease, chronic obstructive pulmonary disease, and PTBs that are attributable to PM_2.5 [3, 13], all which have profound long-term economic impacts both for the individual and for society. For example, the burden of disease for stroke and MI is estimated to correspond on average to 8% of all European health care expenditures [7]. Thus, for air pollution policy processes it is imperative to first value the socioeconomic cost per case in monetary terms and then to upgrade the economic assessments of air pollution policies.

The available literature presenting long term economic costs of these health outcomes is scarce and varies in terms of costs included and if they are disaggregated based on the severity of the condition. Below we present an overview of the economic literature for costs of stroke, MI, and PTB using monetary units and converted into €₂₀₁₆ using purchase power parity exchange rates and consumer price indices presented by the OECD to ease the comparison.

The costs for the health care sector of cardiovascular disease in Europe are estimated to be €₂₀₁₆ 111 billion annually, of which €₂₀₁₆ 45 billion is attributable to stroke burden [7]. For Sweden, the average lifetime cost per stroke case has been estimated to be €₂₀₁₆ 94k, while the distribution of costs can vary by up to six-fold due to patient-specific characteristics and the severity of stroke-induced disability [14]. A systematic review of MI costs reported mean direct costs to be €₂₀₁₆ 9k per incidence [15]; however, individual studies in Sweden and in Canada report higher mean direct costs for MI of €₂₀₁₆ 12–16k [16–18]. The productivity losses out of the total cost of stroke and MI have been estimated to be approximately 9% and 25%, respectively [14, 16]. In contrast, the cost of PTB has been estimated to be €₂₀₁₆ 290k per case, where productivity losses contribute about 85% [19]. However, a systematic review of PTB costs concluded that the existing literature omits several economic costs and that none of the health outcomes of interest in cost-of-illness studies have included the informal costs reflecting decreased quality of life [20]. Further, none of the published stroke and MI cost estimates have clearly separate costs for non-fatal cases and fatal cases, and the published PTB cost estimates do not separate costs according to gestational age. The separation of costs between fatal and non-fatal is of importance when assessing the air pollution health economic burden in order to avoid double counting because mortality-related health economic burden currently dominates.

The main aim of this study was to present the monetary per-case cost estimates of non-fatal stroke, MI, and PTB attributable to air pollution in Sweden. The secondary objective was to ease the economic evaluation process of air pollution morbidity effects and their inclusion in the cost-benefit assessments. This study was limited stroke, MI, and PTB because these are health effects for which there is a consensus in the epidemiological evidence.

Materials and methods

Literature search

To obtain the economic costs of stroke and MI attributable to air pollution, a literature search was conducted using the PubMed and Google Scholar search engines. The search word combinations were based on a previously published scientific paper reporting health care costs attributable to air pollution in the UK [21]. The search combination was extended to also include societal costs. The inclusion criteria were a) published in 2005–2020, b) using data for Sweden, c) outcomes classified according to ICD-code, d) costs representing the health outcome in question; and e) reporting both direct and indirect costs of the health outcome of interest. If more than one paper was found to fulfil all criteria for the same outcome, the most recent paper was chosen. For specific search combinations and the numbers of studies found and excluded, please see S1 File.

Calculation of the Net Present Value (NPV) of specific outcomes

The monetization of impacts into economic costs was based on the method suggested by Hodek et al. [22], which is also the method recommended by Hunt et al. (2016) [9]. The economic cost categories considered both formal costs (direct medical costs (DMCs), direct non-medical costs (DNMCs), and indirect costs (ICs)) and informal costs (InCs) (Table 1) [22]. The data availability and aggregation level in the source literature determined which categories and subcategories were ultimately used in the NPV calculations.

Download:

Table 1. Cost categories for valuing the health outcomes and the available costs used for the monetization of stroke, myocardial infarction, and preterm birth.

https://doi.org/10.1371/journal.pone.0290766.t001

Lifetime costs of the chronic health outcomes were calculated as the sum of the NPVs of the economic value of all cost categories and/or sub-categories (Eq 1). Therefore, the time of occurrence and duration of each sub-category also needed to be specified. (1)

Where:

NPV = Net Present Value

t_inc = life-year when incidence occurs

T = years of life expectancy

r = annual discount rate

c_cat,t = cost category or sub-category

For the calculation of the NPV we used an r-value of 3.5% (low 2%; high 5%) [24] and a T of 82 years (corresponding to the average life expectancy in Sweden [25]).

All DMCs, DNMCs, and ICs stated in the literature were adjusted for inflation between publication year and 2016 and then converted from the stated currency to €₂₀₁₆ using Purchase Power Parity-corrected exchange rates.

In impact assessments of air pollution policies, air pollution-induced mortality is often represented as ‘all-cause’ mortality. This caused a challenge for this study because stroke and MI can be fatal. To avoid double-counting in impact assessments and CBAs on air pollution policies, it is therefore important to separate the costs of fatal stroke and MI incidences from the costs of non-fatal incidences. To ensure this, we only made cost calculations for incidences where the patient survived >30 days after the stroke or MI incidence.

We used the degree of disability severity ranges reported from the source literature as input to calculate the uncertainty intervals of the cost estimates. For stroke, the disease severity classification based on the Modified Ranking Scale (mRS) was used to calculate low, middle, and high cost estimates. For MI, when no information on severity range was given we used a ±20% uncertainty range based on recommendations by Briggs et al. 2012 [26]. For the DMCs for PTB, we also used a ±20% uncertainty range. For PTB income losses we used varying gestational ages as inputs to compare low and middle cost estimates. For comparing middle and high cost estimates of PTB we used alternative sources for costs of work absenteeism expressed as productivity loss. The uncertainty ranges are presented within brackets in the subsequent parts of the method chapter.

Formal costs

Stroke.

Stroke survivors suffer varying degrees of permanent functional disability due to neurological damage. Thus, it is appropriate to assume that costs related to functional disability as a direct cause of stroke continue during the remaining lifetime after the incident. For two years, stroke patients identified with ischemic stroke (ICD-10 I63) and intracerebral haemorrhagic stroke (ICD-10 I61) were followed regarding their health care sector resource use by Lekander et al. [23]. For the DMC category, the inpatient costs in Lekander et al. [23] were highest during the first year of disease occurrence and showed a steep decrease during the second year among all groups stratified according to the degree of disability based on mRS. The DNMCs and ICs related to stroke were relatively small during the first year and rose considerably during the second year for all disability strata.

