Abstract
Introduction
Our systematic evidence map aimed to identify lung-related epidemiological estimates to populate the International Respiratory Coalition's Lung Facts website. We highlight important evidence gaps, suggest how they could be filled, and provide bespoke societal cost estimates to inform resource allocation.
Methods
We examined 14 lung conditions across 53 World Health Organization Europe countries, seeking incidence, prevalence, mortality, years of life lost, years lived with disability, and disability-adjusted life year (DALY) estimates by age/sex. Global Burden of Disease (GBD) study estimates were obtained for nine GBD-included lung conditions: asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, lower respiratory infections, lung cancer/tracheal, bronchus and lung cancer, tuberculosis, mesothelioma, COVID-19 and pulmonary arterial hypertension. Systematic searches of bibliographic databases were necessary for five non-GBD-included lung conditions: cystic fibrosis, obstructive sleep apnoea (OSA), influenza, alpha-1 antitrypsin deficiency (A1AD) and bronchiectasis. All-age country-specific DALY estimates were multiplied by gross domestic product per capita as a proxy for a country's wealth and prosperity to estimate societal costs.
Results
Complete data for nine lung conditions for all countries were extracted from GBD, enabling societal cost estimation. Significant gaps were found for A1AD (only prevalence for 24 countries) and bronchiectasis (incidence, prevalence and mortality for between one and five countries), with more, but incomplete, estimates for cystic fibrosis, OSA and influenza.
Conclusions
More epidemiological evidence is required for A1AD, bronchiectasis, cystic fibrosis, OSA, and influenza. Inclusion in the GDB study could help address these gaps. Lung Facts provides comprehensive lung-related epidemiological and societal cost estimates covering 53 countries to support policy-makers advocating for respiratory interventions.
Shareable abstract
Systematic evidence map identifies lung-related epidemiological data to populate the IRC's Lung Facts website, highlighting evidence gaps, suggesting how they could be filled and giving bespoke societal cost estimates to inform resource allocation https://bit.ly/4klscLG
Introduction
Respiratory diseases contribute greatly to morbidity and mortality worldwide [1]. To raise public and decision-maker awareness of lung health importance and increasing disease burden, a European Lung White Book was launched in 2003 (updated 2013) [2, 3]. The European Respiratory Society (ERS) founded the International Respiratory Coalition (IRC) in 2021, to promote lung health and improve respiratory care by setting up World Health Organization (WHO) Europe-based national respiratory strategies. An update of the White Book epidemiological and economic data was urgently needed. Thus, the “Lung Facts” website was launched in 2022 to meet this need: https://international-respiratory-coalition.org/lung-facts/
Lung Facts provides key epidemiological and economic data for 14 major chronic respiratory conditions within the 53 WHO Europe countries. The IRC updates Lung Facts every 2 years; a key data source is the Global Burden of Disease (GBD) study (www.healthdata.org/research-analysis/gbd). The GBD study collates epidemiological data from multiple sources (e.g. registries, epidemiological surveillance, primary scientific studies), alongside survey data on self-reported health and health-related risk behaviours. Global data are integrated into a single statistical model to estimate incidence, prevalence and mortality. A demographic model estimates years of life lost (YLL), years lived with disability (YLD), and subsequently disability-adjusted life years (DALYs). However, the GBD does not provide evidence for all lung conditions (e.g. cystic fibrosis, sleep apnoea) nor economic considerations (e.g. societal costs).
We identify and describe the epidemiology of 14 respiratory diseases in the WHO Europe region for 2010 onwards. When all-age DALY estimates are available, we subsequently estimate societal costs by monetising these DALYs using gross domestic product (GDP) per capita as a proxy for a country's wealth and prosperity. The Lung Facts website provides all identified and bespoke estimates. This article summarises our methodology and key findings, including evidence gaps with suggestions to fill these gaps.
Methods
The 14 conditions included in Lung Facts are nine diseases reported within GBD, with a selected group of five other important respiratory diseases. These include frequent disorders (obstructive sleep apnoea (OSA), influenza), alongside less common (bronchiectasis) and rare disorders (alpha-1 antitrypsin deficiency (A1AD), cystic fibrosis). The selection of diseases was based on those that were included in the Lung White Book. This selection was reviewed and revised. They were considered to be the key respiratory conditions to include based on expert consultation.
Our methods have three elements. First, collating relevant GBD-sourced evidence. Second, collating non-GBD-sourced evidence using bibliographic searches. Third, estimating societal cost evidence not available in GBD [4]. Initial work was in 2022, with a 2024 update.
GBD data
The nine GBD-represented conditions are asthma; COPD; interstitial lung disease and pulmonary sarcoidosis; lower respiratory infections; lung cancer/tracheal, bronchus and lung cancer; tuberculosis; mesothelioma; pulmonary arterial hypertension (2024 only); COVID-19 (2024 only). Data were downloaded from the GBD website [5] for 2015 and 2019 (in 2022), then 2021 (in 2024) (table 1). The GBD study focuses on epidemiological estimates: incidence, prevalence, mortality, YLL, YLD, and DALYs. Absolute population counts and estimated rates per 100 000 people were extracted. Estimates were stratified by age, sex, country, region and across Europe. Data samples were checked manually against the GBD website to ensure fidelity.
TABLE 1.
Inclusion criteria for Global Burden of Disease (GBD) data and bibliographic searches for non-GBD data to identify systematic reviews and primary studies
| Inclusion criteria | |
|---|---|
| Population | |
|
|
|
|
| Exposure and study design | |
| 1) GBD data |
|
| 2) Bibliographic searches | a) Systematic reviews¶: cystic fibrosis; OSA; influenza b) Primary studies: alpha-1 antitrypsin deficiency; bronchiectasis; pulmonary hypertension# |
| Outcomes | Rates and absolute numbers of: incidence; prevalence; DALYs; YLL; mortality Any type of reporting of the outcome. For example, rates may be age-standardised, reported per capita, reported as a ratio (1 case per X head of population) reported by sex, etc. |
| Settings | |
| WHO European Region | |
| Central Asia | Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Türkiye, Turkmenistan, Uzbekistan |
| Eastern Europe | Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Hungary, Macedonia, Montenegro, Poland, Romania, Serbia, Slovakia, Slovenia |
| Central Europe | Belarus, Estonia, Latvia, Lithuania, Moldova, Russia, Ukraine |
| Western Europe | Andorra, Austria, Belgium, Cyprus, Denmark+, Finland, France, Germany, Greece, Greenland+, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Spain, Sweden, Switzerland, United Kingdom |
WHO: World Health Organization; OSA: obstructive sleep apnoea; DALYs: disability-adjusted life years; YLL: years of life lost. #: pulmonary arterial hypertension and COVID-19 were added to the GBD study in 2024. Therefore, pulmonary hypertension was dropped from search 2b in 2024, and both conditions were added to search 1; ¶: where no systematic reviews were available, the criteria were widened to include nonsystematic reviews, if these provided data of use to the review; +: Greenland is part of the Kingdom of Denmark, but is reported separately by the GBD study.
