Global, regional, and national burden of chronic respiratory diseases,1990–2021 and predictions to 2035: analysis of data from the global burden of disease study 2021

Abstract

Background

Chronic respiratory diseases (CRDs) have undergone significant epidemiological shifts in recent decades. This study comprehensively analyses the evolving global and regional burden of CRDs over the past 30 years and their attributable risk factors, aiming to identify key trends and inform effective prevention and control strategies.

Methods

Using data from the Global Burden of Disease (GBD) 2021 database, this study examines trends in age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized disability-adjusted life years rate (ASDR) for CRDs from 1990 to 2021, across global, regional, and national levels. Estimated annual percentage change (EAPC) was employed to quantify ASR trends over specified intervals. Additionally, Bayesian age-period-cohort (BAPC) modeling was utilized to depict and project CRD trends through 2035.

Results

In 2021, the global incidence of CRDs was 55.21 million (95% UI 48.68–64.56), predominantly comprising asthma (37.86 million; 95% UI 31.38–46.92) and chronic obstructive pulmonary disease (COPD) (16.90 million; 95% UI 15.47–18.34),  followed by interstitial lung disease (ILD) and pulmonary sarcoidosis (0.39 million; 95% UI 0.35–0.43) and pneumoconiosis (0.06 million; 95% UI 0.05–0.07). Since 1990, the age-standardized prevalence (ASPR) of CRDs has generally declined, except for ILD and pulmonary sarcoidosis, which increased by 8.74%. The global ASIR of CRDs is projected to continue declining, reaching 517.25 per 100,000 population by 2035, with ASMR of 39.21 per 100,000. Risk factor studies have shown that solid fuels burden remained higher among females in low-middle sociodemographic index (SDI) areas, while smoking-attributable burden predominated among males.

Conclusion

This study characterizes the CRD burden across regions, ages, and sexes, identifying key risk factors to inform burden–reduction policies.

Introduction

Chronic respiratory disease (CRD) is an encompassing term that describes a range of diseases affecting the lungs and airways, including COPD, asthma, ILD and Pulmonary Sarcoidosis. These diseases are major contributors to the growing global burden of non-communicable diseases (NCDs) and are among the key causes of high morbidity and mortality worldwide. CRDs are associated with significant health burdens and economic costs, and pose a major challenge to global public health [1–3]. With a growing and aging global population [4], understanding the cumulative burden of these conditions is increasingly critical.

As the leading global health authority, the World Health Organization (WHO) plays a central role in coordinating and implementing the health-related Sustainable Development Goals (SDGs) [5]. WHO has developed multiple global action plans to promote health and well-being worldwide [6,7]. In the area of CRDs, WHO established the Global Alliance against Chronic Respiratory Diseases (GARD) [8], whose efforts align with WHO’s broader strategies, including the Global Action Plan for the Prevention and Control of Non-Communicable Diseases 2013–2030 [9], aimed at achieving the SDGs’ health targets. Together, WHO and GARD strive to create a healthier, more equitable world, supporting the right to clean air and reducing the global burden of CRDs.

The GBD study provides a valuable framework for assessing the health impacts of a wide range of diseases and injuries [10,11]. By analyzing data from the GBD database, researchers and policymakers can obtain comprehensive information on disease prevalence, morbidity, mortality, and disability-adjusted life years (DALYs) at global, regional, and national levels [12,13]. Although the impact of CRDs on global health is well acknowledged, the latest report on the global prevalence and attributable health burden of CRDs relies on GBD 2019 data [14,15]. The GBD 2021 dataset encompasses 204 countries and territories, incorporating relational life tables for age-specific mortality calibration and ensemble models quantifying COVID-19 excess mortality with 1,000-sample uncertainty intervals. Enhanced spatial resolution (811 subnational units) and refined under-5 age stratification improve epidemiological surveillance. Integrated Bayesian cohort analysis of 7,000+ environmental exposures strengthens preventive policy evidence [10,16,17].

Detailed age- and sex-specific quantification of the burden and patterns of CRDs, along with projections of future trends, are essential to identify the current and anticipated health needs of populations. This study assessed the global, regional, and national burden of CRDs and their attributable risk factors by sex, age, and SDI between 1990 and 2021, while employing the Bayesian age–period–cohort (BAPC) model to project prevalence trends through 2035 [18,19].

Method

Overview

The GBD data can be accessed online through the GHDx portal (https://vizhub.healthdata.org/gbd-results/), a comprehensive database for retrieving and analyzing health-related data and the primary data source for this study. GBD 2021 provides an extensive epidemiological analysis, assessing the burden of 371 diseases and injuries and 88 risk factors across 204 countries and territories [16]. Additionally, the University of Washington Institutional Review Board waived the informed consent requirement for access to GBD data [11]. This study followed guidelines for accurate and transparent health assessments (https://www.who.int/publications/m/item/gather-checklist), ensuring the reliability and transparency of results.