The results in Lekander et al. [23] were presented separately for ischemic and haemorrhagic stroke and stratified based on the degree of disability using the mRS, and the mRS categorization was used as the basis to categorize the uncertainty intervals. In the present study we weighted the costs for ischemic and haemorrhagic stroke in accordance with their relative share of cases in Lekander et al. [23] and used the average costs for ‘all survivors’ in the mRS strata to reflect the mid-range cost estimates, and low cost estimates corresponded to patients ranked with low disability (mRS 0–2) and high costs corresponded to patients with high disability (mRS 5) [23]. To calculate the stroke-related lifetime costs, we calculated the expected remaining life years for ischemic and intracerebral haemorrhagic stroke survivors in relation to average life expectancy T in Sweden and the t_inc values reported by Lekander et al. [23]. Because we obtained data on stroke-related costs for the first and second year after the incidence, we assumed that costs in all cost categories remained constant at the year-two value for the remaining life years.

Myocardial infarction.

The DMCs and ICs related to MI (defined as “acute myocardial infarction” ICD-9-CM codes 410, 411) were derived from a paper by Mourad et al. [16]. Patients suffering from MI usually have several other diagnoses in the ischemic heart diseases group that can act as risk factors prior to the MI incidence (e.g. hypertension and hyperlipidaemia). However, Mourad et al. [16] did not disaggregate DMCs and ICs for MI, hypertension, or hyperlipidaemia, and thus it is likely that their MI costs overestimated the MI costs. To minimize such a possible overestimation, in this study we only assessed costs during the first year after an MI incident. The DNMCs were not accounted for due to a lack of existing Swedish data. Because the duration of MI was assumed to be one year, no discounting was applied.

Preterm birth.

Because air pollution is associated with PTB occurring mainly after week 32, we calculated the costs of PTB for births occurring at weeks 33–36. Accordingly, the PTB calculations required literature sources that stratified impacts per gestational week.

The DMCs for PTB were calculated as the differences in costs between those born full term and those born preterm, according to the costs in the Swedish Cost Per Patient Database (ICD-10 codes O60.1A–060.1X, 060.3A–060.3X & P07.0–P07.3X) [2,7]. The Swedish Cost Per Patient Database aggregates labour costs associated with PTB over all gestational weeks, while US inpatient first-year cost data [2,8] show large differences in costs between those born before week 28 and those born after week 32, suggesting a large variation in average cost estimates in the Swedish data. Therefore, to estimate the costs for late PTBs, the US cost data and the Swedish gestational data were disaggregated in steps with the following assumptions. Swedish statistics show that around 1% of the Swedish births take place in week 22–32 and 3.8% in week 33–36 [29]. For the 1% we assumed that 20% occur before week 28 and 80% occur in week 28–32. We also assumed that the costs for births in week 32–36 are equal to those in week 33–36. With these assumptions we could calculate weighted costs for those born in week 33–36 by multiplying the relative distribution of costs per group from the US cost data [2,8] with the relative distribution of births from Swedish gestational data [29], the product of which we called the cost factor. The cost factor was calculated as follows. (2)

Where

C-factor_bw.33-36 = the cost factor for those born in week 33–36,

br = the ratio of all births in Sweden per gestational age group,

bw = the PTB gestational age groups included in the birth data (<week.28, w.28–32, w.33–36),

USc = the surplus cost for PTB per gestational age group according to US statistics [28],

cw = PTB gestational age groups in cost data (<w.28, w.28–31, w.32–36).

The full-term age group was excluded in this C-factor calculation.

As percent of all Swedish births, the preterm births, equals 3.8%, 0.8%, and 0.2% for gestational week 33–36, 28–32, and <w.28, respectively. Correspondingly, of the preterm births 79%, 17%, and 4% for gestational week 33–-36, 28–-32, and <w.28, respectively. The corresponding US costs were available for the preterm births occurring on weeks 32–36, 28–31, and <28, corresponding to for US$ 8 960, 83 300, and 179 000, respectively. An assumption that the costs for preterm birth would be equal for preterm births for weeks 33–36 (for which the birth data from Sweden was available) and for 32–36 (for which the cost data was available from US). The sum product of [79%, 17%, 4%] * [8,960, 83,300, 179,000] equals 28 400. The cost factor value for those born week 33–36 is then 8 960/28 400 = 0.32.

Finally, the week 33–36 cost factor was multiplied by the cost difference for PTB derived from the Swedish Cost Per Patient Database [27] to obtain labour-related DMCs for PTB.

For IC estimates we included the costs related to assistance due to disability, lower education, lower income, and lower employment levels and increased numbers of days spent on sick-leave and early retirement [30–32]. Low cost estimates were based on gestational weeks 37–38 instead of weeks 33–36, and high cost estimates for costs related to average personal productivity losses for persons born preterm were from Vredin and Johansson [33]. See the S2 File for detailed cost categories, unit valuations, and durations.

Informal costs

The InCs were estimated by applying a quality-adjusted life year (QALY) approach. For stroke and MI, the difference between the Swedish general populations’ average life quality and the outcome-specific QALY values was used to calculate the average loss of QALYs among stroke and MI survivors for their remaining life years (Table 2) [34–36]. Further, it is known that stroke survivors’ spouses report lower QALY values, and these were thus included in the calculations of InC for stroke [37]. To estimate QALY loss for PTB, we used descriptive statistics on the differences in prevalence of mental retardation, cerebral palsy, hearing loss, and vision impairment between those born at weeks 32–36 and those born full term [28], and we combined these with estimated QALY losses for the corresponding outcomes reported by Graig et al. [38].

Download:

Table 2. QALY weights for the general population and for incident cases of stroke, MI, and PTB and for spouses of stroke survivors.

https://doi.org/10.1371/journal.pone.0290766.t002

The economic valuation of QALYs for the low and middle estimate was based on the threshold values applied by the Swedish Dental and Pharmaceutical Benefits Agency to evaluate the cost-effectiveness of drugs. The rule-of-thumb threshold is €₂₀₁₆ 53k (referred to in this study as the low estimate for one QALY). For the middle estimate we used €₂₀₁₆ 106k, which is one of the highest thresholds accepted for reimbursing a drug [39, 40]. For the high estimate we used a societal willingness to pay of €₂₀₁₆ 250k [41], which is based on a willingness-to-pay study and applied by the Swedish Transport Administration (STA) [42]. Later, Olofsson et al. published a scientific article based on the willingness-to-pay study conducted on commission from the STA, where the specific methods of valuation of a QALY with chained approaches is described in more detail [43].