Non-GBD data: bibliographic searches
Focused bibliographic searches for the five conditions not within GBD (cystic fibrosis, OSA, influenza, A1AD, bronchiectasis) sought systematic reviews for commonly studied conditions, and any-design studies for less commonly studied conditions (table 1, Exposure and study design). Searches were run in 2022 in MEDLINE and updated in 2024 with MEDLINE, Embase and the Cochrane Library. Searches were structured around terms for the conditions and terms for the outcomes. The bibliographic searches were necessarily focussed due to time and resource constraints. To mitigate the risk of missing important studies (see also Discussion, Collated data: strengths and limitations), we selected MEDLINE as the principal database, as it has been shown to retrieve >90% [6] of studies that are findable using bibliographic database searches. We used additional search techniques (i.e. citation chasing, reference list checking, focused searches on Google scholar, and consultation with experts) to identify additional studies (supplementary file S1). Search results were exported into EndNote and duplicates removed. Pulmonary hypertension was included in the 2022 searches, but not in 2024 since it was added to the GBD study.
Table 1 details the eligibility criteria and population, exposure, study design, outcomes, and setting. Studies were selected for inclusion by two reviewers. Disagreements were resolved through discussion between the reviewers, or with topic experts. Data could be expressed in any format, e.g. absolute numbers, ratios, per capita, per 100 000 people. Where possible, rate data were converted to rate per 100 000 people.
In 2022, data were extracted by one reviewer and checked by a second (supplementary file S1). In 2024, summary data were extracted into a table by one reviewer to create an evidence map. ERS disease experts used the evidence-map to decide if a Lung Facts update was required. No 2024 update data were deemed superior to the existing 2022 data; thus, full data extraction with double checking was not performed. Links to new studies are provided within Lung Facts.
Societal cost estimation
When operationalising health-related metrics (e.g. DALYs or quality-adjusted life years (QALYs)) to inform resource allocation, applying a monetary value to (i.e. monetising) those health-related metrics is needed to determine the “opportunity cost” from any financial investment, i.e. what is foregone when investing in health-related outputs because the resources are no longer available to spend elsewhere. We refer to monetised health-related metrics as a societal cost, i.e. the health burden's monetary value. It is important to recognise that these monetised health-related metrics represent the monetised burden-of-illness (i.e. monetised DALYs), not the cost of illness, such as the amount of money the healthcare system pays to prevent/treat such conditions.
DALY monetisation using GDP stems from the WHO's Choosing Interventions that are Cost Effective (WHO-CHOICE) initiative, whereby 1–3×GDP per capita was suggested as a country-specific cost-effectiveness threshold (e.g. the amount of money a country is willing to pay to prevent one DALY) for cost-effectiveness analysis [7–10]. Robinson et al. [10] describes the GDP-based threshold rationale. In essence, GDP is a standard measure of value created through goods and services in a country during a certain period: it measures the income earned from production, or the total amount spent on final goods and services (less imports) [11]. GDP guides policy-makers, investors and businesses in strategic decision-making given an economy's activity, with GDP per capita being the GDP per person in a country's population. Thus, the WHO report suggested that a DALY prevented should be equivalent to the per-capita income, or extra market income, created from an intervention's outcome. However, benefits may be (up to three times) higher due to non-income-based benefits (e.g. reducing pain/suffering) [7]. Therefore, without a defined and/or evidence-based DALY monetary value, 1–3×GDP per capita could be used. More recently, WHO-CHOICE moved away from using any defined cost-effectiveness threshold, not just GDP-based thresholds [12].
Franklin et al. [13] rationalised why, in the absence of a defined or evidence-based DALY monetary value, using 1×GDP per capita as a benchmarked value (i.e. a reference point) of what countries should aim to invest in health-related outcomes could still have many benefits for transparent and consistent decision-making, citing others who debate for and against this point. Franklin et al. [13] subsequently suggested using 1×GDP per capita specifically, as it better aligns with monetised QALY valuations as a similar, widely used health-related metric.
Thus, we use GDP per capita to estimate the resources available in each country that could be invested in lung interventions (e.g. preventative strategies and medical care) to address the DALY burden of lung condition(s). That is, 1×GDP per capita is multiplied by the all-age DALY burden at a population-level or rate per 100 000 people to estimate the societal cost, to be used as a reference point for negotiation with policy-makers investing in lung interventions. GDP per capita is obtained from the WHO Global Health Expenditure Database [14], presented in 2021 values based on United States Dollars (USD$$2021), international dollars (Int$$2021), Euros (EUR€2021), and National Currency Units (NCU2021).
Results
To complement Lung Facts, we describe example GBD and non-GBD epidemiological estimates, and bespoke societal cost estimates, in tables 2 and 3 and figure 1. Lung Facts provides all estimates by country, lung condition, age and sex: https://international-respiratory-coalition.org/lung-facts
TABLE 2.