Case definition

All estimates in this study were stratified by sex, age, location, and country, covering incidence, prevalence, mortality, DALYs, and corresponding age-standardized rate (ASRs) for CRDs from 1990 to 2021. CRD cases were identified using International Classification of Diseases, 10th Revision (ICD-10) codes (e.g. D86–D86.2, D86.9, G47.3–G47.39, J07–J08, J18.7, J19, J23–J35.9, J37–J68.9, J70.8–J84.9, J85.9, J87–J91, J91.8–J94.9, J96–J99.8, R05.0–R06.9, R08–R09.89, R84–R84.9, R91–R91.8).

According to GBD 2021, CRDs are categorized into five main groups: asthma, COPD, ILD and tuberculosis, pneumoconiosis (including silicosis, asbestosis, coal workers’ pneumoconiosis, and other forms), and other CRDs. The ‘other chronic respiratory diseases’ category includes conditions such as obstructive sleep apnea, vasculitis, allergic rhinitis, chronic rhinitis, nasopharyngitis, chronic sinusitis, nasal polyposis, diseases of the nose and sinuses (n.e.c.), chronic tonsillar and adenoidal diseases, chronic laryngitis and laryngotracheitis, vocal cord and larynx diseases (n.e.c.), other upper respiratory tract diseases, respiratory diseases caused by organic dusts or inhalation of chemicals, eosinophilia (n.o.c.), pleural effusions, pleural pemphigoid with asbestos, and pleural plaques without asbestos.

Incidence, prevalence, mortality and DALYs estimates

The GBD study is a comprehensive international research program that offers an integrated framework to assess global and regional health trends, supporting evidence-based policymaking and resource allocation. Over 11,500 collaborators from 164 countries and territories contribute to GBD indicators by collecting, reviewing, and analyzing data. Sources include epidemiological surveys, hospital records, vital registration systems, disease surveillance systems, and additional academic and policy reports. (https://ghdx.healthdata.org/gbd-2021/sources).

The GBD 2021 study utilized advanced modeling techniques to estimate the burden of CRDs. Incidence and prevalence rates were calculated using DisMod-MR 2.1, a Bayesian meta-regression tool that adjusts for differences in measurement methods and case definitions. Bayesian geospatial software integrated disease parameters, epidemiologic relationships, and geospatial data to enhance estimate reliability. Mortality estimation was performed using the Comprehensive Cause of Death Modeling (CODEm) framework, which integrates vital registration systems with inferred cause of death data covering uncoded cases. Accuracy was ensured through rigorous data adjustment prior to analysis [12,20], and CODEm improves estimation accuracy by integrating multiple models and applying them to the 2021 database to synthesize variation across data sources to achieve an ordered estimate of the burden of CRD.

The primary health indicator used was DALYs, calculated to capture the impact of premature mortality and disability. DALYs consist of years of life lost (YLLs) due to premature death and years lived with disability (YLDs) due to disease.

In analyzing all age groups, this study employed ASRs per 100,000 population, including ASIR, ASMR, and ASDR. This approach minimizes the impact of varying age structures, allowing for a more accurate comparison of CRD prevalence across populations. All relevant data are accessible online through the GBD Results Tool(http://ghdx.healthdata.org/gbd-results-tool).

Risk estimations

This study analyzed not only traditional epidemiological indicators of CRDs—such as prevalence, morbidity, mortality, and DALYs—but also examined the impact of specific risk factors. Focusing on major risk factors identified in the GBD 2021 study, we assessed three key risk factors for asthma (high body mass index [BMI], cigarette smoking, and occupational exposures), six for COPD (cigarette smoking, secondhand smoke, household air pollution, ambient particulate matter [AMM], ozone, and occupational particulate matter [OPM], including coal dust), and three for pneumoconiosis (occupational particulate matter, silica, and asbestos). Definitions and selection criteria for these risk factors, well-documented in previous studies, are widely recognized [14,15]. Additionally, we analyzed disability and mortality attributable to these risk factors across 5-year age groups to reveal sex- and age-specific differences in impact.

We employed advanced statistical methods, including DisMod-MR 2.1 and spatio-temporal Gaussian process regression, to accurately model exposure distributions for each risk factor across demographic groups and geographic locations. Using available epidemiological evidence, we defined a theoretical minimum risk exposure level (TMREL) for each risk factor, representing the optimal level to minimize CRD risk. By integrating exposure data, relative risk estimates, and TMRELs, we calculated population attributable fractions (PAFs) for each risk factor, disaggregated by location, age, sex, and year. To quantify these PAFs in terms of specific health outcomes, we multiplied them by DALYs, yielding an estimate of the risk-attributable burden. This calculation provides valuable insights into the potential impact of modifying these risk factors on CRD outcomes in different populations [21]. This comprehensive approach highlights not only the direct burden of CRDs but also the influence of modifiable risk factors, offering a deeper understanding of disease impact and intervention opportunities.