Results

Stroke

The formal life-time costs of stroke varied from 66k up to 500k for the low and high values categorized based on remaining disability level after the incident (Table 3, S3 File). The middle values for DMC and DNMC were estimated to be 36k and 158k, respectively. ICs were highest for the low-cost estimates and lowest for those with high disability ranking among the stroke survivals. The value of loss of QALYs was estimated to correspond 119k up to 570k using low and high QALY valuations, respectively. The average NPVs of stroke were estimated to be 460k, where formal costs constituted approximately 50% of the total.

Download:

Table 3. Formal and informal costs for stroke, MI, and PTB stratified to low, middle, and high estimates discounted by 3.5% and the NPV as the sum of formal and informal costs.

https://doi.org/10.1371/journal.pone.0290766.t003

MI

The formal costs for incident MI were estimated to vary between 12k and 18k, with a middle estimate of 15k (Table 3, S3 File). DNM costs corresponded to 84% of the formal costs, and 16% were estimated as ICs. The value of loss of QALYs was estimated to correspond to 8k when using the middle estimates. The average NPV of MI was estimated to correspond 24k, out of which formal costs contributed 65%.

PTB

For PTB the average DMC corresponding to the medical resource use of the first life-year were estimated to be on average 7k (Table 3, S3 File). However, the average lifetime IC for PTB was estimated to be twice as large as the DMC. The value of loss of QALY using the central estimate was 12k. The average NPV of PTB was estimated to be 34k, out of which the formal costs made up 56%.

Sensitivity analysis

When implementing the low monetary estimate of a QALY, the NPV per case was reduced by 20–30% for each health outcome (Table 3). In contrast, the high estimate of a QALY increased the total costs by 50–70%.

In the sensitivity analysis, we also varied the discount rate between 2% and 5% when estimating the NPVs for stroke and PTB (Table 4, S3 Table). For stroke, depending on the discount rate during the average disease duration, the average per-case total costs varied by ±6%, mainly due to effects of InCs on the NPV. The average per-case total costs for PTB were estimated to be 25% lower than in the middle analysis when assuming a 5% discount rate and to be 50% higher when assuming a 2% discount rate.

Download:

Table 4. Sensitivity analysis of varying discounts and QALY values for total cost estimates for stroke and PTB.

https://doi.org/10.1371/journal.pone.0290766.t004

Discussion

This study reports per-case costs of non-fatal stroke, MI, and PTB by including the formal costs and InCs in Sweden. Our results suggest that the socioeconomic costs of stroke, MI, and PTB are substantial and that InCs constitute on average 50% of the total costs. Even after discounting by varying rates, the total cost of stroke, MI, and PTB remained substantial. The results from this study exclude costs associated with fatal stroke and MI, as well as costs associated with very early PTB, and are thereby designed to facilitate application in already existing CBA tools assessing the monetary effects of air pollution interventions. Together with the ERF values presented in the introduction, these values provide a possibility to increase the number of air pollution impacts considered in CBAs.

Mortality is currently dominating the health economic burden in existing CBA tools. For example, in the ARP model that was used to support the latest National Emission Reduction Commitments (NEC) Directive of the European Union, the health economic burden of mortality corresponded to 66% of the total economic burden. The ARP model version that we compared our results to contains values for preterm fatality (estimated through Value of Statistical Life (VSL) or Value of a life year (VOLY)), preterm fatality of children, chronic bronchitis for adults and bronchitis for children, respiratory and cardiac hospital admissions, restricted activity days, asthma symptom days, and lost working days. Including these morbidity outcomes would alter the estimated proportion of the monetary burden attributable to mortality from the NEC value of 66% of total costs down to 40–60% given the uncertainty ranges.

When including the ERFs and the economic values of stroke, MI, and PTB in this version of the model we can estimate the economic benefits of a 1μg/m³ reduction in the annual average concentration of PM_2.5 in Sweden. For a mortality VOLY valuation at €₂₀₁₆ 45k per life year, the comparison showed that the benefits of cleaner air would increase by some 12–74% if stroke, MI, and PTB are valued as in our low or high estimates respectively (or €₂₀₁₆ 440M–680M instead of €₂₀₁₆ 390M). For the same mortality value, the share of mortality of the total health effect value would be reduced from 66% to 51%. For a mortality value corresponding to a VSL of €₂₀₁₆ 3 150 000, the mortality share would be reduced from 93% to 88%.

The morbidity costs presented in this study highlight that the total economic burden due to air pollution is underestimated in current CBAs guiding European Union air pollution policy processes.

Our study estimates can also be applied to estimate the marginal benefits of reducing air pollution exposure for individual countries to meet air quality goals. For example, the Swedish Environmental Agency’s latest health impact assessment of air pollutants reports the average population-weighted PM_2.5 exposure in 2019 to be 7.2 μg/m³ [44]. According to the report, 82% of the Swedish population is exposed to higher annual PM_2.5 concentrations [44] than recommended by the World Health Organization’s latest Air Quality Guideline value of 5 μg/m³ [45]. If the annual average PM_2.5 exposure is lowered by 1 μg/m³, then on average 178 incident MI cases and 616 incident stroke cases could be avoided, with a corresponding marginal economic benefit of €₂₀₁₆ 289M using this study’s middle estimates of NPVs.