Asthma Global Burden of Disease (GBD) 2021: age-standardised estimates per 100 000 people for 54 countries and World Health Organization (WHO) European subregions
| Incidence (95% UI) | Prevalence (95% UI) | DALYs (95% UI) | YLL (95% UI) | Mortality (95% UI) | |
|---|---|---|---|---|---|
| WHO European subregions | 535.4 (434.1–673.4) | 4607.1 (3907.7–5378.7) | 204.1 (137.5–292.3) | 22.6 (21.1–24.2) | 0.9 (0.9–1) |
| Central Asia | 409.3 (338.4–509.6) | 2577.2 (2227.1–2979.2) | 198.8 (158.9–245.6) | 97.9 (84.5–114) | 4.3 (3.8–5.1) |
| Armenia | 322.9 (250.7–419) | 1753.8 (1411.3–2210) | 77.6 (49.4–111.4) | 7.6 (6.7–8.7) | 0.4 (0.3–0.4) |
| Azerbaijan | 379.9 (310.9–480) | 2247.7 (1909.2–2633.4) | 168.2 (128.9–222.2) | 79.9 (58.8–116.1) | 3.6 (2.7–4.8) |
| Georgia | 370.4 (296.8–470.7) | 2016.7 (1680.7–2434.5) | 114.8 (84.7–157.4) | 34.5 (29.6–40.8) | 1.6 (1.4–1.9) |
| Kazakhstan | 284.4 (233.7–357.9) | 1599.8 (1361–1888.6) | 250.4 (200.8–310.8) | 187.9 (146.1–241.2) | 8.6 (6.7–11.3) |
| Kyrgyzstan | 421.5 (343.4–536.1) | 2572.9 (2149.4–3076) | 134.6 (96.5–185.3) | 32.8 (26.4–40) | 1.3 (1.1–1.6) |
| Tajikistan | 378.7 (311.7–476.5) | 2170.6 (1869.8–2590.1) | 212.5 (156.8–273.4) | 127.5 (90.6–175.6) | 6.6 (4.7–8.8) |
| Türkiye | 711.3 (593.5–869.4) | 4888.7 (4199.5–5680.4) | 249.1 (176.9–342) | 58.4 (46.8–71.9) | 3.1 (2.4–4) |
| Turkmenistan | 318.9 (254.1–407.4) | 1750 (1442.3–2138.7) | 118.1 (86.1–159.1) | 48.4 (32.7–70.5) | 1.6 (1.1–2.2) |
| Uzbekistan | 532.7 (441.5–652.7) | 3791.6 (3314.2–4312.9) | 237.1 (181.8–307.1) | 89.5 (75.6–107.7) | 3.9 (3.4–4.7) |
| Central Europe | 898.7 (725.3–1136.9) | 5643 (4722.7–6691.6) | 239.1 (156.5–350.5) | 15.7 (14.4–17.3) | 0.8 (0.7–0.9) |
| Albania | 471.2 (382.9–589.6) | 2687.2 (2244–3207.9) | 164.3 (117.4–221.4) | 58 (40.8–82.4) | 3.4 (2.3–4.8) |
| Bosnia and Herzegovina | 746.3 (605.8–943.4) | 4972.2 (4133.3–6036.3) | 214.3 (139.4–308) | 18.6 (14.3–24) | 0.9 (0.7–1.2) |
| Bulgaria | 587.2 (470.3–744.4) | 3606.6 (2945.8–4410.9) | 150.2 (94.5–221.2) | 7.3 (6.2–8.5) | 0.3 (0.3–0.4) |
| Croatia | 639.3 (520.1–806.4) | 4070.4 (3362.3–4977) | 172.9 (111.9–249.8) | 12.3 (10–15) | 0.7 (0.6–0.9) |
| Czech Republic | 517.4 (413.3–666) | 3043.3 (2488.7–3715.8) | 130.5 (82.5–189.1) | 9.6 (8.4–10.8) | 0.5 (0.4–0.6) |
| Hungary | 544.2 (437.9–696.5) | 3282.8 (2700.8–3982.4) | 140.1 (89.3–205.9) | 9.9 (8.6–11.1) | 0.5 (0.4–0.5) |
| Macedonia | 796.4 (663.8–986.2) | 5654.9 (4836.5–6652.9) | 261.4 (180.7–362.7) | 41.6 (30–63.9) | 2.3 (1.8–3.4) |
| Montenegro | 591.6 (473.4–753.5) | 3594.6 (2887.7–4497.3) | 149.1 (93.8–218.8) | 6.1 (4.7–7.7) | 0.3 (0.3–0.4) |
| Poland | 1467.9 (1163.4–1879.8) | 9329.1 (7839.4–11 200.3) | 384.9 (251.4–567.2) | 15.9 (14.5–17) | 0.8 (0.7–0.8) |
| Romania | 699.7 (560.4–881.4) | 4526.6 (3745.4–5513.6) | 192.2 (123.1–277.7) | 12.7 (11.1–14.2) | 0.6 (0.5–0.7) |
| Serbia | 536.9 (434.5–675.9) | 3234.9 (2734.7–3871.5) | 163.7 (113.7–227.2) | 35.9 (29.1–44.2) | 1.9 (1.5–2.3) |
| Slovakia | 515.6 (414.6–652) | 3060.8 (2533.7–3742.6) | 131.1 (83.7–192.8) | 9.6 (7.8–12.5) | 0.5 (0.4–0.6) |
| Slovenia | 777.4 (631.1–977.5) | 5374.8 (4469.3–6479.2) | 218 (138.1–318.9) | 5.7 (4.9–6.5) | 0.3 (0.3–0.4) |
| Eastern Europe | 460.1 (360.8–601.7) | 2622 (2125–3234) | 116.5 (76.3–169.5) | 12.5 (11.6–13.4) | 0.5 (0.4–0.5) |
| Belarus | 627.8 (494.7–793) | 4210.1 (3487.4–5059.4) | 172.8 (111.2–252) | 7.2 (5.9–8.6) | 0.2 (0.2–0.3) |
| Estonia | 463 (372.9–591.7) | 2789.3 (2330.5–3387.6) | 121.8 (80.1–174.5) | 11.6 (10.1–13.1) | 0.6 (0.5–0.6) |
| Latvia | 510.1 (408.5–647.2) | 3094.6 (2578.2–3753.2) | 138.1 (91.7–198.7) | 15.5 (13.4–17.8) | 0.7 (0.6–0.8) |