Sociodemographic index

Sociodemographic development has driven substantial health gains over the past 30 years, with the GBD study tracking health burden changes across regions. SDI, a composite indicator strongly correlated with health outcomes, measures a country or region’s development level. SDI is calculated as the geometric mean of three indicators: total fertility rate under age 25 (TFU25), average years of schooling for those aged 15 and older (EDU15+), and per capita lagged-distributed income (LDI). Ranging from 0 to 1, SDI scores reflect a continuum from minimum to maximum theoretical health-related development. In this study, we categorized countries and regions into five SDI groups (low, low-middle, middle, middle-high, and high) and analyzed the health burden across these categories.

Statistical analyses

Our data analysis began by examining the dataset structure and calculating counts and rate for key indicators—prevalence, morbidity, mortality, and DALYs for CRDs—at the global, regional, and national levels. We then analyzed trends in these indicators across regions from 1990 to 2021. To quantify relative changes, we applied the formula: Relative change (%) = [(2021 value–1990 value)/1990 value] × 100%. This calculation was used for cases per 100,000 and for the age-standardized rate (ASR), a critical metric that adjusts for age differences across populations, allowing for fair comparisons of disease incidence over time and among regions [22,23]. The ASR formula is as follows:

$$\mathit{\text{ASR}}=\frac{{\sum }_{i=1}^A{a}_i{w}_i}{{\sum }_{i=1}^A{w}_i}\times 100,000$$

(ai, where i denotes age i) and the sum of the number of people in the same age group i in the selected reference standard population (or weight wi) divided by the standard population weight.

The EAPC is also an important indicator for assessing trends in CRD [24]. We use the EAPC to characterize trends in ASR over a given time interval. The natural logarithm of ASR is linearly related to time. The EAPC was estimated by a linear regression model:

$$y=\alpha +\mathit{\text{βx}}+\epsilon$$

where y is ln(ASR), x is the calendar year, and ε is the error term. The EAPC was calculated as

$$\mathit{\text{EAPC}}=(e\beta -1)\times 100\%$$

with 95% confidence intervals (CIs) derived from the linear regression model. An upward trend in ASR is indicated when both the estimated EAPC and its lower 95% CI are greater than 0. Conversely, a decreasing trend is observed when both the estimated EAPC and the upper limit of its 95% CI are below 0.

To further analyze and predict trends in CRDs up to 2035, we utilized a BAPC model. Previous studies have demonstrated that the BAPC model outperforms other prediction methods in terms of coverage and accuracy [23,25]. This advanced model integrates historical data with probability distributions, enabling precise future estimates of CRD trends by comprehensively accounting for age, period, and cohort effects. Data visualization and statistical analyses were conducted using R software (version 4.2.3) and the JD_GBDR package (V2.22, Jingding Medical Technology Co., Ltd.).

Result

Global incidence, prevalence, mortality and DALYs

In 2021, the global ASIR, ASPR, ASMR and ASDR of CRDs exhibited distinct characteristics (Figure 1). Among them, asthma is the most common disease. CRDs was approximately 55.21 million cases (95% UI 48.68–64.56), with asthma (37.86 million, 95% UI 31.38–46.92) being the most prevalent, followed by COPD (16.90 million, 95% UI 15.47–18.34), ILD and Pulmonary Sarcoidosis (0.39 million, 95% UI 0.35–0.43), and pneumoconiosis (0.06 million, 95%UI 0.05–0.07) (Table 1).

Figure 1.

Global burden of disease for chronic respiratory diseases in 204 countries and areas: (A) age-standardized disability-adjusted life years rate (ASDR), (B) age-standardized mortality rate (ASMR), (C) age-standardized incidence rate (ASIR), (D) age-standardized prevalence rate (ASPR).

Table 1.

Global incidence, prevalence, mortality and DALYs of chronic respiratory diseases from 1990 to 2021.