Formal costs

The results from this study are not directly comparable to earlier literature because we included indirect costs and excluded fatal outcomes for stroke and MI and preterm births earlier than 33 weeks gestational age. Regardless of these qualitative differences, our results are still sufficiently consistent with values from other studies. For example, the average cost reported per stroke patient in the EU in 2015 was €₂₀₁₆ 158k [7]. This is a little bit higher than annual costs reported by Lekander et al. [23], who reported an annual cost per stroke patient of €₂₀₁₆ 120k. Persson et al. [14] estimated the costs per stroke case to be €₂₀₁₆ 94k, which is comparable with this study’s low estimates corresponding to stroke survivals with low disability with a NPV of €₂₀₁₆ 66k–71k. In comparison with MI, the average cost of one ischemic heart disease incidence in 2015 was estimated to be €₂₀₁₆ 20k in Europe [7], which again compares well with this study’s average cost estimate of €₂₀₁₆ 24k. Previously published economic costs of PTB vary great. Trasande et al. [19] and Kim et al. [46] presented economic costs of €₂₀₁₆ 290k and €₂₀₁₆ 62k–188k per incidence of PTB, respectively. The US Committee on Understanding Premature Birth and Assuring Healthy Outcomes reported a unit value for PTB of €₂₀₁₆ 63k in the US, accounting for DMCs, IDMCs, and ICs and applying a 3% discount rate [28], which was twice as high as this study’s estimate. A likely explanation for the higher per-case cost estimate for PTB in previous research is that we accounted for costs for PTB that occurred during gestational weeks 33–36 in contrast to aggregated costs for all PTB cases regardless of gestational age. In the US, for example, PTB that occurs before gestational week 28 incurs 17-times higher costs than births in gestational week 32–36 [28]. Furthermore, previous estimates are based on US data, a country known to often have higher health care costs compared to Europe. As relevant examples, costs for normal delivery births and percutaneous transluminal coronary angioplasty have been reported to be 70% and 55% more expensive in the US than in Sweden [47]. The health care system in Sweden has universal access, which is associated with lower health care costs, whereas the US has a fragmented private insurance-based health care system, which is associated with higher health care costs. The socioeconomic cost for stroke and MI could also be used by other fields of research trying to quantify the economic burden of disease, but caution is needed if using the benefit transfer method to apply the economic cost estimates to other countries. Health care costs can vary due to the structure of the health care system, and lifetime levels of education and income are affected by, among other things, the degree of social mobility in the country of interest.

The use and value of discount rates is a debated praxis in the environmental economic literature, with renewed intensity after the Stern Review in 2006, mostly due to the ethical implications of the chosen values [48–50]. Following economic theory, an individual’s willingness to pay for goods received at the present time is greater than for goods received in the future. However, in environmental and health policy contexts, discounting the value of future benefits is controversial. If decision-makers are contemplating an intervention to reduce air pollution and are accounting for expected future health benefits, applying a discounting rate will reduce the total NPV of the health benefits. For example, applying a 5% discount rate on our results leads to greater reductions in InCs compared to formal costs. A declining discount rate has been suggested as a method for evaluating the economic benefits of health interventions with immediately occurring implementation costs but with benefits that are expected to occur in the far future [51, 52]. While this discussion is of interest, there is to our knowledge a lack of empirical studies that have tested the effect of discounting of health effects on decision-making outcomes.

As a strength of this study, total cost estimates for stroke were based on disaggregated cost data for stroke survivors, which created good premises to estimate lifetime costs taking into consideration the large variation in costs between different mRS strata [23]. This study also included the losses of QALY to be able to account for informal costs, which are often excluded in environmental economics due to theoretical differences in the methods for valuing a QALY (threshold vs. willingness-to-pay). In the Swedish context, the health economics evaluations rely on threshold values to assess cost-effectiveness in terms of QALY’s gained, but this method has little empirical support. However, environmental economic theory prefers a QALY valuation based on willingness-to-pay studies. Nevertheless, this study is the first to quantify the InCs, which were estimated to corresponded to approximately 50% of the total costs, illustrating the possible depth of underestimation of socioeconomic costs due to stroke, MI, and PTB. Secondly, we demonstrated the uncertainty ranges based on the monetary values applied in praxis to value a QALY, therefore suggesting a possible direction for developing more interdisciplinary valuation methods applicable to both health and environmental economic disciplines. Additionally, extension of the theoretical framework is warranted to serve as a foundation for future empirical studies trying to estimate the willingness-to-pay value for a QALY that could be applicable in both fields of research.

A shortcoming of this study is that all costs were not available for all the recommended cost categories. Stroke and MI socioeconomic cost estimates are also subject to inconsistency related to source of data reporting [16, 23]. Most prominently, the costs for control groups are often not reported, which leads to the risk of double-counting some fraction of the economic costs for the outcomes in question. Thus, a potential overestimation of socioeconomic costs associated with morbidity outcomes should be considered. However, if we compare the average DMC of 65-year-olds in the Swedish county of Västra Götaland with the DMC of MI patients, the latter has five times the average DMC compared to health care consumption for an average person [53]. This supports the notion that both stroke and MI incidence result in a substantial increase in DMC and suggests that the overestimation induced by the absence of control groups in the source data is likely to be small.

With respect to PTB, the outpatient DMC and DNMC items were not covered in the Swedish data or literature and were therefore excluded from the main analysis. To check the potential importance of these items, we used cost estimates from the US as a proxy, which resulted in a 9% higher central estimate compared to our main analysis (SM10, SM11). The difference from the central estimate in the main analysis was not very large, which indicates that our central estimate included most of the relevant costs.

The PTB InCs were probably underestimated because the parents of a PTB probably suffer a loss of QALY compared to parents to a normal gestational age (38–42 weeks) similarly to caregivers of stroke patients. However, there is a need to quantify the estimated utility losses among the PTB parents in future studies to also be able to account for these in economic evaluation studies.

Recommendations

Future research should focus on acquiring more relevant data, especially for MI and the quality-of-life effects of PTB. Research should also focus on continuing the economic valuation of the other health outcomes related to air pollution exposure [3]. Continuing the valuation of morbidity outcomes of air pollution exposure is important because it is likely that these outcomes affect the welfare-maximising level of pollution as analysed in the cost-benefit analyses currently used to support air pollution policies in Europe.

Conclusion

This study reports socioeconomic cost estimates for stroke, MI, and PTB to facilitate the upgrade process of CBAs attempting to monetize the adverse health effects of air pollution. The valuation method proposed by Hodek et al. [22] and the data available in Sweden were sufficient to establish reliable cost estimates for this aim. Other CBA research fields such as climate economics can use the monetary values reported here for stroke, MI, and PTB as input values as well. If including formal and informal welfare costs of stroke, MI, and PTB in the group of already established welfare costs of air pollution (such as loss of preterm fatality, chronic bronchitis, etc.), the quantified benefits of cleaner air would increase substantially in the Swedish case. The inclusion of monetized values of stroke, MI, and PTB is important prior to future analyses of the costs and benefits of air pollution emission control.