| Lithuania | 476.5 (381.4–609.4) | 2819.6 (2331.8–3408.3) | 121.6 (77.8–176) | 10 (8.7–11.2) | 0.4 (0.4–0.5) |
| Moldova | 464.5 (366.5–598.2) | 2692.8 (2203.9–3279.2) | 117.9 (76–172.1) | 10.7 (9.4–12.2) | 0.4 (0.3–0.4) |
| Russia | 431.1 (337.9–565.9) | 2418.9 (1960.8–2967.8) | 111 (74.2–160.6) | 15.1 (14–16.2) | 0.6 (0.5–0.6) |
| Ukraine | 534.2 (408.2–709.5) | 2970.9 (2345.2–3690.8) | 123.9 (76.2–182.6) | 5.4 (3.9–7.1) | 0.2 (0.1–0.2) |
| Western Europe | 498.8 (404.7–632.6) | 5886.8 (4944–6943.6) | 248.3 (161.3–359.5) | 16.4 (15.5–17) | 0.7 (0.6–0.7) |
| Andorra | 494 (401.2–626.5) | 6536.6 (5337.2–7774.3) | 274.9 (177.1–406.9) | 17.3 (12.1–23) | 0.6 (0.4–0.8) |
| Austria | 416.6 (339.9–532.7) | 4788.8 (3942.2–5796.4) | 203.1 (131.4–297.4) | 14.1 (13.2–15) | 0.5 (0.5–0.6) |
| Belgium | 407 (329.6–530.6) | 4618.8 (3855.3–5517.6) | 197.9 (128.9–283.9) | 16 (14.9–17.1) | 0.6 (0.5–0.7) |
| Cyprus | 524.6 (417.5–661.2) | 7411.9 (6003.5–9009.1) | 327.1 (214.7–474.7) | 36.5 (30.5–43.9) | 2 (1.7–2.5) |
| Denmark# | 460.5 (371.7–582.5) | 4631.3 (3772.2–5507.2) | 201.3 (132.7–287.3) | 18.6 (17.1–20) | 0.8 (0.7–0.8) |
| Finland | 462.6 (378.3–586.9) | 6163 (5111.4–7331) | 255.5 (165.8–365.9) | 14 (12.7–15) | 0.7 (0.6–0.7) |
| France | 484.6 (392.3–611.2) | 6244.6 (5192.7–7435.8) | 261.2 (167.8–381.1) | 15.6 (14.5–16.6) | 0.6 (0.6–0.7) |
| Germany | 388.8 (315.8–498.4) | 4262.2 (3529.5–5050.3) | 183.5 (119–260.6) | 15.5 (14.5–16.5) | 0.6 (0.5–0.6) |
| Greece | 411.5 (328.4–522.7) | 4781.9 (3914.3–5721.5) | 194.3 (122.7–283.2) | 6 (5.7–6.4) | 0.2 (0.2–0.2) |
| Greenland# | 1067.7 (865.6–1350.2) | 7632.2 (6612.9–8851) | 350.6 (241.7–494.6) | 51.6 (42.5–62.4) | 1.5 (1.3–1.8) |
| Iceland | 634.6 (511.8–817.3) | 7330.5 (5957.6–8914.1) | 306.5 (193.1–453.2) | 16.6 (15–18.2) | 0.6 (0.6–0.7) |
| Ireland | 525.5 (417.6–670.5) | 6914.5 (5760.1–8351.5) | 288.1 (181–418.3) | 15.4 (14.1–16.5) | 0.6 (0.5–0.7) |
| Israel | 365.5 (294.4–472.9) | 3765.6 (3130.6–4472.4) | 169.5 (112.3–240.2) | 20.6 (18.6–22) | 1 (0.8–1.1) |
| Italy | 415.1 (323.4–540.1) | 3722.9 (2995.2–4569) | 152.4 (96.5–227.4) | 5.6 (5.1–5.9) | 0.3 (0.2–0.3) |
| Luxembourg | 514.3 (417.4–662) | 7102.3 (5964–8441.9) | 299.8 (194.3–439.5) | 20 (17.9–22.2) | 0.8 (0.8–0.9) |
| Malta | 502.1 (404–644.3) | 6700.3 (5456–8082) | 277 (175.6–410.3) | 13.5 (11.9–15.3) | 0.6 (0.5–0.6) |
| Monaco | 474.6 (376.4–605.8) | 6054.7 (4991–7148.9) | 243.7 (151.6–370.9) | 4.7 (3.6–6) | 0.2 (0.1–0.2) |
| Netherlands | 339 (280.7–419.6) | 6507.1 (5455.3–7766.1) | 265.9 (169.1–388.9) | 9.7 (9–10.4) | 0.4 (0.4–0.5) |
| Norway | 644.3 (511–823.6) | 6050.4 (4949.5–7293.2) | 255.7 (167.2–375.3) | 16.7 (15.6–17.5) | 0.8 (0.7–0.9) |
| Portugal | 621.8 (514.9–792.4) | 9465.4 (7816.9–11 402.8) | 386.8 (246.2–572.9) | 15.8 (14.7–16.7) | 0.6 (0.6–0.7) |
| San Marino | 474.2 (382.8–600) | 5999.9 (4970.9–7191.1) | 243.4 (154.8–364.1) | 7 (4.5–9.7) | 0.3 (0.2–0.4) |
| Spain | 402.7 (322.2–512.9) | 4332.6 (3602.4–5189.6) | 187 (122.3–264.8) | 16.3 (14.4–17.5) | 0.9 (0.7–1) |
| Sweden | 679.6 (541.4–872.6) | 7522.6 (6140.8–9106.1) | 309.1 (199–459.2) | 12.5 (11.2–13.9) | 0.6 (0.5–0.7) |
| Switzerland | 465.8 (375.8–604.2) | 5655.5 (4527–6839.2) | 235.2 (149.9–345) | 12.4 (11.4–13.4) | 0.5 (0.5–0.6) |
| United Kingdom | 793.7 (643.1–999) | 10 029.8 (8450.2–11 747.4) | 427.7 (282.7–620.1) | 33.1 (31.5–34.1) | 1.3 (1.1–1.3) |
95% UI: 95% uncertainty interval; DALYs: disability-adjusted life years; YLL: years of life lost. #: Greenland is part of the Kingdom of Denmark, but is reported separately by the GBD study.
TABLE 3.