Supporting information

S1 File. Search terms applied and results of literature search in PubMed to retrieve cost-of-illness studies for stroke and MI.

https://doi.org/10.1371/journal.pone.0290766.s001

(PDF)

S2 File. Data on occurrence, duration, and indirect costs associated with preterm birth.

https://doi.org/10.1371/journal.pone.0290766.s002

(PDF)

S3 File. Disaggregated costs-per case of stroke, MI and PTB and sensitivity analyses.

https://doi.org/10.1371/journal.pone.0290766.s003

(XLSX)

Acknowledgments

The authors would like to thank Bertil Forsberg for guidance and general knowledge sharing and Ståle Navrud for reviewing the draft of the manuscript.

References

1. WHO. Ten threats to global health in 2019. World Health Organization: WHO 2019.
2. Krzyżanowski M, et al. Health effects of transport-related air pollution: WHO Regional Office Europe. 2005.
3. Thurston GD, Kipen H, Annesi-Maesano I, Balmes J, Brook RD, Cromar K, et al. A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework. Eur Respir J. 2017;49(1). pmid:28077473
- View Article
- PubMed/NCBI
- Google Scholar
4. Yang H, Velasco, R.P., Mudu, P., Jarosinska, D. Noncommunicable diseases and air pollution. World Health Organization Regional Office of Europe: WHO 2019.
5. Stafoggia M, Cesaroni G, Peters A, Andersen ZJ, Badaloni C, Beelen R, et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: results from 11 European cohorts within the ESCAPE project. Environ Health Perspect. 2014;122(9):919–25. pmid:24835336
- View Article
- PubMed/NCBI
- Google Scholar
6. Klepac P, Locatelli I, Korošec S, Künzli N, Kukec A. Ambient air pollution and pregnancy outcomes: A comprehensive review and identification of environmental public health challenges. Environ Res. 2018;167:144–59. pmid:30014896
- View Article
- PubMed/NCBI
- Google Scholar
7. Wilkins EWL, Wickramasinghe K, Bhatnagar P, Leal J, Luengo-Fernandez R, Burns R, et al. European Cardiovascular Disease Statistics 2017. Brussels: European Heart Network; 2017.
- View Article
- Google Scholar
8. Cao G, Liu J, Liu M. Global, Regional, and National Incidence and Mortality of Neonatal Preterm Birth, 1990–2019. JAMA pediatrics. 2022;176(8):787–96. pmid:35639401
- View Article
- PubMed/NCBI
- Google Scholar
9. Hunt A, Hurley F, Ferguson J, Searl A. Social Costs of Morbidity Impacts of Air Pollution. Paris: OECD; 2016. Contract No.: No. 99.
10. Protection USE. How BenMAP-CE Estimates the Health and Economic Effects of Air Pollution United States Environmental Protection Agency United States Environmental Protection Agency 2022 [https://www.epa.gov/benmap/how-benmap-ce-estimates-health-and-economic-effects-air-pollution].
11. Holland MR, Pye, S., Hunt, A. EC4MACS Modelling Methodology. THE ALPHA Benefit Assessment Model. EMRC/AEA Energy and Environment: Metroeconomica; 2013.
12. Ostro BD. Air pollution and morbidity revisited: A specification test. Journal of Environmental Economics and Management. 1987;14(1):87–98.
- View Article
- Google Scholar
13. Alexeeff SE, Liao NS, Liu X, Van Den Eeden SK, Sidney S. Long-Term PM(2.5) Exposure and Risks of Ischemic Heart Disease and Stroke Events: Review and Meta-Analysis. J Am Heart Assoc. 2021;10(1):e016890. pmid:33381983
- View Article
- PubMed/NCBI
- Google Scholar
14. Persson J, Ferraz-Nunes J, Karlberg I. Economic burden of stroke in a large county in Sweden. BMC Health Serv Res. 2012;12(1):341.
- View Article
- Google Scholar
15. Nicholson G, Gandra SR, Halbert RJ, Richhariya A, Nordyke RJ. Patient-level costs of major cardiovascular conditions: a review of the international literature. ClinicoEconomics and outcomes research: CEOR. 2016;8:495–506. pmid:27703385
- View Article
- PubMed/NCBI
- Google Scholar
16. Mourad G, Alwin J, Strömberg A, Jaarsma T. Societal costs of non-cardiac chest pain compared with ischemic heart disease–a longitudinal study. BMC Health Serv Res. 2013;13(403). pmid:24107009
- View Article
- PubMed/NCBI
- Google Scholar
17. Tran DT, Welsh RC, Ohinmaa A, Thanh NX, Kaul P. Resource Use and Burden of Hospitalization, Outpatient, Physician, and Drug Costs in Short- and Long-term Care After Acute Myocardial Infarction. Can J Cardiol. 2018;34(10):1298–306. pmid:30170782
- View Article
- PubMed/NCBI
- Google Scholar
18. Janzon M, Henriksson M, Hasvold P, Hjelm H, Thuresson M, Jernberg T. Long-term resource use patterns and healthcare costs after myocardial infarction in a clinical practice setting: results from a contemporary nationwide registry study. Eur Heart J Qual Care Clin Outcomes. 2016;2(4):291–8. pmid:29474723
- View Article
- PubMed/NCBI
- Google Scholar
19. Trasande L, Malecha P, Attina TM. Particulate Matter Exposure and Preterm Birth: Estimates of U.S. Attributable Burden and Economic Costs. Environ Health Perspect. 2016;124(12):1913–8. pmid:27022947
- View Article
- PubMed/NCBI
- Google Scholar
20. Petrou S, Yiu HH, Kwon J. Economic consequences of preterm birth: a systematic review of the recent literature (2009–2017). Arch Dis Child. 2019;104(5):456–65. pmid:30413489
- View Article
- PubMed/NCBI
- Google Scholar
21. Pimpin L, Retat L, Fecht D, de Preux L, Sassi F, Gulliver J, et al. Estimating the costs of air pollution to the National Health Service and social care: An assessment and forecast up to 2035. (Research Article). PLoS Med. 2018;15(7):e1002602. pmid:29990358
- View Article
- PubMed/NCBI
- Google Scholar
22. Hodek JM, von der Schulenburg JM, Mittendorf T. Measuring economic consequences of preterm birth–Methodological recommendations for the evaluation of personal burden on children and their caregivers. Health Econ Rev. 2011;1(1):6. pmid:22828392
- View Article
- PubMed/NCBI
- Google Scholar
23. Lekander I, Willers C, von Euler M, Lilja M, Sunnerhagen KS, Pessah-Rasmussen H, et al. Relationship between functional disability and costs one and two years post stroke. PLoS One. 2017;12(4):e0174861. pmid:28384164
- View Article
- PubMed/NCBI
- Google Scholar
24. Moore MA, Boardman AE, Vining AR. More appropriate discounting: the rate of social time preference and the value of the social discount rate. Journal of Benefit-Cost Analysis. 2013;4(1):1–16.
- View Article
- Google Scholar
25. The public health agency of Sweden. Folkhälsan i Sverige 2016. 2016.
26. Briggs AH, Weinstein MC, Fenwick EAL, Karnon J, Sculpher MJ, Paltiel AD. Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6. Med Decis Making. 2012;32(5):722–32. pmid:22990087
- View Article
- PubMed/NCBI
- Google Scholar
27. Sverige Kommuner och Regioner. Cost per Patient (CPP) Database. 2016.
28. Committee on Understanding Premature Birth and Assuring Healthy Outcomes, Board on Health Sciences Policy. Preterm Birth: Causes, Consequences, And Prevention. 2007.
29. National Board of Health and Welfare. Graviditeter, förlossningar och nyfödda barn, Medicinska födelseregistret 1973–2014, Assisterad befruktning 1991–2013. 2015.
30. Lindström K, Winbladh B, Haglund B, Hjern A. Preterm infants as young adults: a Swedish national cohort study. Pediatrics. 2007;120(1):70–7. pmid:17606563
- View Article
- PubMed/NCBI
- Google Scholar
31. Husby IM, Stray KM, Olsen A, Lydersen S, Indredavik MS, Brubakk AM, et al. Long-term follow-up of mental health, health-related quality of life and associations with motor skills in young adults born preterm with very low birth weight. Health Qual Life Outcomes. 2016;14:56. pmid:27052007
- View Article
- PubMed/NCBI
- Google Scholar
32. Statistics Sweden. Statistical Database. In: Statistics Sweden, 2016.
33. Vredin Johansson M. Ekonomisk värdering av liv och hälsa. 2012.
34. Burström K, Sun S, Gerdtham U-G, Henriksson M, Johannesson M, Levin L-Å, et al. Swedish experience-based value sets for EQ-5D health states. Qual Life Res. 2014;23(2):431–42. pmid:23975375
- View Article
- PubMed/NCBI
- Google Scholar
35. Gencer B, Rodondi N, Auer R, Nanchen D, Räber L, Klingenberg R, et al. Health utility indexes in patients with acute coronary syndromes. 2016;3(1):e000419.
- View Article
- Google Scholar
36. Ghatnekar O, Eriksson M, Glader E-L. Mapping health outcome measures from a stroke registry to EQ-5D weights. Health And Quality Of Life Outcomes. 2013;11(34). pmid:23496957
- View Article
- PubMed/NCBI
- Google Scholar
37. Persson J, Aronsson M, Holmegaard L, Redfors P, Stenlöf K, Jood K, et al. Long-term QALY-weights among spouses of dependent and independent midlife stroke survivors. Qual Life Res. 2017;26(11):3059–68. pmid:28664459
- View Article
- PubMed/NCBI
- Google Scholar
38. Craig BM, Hartman JD, Owens MA, Brown DS. Prevalence and Losses in Quality-Adjusted Life Years of Child Health Conditions: A Burden of Disease Analysis. Matern Child Health J. 2016;20(4):862–9. pmid:26645618
- View Article
- PubMed/NCBI
- Google Scholar
39. Tandvårds- och läkemedelsförmånsverket (TLV) Nfl. Cerezyme (imiglukeras) och Vpriv (velaglukeras alfa). Underlag för beslut om subvention–Omprövning. (Dnr 1961/2015.).
40. Svensson M, Nilsson FOL, Arnberg K. Reimbursement Decisions for Pharmaceuticals in Sweden: The Impact of Disease Severity and Cost Effectiveness. Pharmacoeconomics. 2015;33(11):1229–36. pmid:26093889
- View Article
- PubMed/NCBI
- Google Scholar
41. Persson UO, S. Ett QALY är värt mer än två miljoner kronor. Läkartidningen. 2018.
42. Olofsson S, Persson, U., HUltkranz, L., Gerdtham, U. Betalningsviljan för att minska risken för icke-dödliga och dödliga skador i samband med vägtrafikolyckor—en pilotstudie med jämförelse av CV och kedje-ansats. IHE—Institute för Hälso- och Sjukvårdsekonomi The Swdish Institute for health Economics; 2016. Contract No.: ISSN 1651-7598.
43. Olofsson S, Gerdtham UG, Hultkrantz L, Persson U. Value of a QALY and VSI estimated with the chained approach. Eur J Health Econ. 2019;20(7):1063–77. pmid:31172400
- View Article
- PubMed/NCBI
- Google Scholar
44. Gustafsson M, Linden J., Forsberg B., Åström S. Quantification of population expousre to NO2, PM10 and PM2.5 and estimated health impacts 2019. IVL Swedish Environmental Research Institute; 2022. Report No: B2446.
- View Article
- Google Scholar
45. World Health O. WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: World Health Organization; 2021 2021.
46. Kim JJ, Axelrad DA, Dockins C. Preterm birth and economic benefits of reduced maternal exposure to fine particulate matter. Environ Res. 2019;170:178–86. pmid:30583127
- View Article
- PubMed/NCBI
- Google Scholar
47. Why is health spending in the United States so high? OECD 2011.
48. Sterner T, Persson UM. An Even Sterner Review: Introducing Relative Prices into the Discounting Debate. Review of environmental economics and policy. 2008;2(1):61–76.
- View Article
- Google Scholar
49. Nordhaus WD. A Review of the "Stern Review on the Economics of Climate Change". J Econ Lit. 2007;45(3):686–702.
- View Article
- Google Scholar
50. Dasgupta P. Commentary: The Stern Review’s Economics of Climate Change. Nat Inst Econ Rev. 2007;199(1):4–7.
- View Article
- Google Scholar
51. Attema AE, Bleichrodt H, L’Haridon O, Peretti-Watel P, Seror V. Discounting health and money: New evidence using a more robust method. J Risk Uncertainty. 2018;56(2):117–40. pmid:31007384
- View Article
- PubMed/NCBI
- Google Scholar
52. Haacker M, Hallett TB, Atun R. On discount rates for economic evaluations in global health. Health Policy Plan. 2020;35(1):107–14. pmid:31625564
- View Article
- PubMed/NCBI
- Google Scholar
53. Järbrink K, Kjellström, A., Althoff, K. Cost during the last year of life–implications for future health care costs. Kostnader sista levnadsåret—betydelse för framtida vårdkostnader. Västra Götalandsregion; 2012.