Asthma societal cost examples based on Global Burden of Disease (GBD) 2021 disability-adjusted life year (DALY) rates and gross domestic product (GDP) per capita by 53 countries and World Health Organization (WHO) European region
| NCU | DALY rates per 100 000 people, | GDP per capita (2021) | Societal cost per 100 000 people (2021)# | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Mean (95% CI) | Int$¶ | USD | EUR | NCU | Int$¶ | USD | EUR | NCU | ||
| Albania | ALL | 191.2 (142.7–250) | 15 572 | 6395 | 5407 | 662 007 | 2 974 258 | 1 221 439 | 1 032 720 | 126 443 404 |
| Andorra | EUR | 280.6 (188.7–401.3) | 58 885 | 42 066 | 35 567 | 35 567 | 16 487 824 | 11 778 492 | 9 958 644 | 9 958 644 |
| Armenia | AMD | 72.5 (48.6–102.1) | 15 592 | 4967 | 4199 | 2 501 981 | 1 122 659 | 357 589 | 302 340 | 180 142 615 |
| Austria | EUR | 206 (138.9–291.7) | 60 118 | 53 777 | 45 468 | 45 468 | 12 384 331 | 11 077 984 | 9 366 369 | 9 366 369 |
| Azerbaijan | AZN | 161.9 (123.4–214.1) | 15 585 | 5296 | 4478 | 9004 | 2 509 216 | 852 730 | 720 978 | 1 449 641 |
| Belarus | BYN | 177.6 (118.3–251.5) | 21 160 | 7121 | 6021 | 18 078 | 3 745 273 | 1 260 403 | 1 065 663 | 3 199 785 |
| Belgium | EUR | 199.8 (136.6–277.3) | 59 035 | 51 438 | 43 490 | 43 490 | 11 747 995 | 10 236 127 | 8 654 584 | 8 654 584 |
| Bosnia and Herzegovina | BAM | 223.5 (155.9–306.8) | 17 701 | 7230 | 6113 | 11 956 | 3 947 385 | 1 612 331 | 1 363 216 | 2 666 161 |
| Bulgaria | BGN | 138.9 (92.8–194.2) | 27 948 | 12 156 | 10 278 | 20 099 | 3 856 862 | 1 677 509 | 1 418 324 | 2 773 594 |
| Croatia | EUR | 183 (125.2–253.3) | 33 381 | 17 094 | 14 453 | 14 453 | 6 108 703 | 3 128 256 | 2 644 922 | 2 644 922 |
| Cyprus | EUR | 354.3 (236.8–499.1) | 44 610 | 31 709 | 26 810 | 26 810 | 15 791 768 | 11 224 953 | 9 490 630 | 9 490 630 |
| Czech Republic | CZK | 122.9 (83.4–169.1) | 44 783 | 26 331 | 22 263 | 570 813 | 5 463 524 | 3 212 413 | 2 716 076 | 69 639 227 |
| Denmark+ | DKK | 202.6 (140.3–279.1) | 65 069 | 68 202 | 57 664 | 428 794 | 13 144 001 | 13 776 822 | 11 648 220 | 86 616 440 |
| Estonia | EUR | 129.1 (91.1–177) | 43 505 | 27 962 | 23 642 | 23 642 | 5 612 158 | 3 607 080 | 3 049 764 | 3 049 764 |
| Finland | EUR | 280.3 (191.6–389) | 54 756 | 53 538 | 45 266 | 45 266 | 15 331 708 | 14 990 728 | 12 674 570 | 12 674 570 |
| France | EUR | 271.4 (183.9–380.4) | 51 434 | 43 719 | 36 964 | 36 964 | 13 938 692 | 11 847 840 | 10 017 278 | 10 017 278 |
| Georgia | GEL | 127.2 (101.1–163.6) | 16 790 | 4956 | 4190 | 15 967 | 2 132 325 | 629 435 | 532 183 | 2 027 786 |
| Germany | EUR | 186.8 (129.1–256.3) | 58 813 | 51 229 | 43 314 | 43 314 | 10 939 237 | 9 528 564 | 8 056 344 | 8 056 344 |
| Greece | EUR | 193.1 (125.4–275.5) | 31 185 | 20 122 | 17 013 | 17 013 | 6 018 737 | 3 883 519 | 3 283 492 | 3 283 492 |
| Hungary | HUF | 134 (90.9–186.5) | 36 694 | 18 732 | 15 838 | 5 678 391 | 4 917 050 | 2 510 069 | 2 122 248 | 760 904 392 |
| Iceland | ISK | 302.7 (196.6–436.3) | 58 524 | 68 999 | 58 338 | 8 762 068 | 17 674 220 | 20 837 612 | 17 618 075 | 2 646 144 575 |
| Ireland | EUR | 285.6 (185.1–404.9) | 107 142 | 100 709 | 85 149 | 85 149 | 30 535 405 | 28 702 112 | 24 267 462 | 24 267 462 |
| Israel | ILS | 173.2 (116.3–241.9) | 46 365 | 54 890 | 46 409 | 177 306 | 8 021 149 | 9 496 016 | 8 028 824 | 30 674 011 |
| Italy | EUR | 154.3 (102.7–222.9) | 46 593 | 35 694 | 30 179 | 30 179 | 7 175 390 | 5 496 821 | 4 647 529 | 4 647 529 |
| Kazakhstan | KZT | 240.2 (192.3–296) | 28 393 | 10 268 | 8682 | 4 373 284 | 6 814 272 | 2 464 357 | 2 083 599 | 1 049 588 067 |
| Kyrgyzstan | KGS | 128.5 (90.1–179.8) | 5548 | 1339 | 1132 | 113 334 | 710 122 | 171 393 | 144 911 | 14 506 805 |
| Latvia | EUR | 142.9 (102–194.8) | 34 887 | 21 001 | 17 756 | 17 756 | 4 953 944 | 2 982 144 | 2 521 384 | 2 521 384 |
| Lithuania | EUR | 118.7 (82.3–164) | 43 866 | 23 759 | 20 088 | 20 088 | 5 176 246 | 2 803 555 | 2 370 389 | 2 370 389 |
| Luxembourg | EUR | 311.6 (208.1–448.1) | 132 583 | 134 713 | 113 899 | 113 899 | 41 233 169 | 41 895 685 | 35 422 549 | 35 422 549 |
| Macedonia | MKD | 286.2 (202.5–390.1) | 18 006 | 6573 | 5558 | 342 511 | 5 149 786 | 1 879 961 | 1 589 496 | 97 958 192 |
| Malta | EUR | 293.1 (195.3–419.4) | 50 230 | 34 406 | 29 090 | 29 090 | 14 717 393 | 10 080 814 | 8 523 268 | 8 523 268 |
| Moldova | MDL | 108.8 (73.1–152.3) | 15 572 | 5293 | 4475 | 93 575 | 1 681 737 | 571 597 | 483 282 | 10 106 120 |
| Monaco | EUR | 240.1 (154.9–348.9) | 238 509 | 234 317 | 198 114 | 198 114 | 57 242 067 | 56 236 100 | 47 547 284 | 47 547 284 |
| Montenegro | EUR | 136.7 (88.2–197.1) | 23 111 | 9335 | 7893 | 7893 | 3 143 127 | 1 269 576 | 1 073 418 | 1 073 418 |
| Netherlands | EUR | 276.6 (180.5–396.8) | 63 562 | 57 898 | 48 953 | 48 953 | 17 543 085 | 15 979 966 | 13 510 965 | 13 510 965 |
| Norway | NOK | 259.3 (176.2–365.5) | 82 124 | 90 940 | 76 890 | 781 178 | 21 270 165 | 23 553 569 | 19 914 400 | 202 325 157 |