[ref1] 1. WHO. Ten threats to global health in 2019. World Health Organization: WHO 2019.

[ref2] 2. Krzyżanowski M, et al. Health effects of transport-related air pollution: WHO Regional Office Europe. 2005.

[ref3] 3. Thurston GD, Kipen H, Annesi-Maesano I, Balmes J, Brook RD, Cromar K, et al. A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework. Eur Respir J. 2017;49(1). pmid:28077473
View Article
PubMed/NCBI
Google Scholar

[4] View Article

[5] PubMed/NCBI

[6] Google Scholar

[ref4] 4. Yang H, Velasco, R.P., Mudu, P., Jarosinska, D. Noncommunicable diseases and air pollution. World Health Organization Regional Office of Europe: WHO 2019.

[ref5] 5. Stafoggia M, Cesaroni G, Peters A, Andersen ZJ, Badaloni C, Beelen R, et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: results from 11 European cohorts within the ESCAPE project. Environ Health Perspect. 2014;122(9):919–25. pmid:24835336
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref6] 6. Klepac P, Locatelli I, Korošec S, Künzli N, Kukec A. Ambient air pollution and pregnancy outcomes: A comprehensive review and identification of environmental public health challenges. Environ Res. 2018;167:144–59. pmid:30014896
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref7] 7. Wilkins EWL, Wickramasinghe K, Bhatnagar P, Leal J, Luengo-Fernandez R, Burns R, et al. European Cardiovascular Disease Statistics 2017. Brussels: European Heart Network; 2017.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref8] 8. Cao G, Liu J, Liu M. Global, Regional, and National Incidence and Mortality of Neonatal Preterm Birth, 1990–2019. JAMA pediatrics. 2022;176(8):787–96. pmid:35639401
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref9] 9. Hunt A, Hurley F, Ferguson J, Searl A. Social Costs of Morbidity Impacts of Air Pollution. Paris: OECD; 2016. Contract No.: No. 99.

[ref10] 10. Protection USE. How BenMAP-CE Estimates the Health and Economic Effects of Air Pollution United States Environmental Protection Agency United States Environmental Protection Agency 2022 [https://www.epa.gov/benmap/how-benmap-ce-estimates-health-and-economic-effects-air-pollution].

[ref11] 11. Holland MR, Pye, S., Hunt, A. EC4MACS Modelling Methodology. THE ALPHA Benefit Assessment Model. EMRC/AEA Energy and Environment: Metroeconomica; 2013.

[ref12] 12. Ostro BD. Air pollution and morbidity revisited: A specification test. Journal of Environmental Economics and Management. 1987;14(1):87–98.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref13] 13. Alexeeff SE, Liao NS, Liu X, Van Den Eeden SK, Sidney S. Long-Term PM(2.5) Exposure and Risks of Ischemic Heart Disease and Stroke Events: Review and Meta-Analysis. J Am Heart Assoc. 2021;10(1):e016890. pmid:33381983
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref14] 14. Persson J, Ferraz-Nunes J, Karlberg I. Economic burden of stroke in a large county in Sweden. BMC Health Serv Res. 2012;12(1):341.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref15] 15. Nicholson G, Gandra SR, Halbert RJ, Richhariya A, Nordyke RJ. Patient-level costs of major cardiovascular conditions: a review of the international literature. ClinicoEconomics and outcomes research: CEOR. 2016;8:495–506. pmid:27703385
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref16] 16. Mourad G, Alwin J, Strömberg A, Jaarsma T. Societal costs of non-cardiac chest pain compared with ischemic heart disease–a longitudinal study. BMC Health Serv Res. 2013;13(403). pmid:24107009
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref17] 17. Tran DT, Welsh RC, Ohinmaa A, Thanh NX, Kaul P. Resource Use and Burden of Hospitalization, Outpatient, Physician, and Drug Costs in Short- and Long-term Care After Acute Myocardial Infarction. Can J Cardiol. 2018;34(10):1298–306. pmid:30170782
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref18] 18. Janzon M, Henriksson M, Hasvold P, Hjelm H, Thuresson M, Jernberg T. Long-term resource use patterns and healthcare costs after myocardial infarction in a clinical practice setting: results from a contemporary nationwide registry study. Eur Heart J Qual Care Clin Outcomes. 2016;2(4):291–8. pmid:29474723
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref19] 19. Trasande L, Malecha P, Attina TM. Particulate Matter Exposure and Preterm Birth: Estimates of U.S. Attributable Burden and Economic Costs. Environ Health Perspect. 2016;124(12):1913–8. pmid:27022947
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref20] 20. Petrou S, Yiu HH, Kwon J. Economic consequences of preterm birth: a systematic review of the recent literature (2009–2017). Arch Dis Child. 2019;104(5):456–65. pmid:30413489
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref21] 21. Pimpin L, Retat L, Fecht D, de Preux L, Sassi F, Gulliver J, et al. Estimating the costs of air pollution to the National Health Service and social care: An assessment and forecast up to 2035. (Research Article). PLoS Med. 2018;15(7):e1002602. pmid:29990358
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref22] 22. Hodek JM, von der Schulenburg JM, Mittendorf T. Measuring economic consequences of preterm birth–Methodological recommendations for the evaluation of personal burden on children and their caregivers. Health Econ Rev. 2011;1(1):6. pmid:22828392
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref23] 23. Lekander I, Willers C, von Euler M, Lilja M, Sunnerhagen KS, Pessah-Rasmussen H, et al. Relationship between functional disability and costs one and two years post stroke. PLoS One. 2017;12(4):e0174861. pmid:28384164
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref24] 24. Moore MA, Boardman AE, Vining AR. More appropriate discounting: the rate of social time preference and the value of the social discount rate. Journal of Benefit-Cost Analysis. 2013;4(1):1–16.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref25] 25. The public health agency of Sweden. Folkhälsan i Sverige 2016. 2016.

[ref26] 26. Briggs AH, Weinstein MC, Fenwick EAL, Karnon J, Sculpher MJ, Paltiel AD. Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6. Med Decis Making. 2012;32(5):722–32. pmid:22990087
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref27] 27. Sverige Kommuner och Regioner. Cost per Patient (CPP) Database. 2016.