| Poland | PLN | 368.2 (249.6–525) | 38 148 | 18 006 | 15 224 | 69 538 | 14 038 408 | 6 626 198 | 5 602 411 | 25 589 828 |
| Portugal | EUR | 419 (279.9–598.5) | 36 734 | 24 663 | 20 852 | 20 852 | 15 391 422 | 10 333 680 | 8 737 064 | 8 737 064 |
| Romania | RON | 185.6 (125.9–258.5) | 36 114 | 14 864 | 12 567 | 61 839 | 6 681 035 | 2 749 766 | 2 324 911 | 11 440 127 |
| Russia | RUB | 107.7 (76.2–148.4) | 34 222 | 12 659 | 10 703 | 932 408 | 3 661 751 | 1 354 539 | 1 145 254 | 99 767 678 |
| San Marino | EUR | 247.5 (163–356.4) | 67 831 | 51 809 | 43 805 | 43 805 | 16 754 356 | 12 796 916 | 10 819 715 | 10 819 715 |
| Serbia | RSD | 186.1 (140.9–242.9) | 21 525 | 9180 | 7762 | 912 472 | 4 003 580 | 1 707 514 | 1 443 693 | 169 719 864 |
| Slovakia | EUR | 124.2 (84.4–174.4) | 34 449 | 21 733 | 18 375 | 18 375 | 4 271 725 | 2 694 874 | 2 278 499 | 2 278 499 |
| Slovenia | EUR | 218.7 (144.6–313.2) | 43 944 | 29 279 | 24 755 | 24 755 | 9 579 871 | 6 382 812 | 5 396 629 | 5 396 629 |
| Spain | EUR | 202.6 (142.5–278.8) | 40 666 | 30 114 | 25 462 | 25 462 | 8 214 595 | 6 083 099 | 5 143 224 | 5 143 224 |
| Sweden | SEK | 316.8 (212.7–453.5) | 60 323 | 61 358 | 51 878 | 526 244 | 19 062 098 | 19 389 233 | 16 393 480 | 166 293 052 |
| Switzerland | CHF | 238.3 (158.3–339.7) | 76 251 | 92 343 | 78 075 | 84 387 | 18 147 637 | 21 977 593 | 18 581 922 | 20 084 136 |
| Tajikistan | TJS | 150.9 (109.8–198.8) | 4382 | 917 | 775 | 10 367 | 657 306 | 137 504 | 116 259 | 1 555 005 |
| Türkiye | TRY | 244.7 (175.7–332) | 30 452 | 9661 | 8169 | 85 506 | 7 430 312 | 2 357 340 | 1 993 116 | 20 863 416 |
| Turkmenistan | TMT | 115 (83.3–156) | 17 219 | 10 150 | 8581 | 35 523 | 1 980 216 | 1 167 196 | 986 857 | 4 085 186 |
| Ukraine | UAH | 101.9 (65.7–147.2) | 13 516 | 4596 | 3886 | 125 417 | 1 365 147 | 464 231 | 392 505 | 12 667 102 |
| United Kingdom | GBP | 429.9 (293.4–600.2) | 49 818 | 46 410 | 39 240 | 33 743 | 21 371 750 | 19 909 987 | 16 833 774 | 14 475 855 |
| Uzbekistan | UZS | 213 (160.5–279.2) | 8705 | 2031 | 1718 | 21 553 886 | 1 854 229 | 432 699 | 365 844 | 4 590 977 788 |
| WHO European Region | NA | 211.7 (148.1–288.8) | 39 501 | 27 553 | 23 296 | NA | 8 334 703 | 5 813 713 | 4 915 459 | NA |
NCU: National Currency Unit; Int$: International Dollars; USD: United States Dollars; EUR: Euro; ALL: Albanian Lek; AMD: Armenian Dram; AZN: Azerbaijan Manat; BYN: Belarusian Ruble; BAM: Bosnia-Herzegovina Convertible Mark; BGN: Bulgarian Lev; CZK: Czech Koruna; DKK: Danish Krone; GEL: Georgian Lari; HUF: Hungarian Forint; ISK: Icelandic Krona; ILS: Israeli New Shekel; KZT: Kazakhstani Tenge; MKD: Macedonian Denar; MDL: Moldovan Leu; NOK: Norwegian Krone; PLN: Polish Złoty; RON: Romanian Leu; RUB: Russian Ruble; RSD: Serbian Dinar; SEK: Swedish Krona; CHF: Swiss Franc; TJS: Tajikistani Somoni; TRY: Turkish Lira; TMT: Turkmenistani Manat; UAH: Ukrainian Hryvnia; GBP: Great British Pound; UZS: Uzbekistani Som; NA: not applicable. #: an example of how to use these societal cost estimates is to state that WHO Europe should seek to invest USD 5 813 713 to eradicate the 211.7 (95% CI 148.1–288.8) DALY burden associated with asthma per 100 000 people (i.e. USD 58.14 per person). Other examples based on Austria and Türkiye are provided in the main article; ¶: data on GDP per capita are converted to a common currency (i.e. international dollars, Int$) using a purchasing power parity (PPP) adjustment (Int$2021). Relative to converting all currencies to a common currency using an exchange rate (e.g. USD), PPP-adjusted figures are more stable over time and allow a better comparison between countries; +: although Greenland is reported separately to Denmark in table 2, our GDP per capita data source does not provide estimates for Greenland, thus Greenland is not reported separately here.
FIGURE 1.
Evidence map for conditions not covered by the Global Burden of Disease Study. RUS: Russia; NR: not reported; KG: Kyrgyzstan; IRL: Ireland; GBR: United Kingdom of Great Britain and Northern Ireland; AHI: apnoea–hypopnoea index; ARM: Armenia; CHE: Switzerland; ISL: Iceland; LVA: Latvia; DEU: Germany; ITA: Italy; ESP: Spain; LTU: Lithuania; MKD: Macedonia; ROU: Romania. #: data were reported as ratios. The upper ratio was reported as being between 1:5200 and 1:6500 and the lower as <1:7000. Reported for Czech Republic, Poland, Slovakia, Denmark, Netherlands; ¶: data were reported as rate per 10 000, and converted to rate per 100 000; +: data were also reported for AHI ≥15 events·h−1; §: reported for Andorra, Greenland and Iceland; ƒ: reported for Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Hungary, Macedonia, Montenegro, Poland, Romania, Serbia, Slovakia, Slovenia, Belarus, Estonia, Latvia, Lithuania, Moldova, Andorra, Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Greenland, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland.
GBD data covering nine lung conditions
Lung Facts provides GBD-reported epidemiological estimates for nine lung conditions, by age and sex, by and across all 53 WHO European countries and associated regions. Table 2 provides illustrative results for asthma.
Non-GBD data covering five lung conditions
Data relating to conditions not in the GBD were systematically sought. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram relating to the process of study selection is provided in supplementary file S2. In 2022, 1622 records were identified by the bibliographic and supplementary searches. Of these, 1493 were excluded based on their title or abstract. Full texts of 129 records were obtained, with data extracted from 16 studies: two cystic fibrosis reviews [15, 16] (one non-systematic) [16], one OSA systematic review [17], one influenza GBD study [18], one A1AD systematic review [19], six bronchiectasis primary studies [20–25], and five pulmonary arterial hypertension studies (two systematic reviews, three primary studies) [26–30].
In 2024, the updated search found 199 unique records; 172 were excluded based on their title or abstract. Full texts of 27 records were obtained, with 10 [31–40] included in the mapping review. 10 additional studies were identified through citation tracking; eight were excluded, with two [41, 42] included in the mapping review.
Figure 1 provides an evidence map of the data sourced for Lung Facts for non-GBD-included conditions. Supplementary file S2 shows a PRISMA flow diagram relating to the process of study selection and reasons for study exclusions are provided in supplementary file S3.
Cystic fibrosis
No data were identified for some countries across all outcomes, with no data broken down by age or sex. One systematic review [15] estimated prevalence across 40 European countries. One nonsystematic review [16] reported incidence-ratios across 19 European countries. One 2024 study provided Iceland [35], and another whole-Europe only [34], estimates. Overall, the systematic review data were considered more consistent and included in Lung Facts.
Obstructive sleep apnoea
No data were identified for incidence or mortality. One systematic review [17] modelled worldwide country-specific (50 European-specific) prevalence rates/numbers, based on 18 countries’ data (six European: Poland, Germany, Iceland, Norway, Spain and Switzerland) including by age and sex. Four prevalence-related systematic reviews [33, 36, 38, 40] were identified in 2024, but none were superior to the previous review [17] as they included fewer countries [33, 38, 40], reported fewer outcome data [33, 36, 40] or were in specific age groups only (one in children [38]; one in people aged ≥65 years [36]).
Alpha-1 antitrypsin deficiency
There were no data on incidence or mortality. One review [19] based on a systematic search calculated expected prevalence rates/numbers for 26 countries using Z-allele frequency and autosomal recessive inheritance. Four new studies [31, 32, 41, 42] were identified in 2024 covering Denmark, Germany, Madeira and La Palma; however, previous review data were retained to maintain consistency between countries.
Bronchiectasis
There were few data across countries and outcomes. Six primary studies [20–25] found in 2022 reported incidence, prevalence and mortality data for the United Kingdom, Poland, Germany, Italy and Spain. No new studies were found in 2024.
Influenza
One GBD study found in 2022, using data from 2017, attributed influenza to a fraction of the GBD's “lower respiratory tract infections” incidence and mortality estimates. One new study [37] found in 2024 was based on data older than 2017.
Societal cost estimates
Table 3 provides illustrative asthma mean societal costs for 53 WHO Europe countries per 100 000 people. We focus on mean societal costs, with DALY 95% confidence intervals presented to indicate uncertainty impacting the societal cost estimates.
Based on two example countries chosen because they have quite different GDP per capita, but similar asthma DALY burden, ways to use these societal costs (USD) could be to state:
Türkiye should aim to invest up to USD 2 357 340 to eradicate the 244.7 (175.7–332) DALY burden associated with asthma per 100 000 people (i.e. USD 23.57 per person);
Austria should aim to invest up to USD 11 077 984 to eradicate the 206.0 (138.9–291.7) DALY burden associated with asthma per 100 000 people (i.e. USD 110.78 per person).
As these societal costs are presented as rates per 100 000 people, the main difference between countries is the DALY burden and GDP per capita. For example, while Austria has the lower asthma mean DALY burden rate compared to Türkiye (206.0 versus 244.7), Austria has the higher GDP per capita (USD 53 777 versus USD 9661), making the societal cost higher for Austria than Türkiye. This does not mean that Austria values avoiding the asthma burden more than Türkiye, rather it means that due to Austria's wealthier economy, the amount Austria could invest in lung care is estimated to be higher than the amount Türkiye could invest. In this context, the societal cost is how much a country could consider investing in lung care to tackle the condition burden within that society. Further ways to use these societal costs to inform policy-making are described by Franklin et al. [13].
Discussion
Epidemiological estimates of respiratory health need regular review as determinants (e.g. exposure patterns, sociopolitical factors) change over time and geographies [43]. Accurate data are a critical component to developing national respiratory health plans. Lung Facts' methodology allows for relatively easy comparisons within and across a large number of WHO European countries, helping national coalitions assess and monitor policy progress over time. Lung Facts is largely populated with GBD estimates, to facilitate replicability and regular updates.
Current evidence gaps
The GBD methodology is becoming the gold standard in population health data [44], used by most major administrations and organisations. Where GBD included the condition, estimates were complete across countries, estimates, ages and sexes. However, for non-GBD-included conditions, there were data gaps.
A1AD has particularly poor coverage, with prevalence based on allele frequency rather than diagnoses, though the 2024 update did identify some data on diagnoses in Germany [42] and Denmark [31]. Bronchiectasis has significant data gaps, with only six primary studies covering five countries and no new studies found in 2024 [20–25]. For cystic fibrosis and OSA, reviews identified cross-country epidemiological estimates using data from multiple sources, but not for mortality (both conditions) or incidence (OSA). The studies used different methodologies; thus, estimates may not be interchangeable nor comparable with GBD estimates. GBD's influenza study, based on 2017 lower respiratory infection data, did not report prevalence, YLL nor DALYs; no update was identified and influenza is not a current GBD condition.