[ref28] 28. Committee on Understanding Premature Birth and Assuring Healthy Outcomes, Board on Health Sciences Policy. Preterm Birth: Causes, Consequences, And Prevention. 2007.

[ref29] 29. National Board of Health and Welfare. Graviditeter, förlossningar och nyfödda barn, Medicinska födelseregistret 1973–2014, Assisterad befruktning 1991–2013. 2015.

[ref30] 30. Lindström K, Winbladh B, Haglund B, Hjern A. Preterm infants as young adults: a Swedish national cohort study. Pediatrics. 2007;120(1):70–7. pmid:17606563
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref31] 31. Husby IM, Stray KM, Olsen A, Lydersen S, Indredavik MS, Brubakk AM, et al. Long-term follow-up of mental health, health-related quality of life and associations with motor skills in young adults born preterm with very low birth weight. Health Qual Life Outcomes. 2016;14:56. pmid:27052007
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref32] 32. Statistics Sweden. Statistical Database. In: Statistics Sweden, 2016.

[ref33] 33. Vredin Johansson M. Ekonomisk värdering av liv och hälsa. 2012.

[ref34] 34. Burström K, Sun S, Gerdtham U-G, Henriksson M, Johannesson M, Levin L-Å, et al. Swedish experience-based value sets for EQ-5D health states. Qual Life Res. 2014;23(2):431–42. pmid:23975375
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref35] 35. Gencer B, Rodondi N, Auer R, Nanchen D, Räber L, Klingenberg R, et al. Health utility indexes in patients with acute coronary syndromes. 2016;3(1):e000419.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref36] 36. Ghatnekar O, Eriksson M, Glader E-L. Mapping health outcome measures from a stroke registry to EQ-5D weights. Health And Quality Of Life Outcomes. 2013;11(34). pmid:23496957
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref37] 37. Persson J, Aronsson M, Holmegaard L, Redfors P, Stenlöf K, Jood K, et al. Long-term QALY-weights among spouses of dependent and independent midlife stroke survivors. Qual Life Res. 2017;26(11):3059–68. pmid:28664459
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref38] 38. Craig BM, Hartman JD, Owens MA, Brown DS. Prevalence and Losses in Quality-Adjusted Life Years of Child Health Conditions: A Burden of Disease Analysis. Matern Child Health J. 2016;20(4):862–9. pmid:26645618
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref39] 39. Tandvårds- och läkemedelsförmånsverket (TLV) Nfl. Cerezyme (imiglukeras) och Vpriv (velaglukeras alfa). Underlag för beslut om subvention–Omprövning. (Dnr 1961/2015.).

[ref40] 40. Svensson M, Nilsson FOL, Arnberg K. Reimbursement Decisions for Pharmaceuticals in Sweden: The Impact of Disease Severity and Cost Effectiveness. Pharmacoeconomics. 2015;33(11):1229–36. pmid:26093889
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref41] 41. Persson UO, S. Ett QALY är värt mer än två miljoner kronor. Läkartidningen. 2018.

[ref42] 42. Olofsson S, Persson, U., HUltkranz, L., Gerdtham, U. Betalningsviljan för att minska risken för icke-dödliga och dödliga skador i samband med vägtrafikolyckor—en pilotstudie med jämförelse av CV och kedje-ansats. IHE—Institute för Hälso- och Sjukvårdsekonomi The Swdish Institute for health Economics; 2016. Contract No.: ISSN 1651-7598.

[ref43] 43. Olofsson S, Gerdtham UG, Hultkrantz L, Persson U. Value of a QALY and VSI estimated with the chained approach. Eur J Health Econ. 2019;20(7):1063–77. pmid:31172400
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref44] 44. Gustafsson M, Linden J., Forsberg B., Åström S. Quantification of population expousre to NO2, PM10 and PM2.5 and estimated health impacts 2019. IVL Swedish Environmental Research Institute; 2022. Report No: B2446.
View Article
Google Scholar

[124] View Article

[125] Google Scholar

[ref45] 45. World Health O. WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: World Health Organization; 2021 2021.

[ref46] 46. Kim JJ, Axelrad DA, Dockins C. Preterm birth and economic benefits of reduced maternal exposure to fine particulate matter. Environ Res. 2019;170:178–86. pmid:30583127
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref47] 47. Why is health spending in the United States so high? OECD 2011.

[ref48] 48. Sterner T, Persson UM. An Even Sterner Review: Introducing Relative Prices into the Discounting Debate. Review of environmental economics and policy. 2008;2(1):61–76.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref49] 49. Nordhaus WD. A Review of the "Stern Review on the Economics of Climate Change". J Econ Lit. 2007;45(3):686–702.
View Article
Google Scholar

[136] View Article

[137] Google Scholar

[ref50] 50. Dasgupta P. Commentary: The Stern Review’s Economics of Climate Change. Nat Inst Econ Rev. 2007;199(1):4–7.
View Article
Google Scholar

[139] View Article

[140] Google Scholar

[ref51] 51. Attema AE, Bleichrodt H, L’Haridon O, Peretti-Watel P, Seror V. Discounting health and money: New evidence using a more robust method. J Risk Uncertainty. 2018;56(2):117–40. pmid:31007384
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref52] 52. Haacker M, Hallett TB, Atun R. On discount rates for economic evaluations in global health. Health Policy Plan. 2020;35(1):107–14. pmid:31625564
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref53] 53. Järbrink K, Kjellström, A., Althoff, K. Cost during the last year of life–implications for future health care costs. Kostnader sista levnadsåret—betydelse för framtida vårdkostnader. Västra Götalandsregion; 2012.

Figures

Abstract

Background

Objectives

Methods

Results

Recommendation

Introduction

Materials and methods

Literature search

Calculation of the Net Present Value (NPV) of specific outcomes

Formal costs

Stroke.

Myocardial infarction.

Preterm birth.

Informal costs

Results

Stroke

MI

PTB

Sensitivity analysis

Discussion

Formal costs

Recommendations

Conclusion

Supporting information

S1 File. Search terms applied and results of literature search in PubMed to retrieve cost-of-illness studies for stroke and MI.

S2 File. Data on occurrence, duration, and indirect costs associated with preterm birth.

S3 File. Disaggregated costs-per case of stroke, MI and PTB and sensitivity analyses.

Acknowledgments

References