Filling evidence gaps
The GBD collates and analyses a huge amount of data to produce its global estimates. Harnessing GBD's high-quality methodology and expertise would be efficient to provide reasonably consistent estimation across countries and conditions. While not as comprehensive as the GBD, our research has filled evidence-gaps for some conditions (cystic fibrosis, OSA) while highlighting gaps for others (A1AD, bronchiectasis). Also, increasing GBD's current four-level condition categorisation could support more granular condition-specific estimation, e.g. influenza within lower respiratory infections, sarcoidosis within interstitial lung disease.
There are a few examples of national and international initiatives to map and monitor the epidemiology of respiratory conditions (European Community Respiratory Health Survey, www.ecrhs.org/; Respiratory Health in Northern Europe, https://breathesweden.com/rhine; the International Study of Asthma and Allergies in Childhood (ISAAC), https://isaac.auckland.ac.nz) [45, 46, 47]. For example, ISAAC [47] is a project investigating variations in the prevalence of asthma rhinitis and eczema at the population level, and their potential causes. The largest worldwide collaborative research project ever undertaken in children, it involves 306 centres in 105 countries, with nearly 2 million children participating and >1240 PubMed-indexed publications. Similar initiatives could be replicated across other respiratory conditions where data are sparse.
Collated data: strengths and limitations
Our evidence review has helped populate Lung Facts, providing policy-makers and clinicians with a one-stop resource containing important statistics to help advocate for improved respiratory health. Lung Facts has already supported care and decision-making, e.g. the Belgian Lung Foundation used Lung Facts to emphasise the need to improve COPD patient care, and set strong objectives focusing on advocacy, better prevention and improved rehabilitation access.
Limitations of our approach include the focussed search strategies: while efforts were taken to identify all relevant data (i.e. citation searching, reference checking, asking experts), some studies may have been missed. It is well-known that bibliographic searches are generally only ∼80% [6] sensitive. Our additional search techniques identified 37.5% of the studies included in 2022 (an excess over comprehensive searches of 17.5%), and 17% in the 2024 update. However, only systematic reviews were included for some conditions, thus primary studies published since the most recent systematic review will have been missed.
Societal costs: strengths and limitations
Our GDP-based approach for generating societal cost estimates is based on theory because there are no defined or empirically-based monetary DALY values, nor sufficient direct and indirect lung health cost estimates, for all WHO European countries. Despite this, decision-makers regularly need to prioritise resource allocation within countries, across conditions and associated population groups. As such, understanding the monetary cost of lung conditions is vital, but evidence is lacking.
The strengths and limitations of this GDP-based approach are described in detail elsewhere [13]. A key point is that monetised DALYs based on GDP are by intention and design a simplistic approach to valuing DALYs. The simplicity of the approach also aids its usefulness, if decision-makers are willing to utilise the valuation as a reference point [13]. For example, Drake [48] suggests a monetary DALY value, even with limitations and flaws, could be justified as it could “radically improve transparency and efficiency of priority setting in global health”, providing examples including the Millennium Development Goals and absolute poverty threshold.
Internationally, monetary valuations of the QALY for health technology assessment cost-effectiveness thresholds are also not evidence-based; rather they are decided by decision-makers as “approval norms” to support and rationalise resource allocation and care price negotiation within a fixed budget [13, 49]. The logic and necessity are similar to the GDP-based approach, but our DALY valuation is based on an economic activity metric (GDP per capita) rather than the QALY's “expert opinion” valuation [13].
The monetised DALY figures in Lung Facts do not replace the need for rigorously estimated economic data (e.g. understanding the care costs for conditions), but the benchmarked figures can be used as a reference point for investment to alleviate condition burden, even if the underlying model for monetising DALYs has flaws [13].
Conclusion
There are evidence gaps for several respiratory conditions that have an epidemiological and economic burden on people, their families/carers, and society. Researchers, including the GBD study, should extend their scope to additional respiratory conditions, including A1AD, cystic fibrosis, OSA and bronchiectasis. New evidence is needed to benefit more people burdened by poor lung health internationally. Lung Facts provides epidemiological and economic data for WHO European countries for a range of lung conditions, to support clinicians and policy-makers in advocating for lung health and in setting up respiratory plans.
Acknowledgements
We would like to acknowledge the support of the wider Lung Facts steering group (Barbara Hoffman (Centre for Health and Society, Heinrich Heine University, Düsseldorf, Germany), Zorana Jovanovic Andersen (Department of Public Health, University of Copenhagen, Copenhagen, Demark) and Brian Ward (employee of the European Respiratory Society)) and the wider International Respiratory Coalition Steering Committee.
Footnotes
Provenance: Submitted article, peer reviewed.
Conflicts of interest: J.B. Soriano has received pharmaceutical company grants from Chiesi, GSK, Linde and Novartis via Hospital Universitario de La Princesa; and has participated in speaking activities, advisory committees and consultancies sponsored by Air Liquide, Almirall, AstraZeneca, Boehringer Ingelheim, CHEST, Chiesi, CNPT, ERS, FTH, Gebro, Grifols, GSK, IHME, Laminar Pharma, Linde, Lipopharma, Menarini, Mundipharma, Novartis, OMS/WHO, Pfizer, ResApp, RiRL, ROVI, SEPAR, SAPIO, Seqirus, WHO EUR, Takeda and Zambon. A. Johannessen is the Head of ERS Assembly 6 (Epidemiology and Environment) for 2023–2026. A. Auer is an employee of the European Respiratory Society (ERS). P. Powell is an employee of and L. Anderson is contracted by the European Lung Foundation, which was founded and is partly funded by the ERS, and is part of the International Respiratory Coalition. G. Joos has received personal payments for work unrelated to this project from AstraZeneca, Chiesi, GlaxoSmithKline and Sanofi, and payments to his institution from AstraZeneca and Diadyc Intl – CR20; and has unpaid roles with the International Respiratory Coalition (Chair) and as a GINA advocate. All other authors declare that they have no conflicts of interest in relation to this work.
Support statement: This work was funded by the International Respiratory Coalition. Funding information for this article has been deposited with the Open Funder Registry.
Supplementary material
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Search strategoes and data extraction
01351-2025.SUPPLEMENT
PRISMA flow diagram
01351-2025.SUPPLEMENT2
Studies excluded from both reviews
01351-2025.SUPPLEMENT3
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Supplementary Materials
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Search strategoes and data extraction
01351-2025.SUPPLEMENT
PRISMA flow diagram
01351-2025.SUPPLEMENT2
Studies excluded from both reviews
01351-2025.SUPPLEMENT3