Year	CRD	Asthma	COPD	ILD and PS	Other CRD	Pneumoconiosis
1990
Deaths (×105, 95% UI)	29.91(26.97,32.00)	3.74(3.05,4.93)	24.96(22.39,26.95)	0.55(0.45,0.68)	0.49(0.44,0.56)	0.17(0.16,0.19)
DALYs (×105, 95% UI)	848.91(769.21,922.51)	228.62(183.07,287.69)	568.57(512.94,614.12)	15.01(12.21,18.51)	32.09(28.13,35.96)	4.62(4.06,5.17)
Prevalence (×105, 95% UI)	3812.30(3412.27,4281.44)	2873.01(2503.70,3310.81)	1005.45(912.13,1106.08)	18.87(16.09,22.06)	–	2.25(18.18,27.31)
Incidence (×105, 95% UI)	498.10(428.01,603.15)	415.55(341.58,517.05)	80.54(74.19,87.06)	1.57(1.36,1.79)	–	0.42(0.35,0.48)
ASIR	944.21(823.93,1120.03)	736.99(615.03,905.17)	202.42(186.70,218.25)	3.76(3.26,4.27)	–	1.02(0.87,1.18)
ASPR	7936.43(7224.91,8809.06)	5568.25(4899.64,6349.80)	2550.01(2318.34,2806.32)	45.99(39.41,53.78)	–	5.42(4.43,6.54)
ASMR	84.55(76.19,90.45)	9.63(7.76,12.79)	71.91(64.47,77.53)	1.51(1.25,1.86)	1.01(0.91,1.16)	0.46(0.41,0.51)
ASDR	2075.20(1893.96,2237.61)	476.48(386.56,587.84)	1492.63(1342.46,1609.29)	37.148(30.61,45.36)	57.57(51.07,64.49)	11.35(10.05,12.69)
2021
Deaths (×105, 95% UI)	44.14(40.05,48.66)	4.36(35.77,5.55)	37.19(33.47,40.84)	1.88(1.61,2.12)	0.51(0.40,0.64)	0.18(0.16,0.20)
DALYs (×105, 95% UI)	1085.03(1003.51,1180.43)	214.22(169.56,268.87)	797.79(740.26,860.11)	40.42(34.89,45.16)	28.11(23.52,34.03)	4.47(3.92,5.13)
Prevalence (×105, 95% UI)	4682.66(4289.32,5131.11)	2604.79(2272.09,2979.67)	2133.87(1948.68,2339.75)	43.06(38.02,48.98)	–	3.96(3.28,4.72)
Incidence (×105, 95% UI)	552.12(486.78,645.61)	378.64(313.81,469.19)	168.95(154.71,183.35)	3.90(3.46,4.33)	–	0.62(0.54,0.71)
ASIR	719.35(627.50,854.13)	516.69(425.35,646.13)	197.37(181.64,213.41)	4.54(4.05,5.03)	–	0.73(0.63,0.83)
ASPR	5785.36(5269.67,6371.87)	3340.12(2905.23,3832.24)	2512.86(2293.92,2748.51)	50.01(44.23,56.77)	–	4.62(3.85,5.51)
ASMR	53.56(48.45,59.09)	5.19(4.26,6.59)	45.22(40.61,49.69)	2.28(1.95,2.56)	0.64(0.50,0.80)	0.21(0.19,0.24)
ASDR	1294.59(1196.60,1412.08)	264.62(208.31,333.43)	940.65(871.48,1014.59)	47.61(41.25,53.16)	36.48(30.40,44.57)	5.21(4.58,5.98)
1990–2021 EAPC
ASIR (95% CI)	−0.79(–0.90, –0.68)	−1.03(–1.18, –0.88)	−0.10(–0.12, –0.08)	0.72(0.62,0.82)	–	−1.14(–1.18, –1.10)
ASPR (95% CI)	−0.96(–1.06, –0.87)	−1.59(–1.75, –1.42)	−0.04(–0.08, –0.01)	0.36(0.28,0.45)	–	−0.48(–0.55, –0.41)
ASMR (95% CI)	−1.68(–1.77, –1.60)	−2.03(–2.09, –1.97)	−1.75(–1.84, –1.65)	1.55(1.41,1.70)	−1.56(–1.77, –1.35)	−2.55(–2.64, –2.47)
ASDR (95% CI)	−1.68(–1.74, –1.62)	−1.91(–1.98, –1.83)	−1.71(–1.79, –1.63)	0.95(0.85,1.05)	−1.47(–1.60, –1.34)	−2.63(–2.69, –2.57)

CRD, Chronic respiratory diseases; COPD, chronic obstructive pulmonary disease; ILD, Interstitial lung disease; PS, Pulmonary Sarcoidosis DALYs, disability-adjusted life-years; ASIR, age-standardized incidence rate; ASPR, age-standardized prevalence rate; ASMR, age-standardized mortality rate; ASDR, age-standardized DALYs rate; EAPC, estimated annual percentage change; CI, confidence interval; UI, uncertainty intervals.

From 1990 to 2021, asthma consistently displayed the highest age-standardized prevalence among CRDs, followed by COPD, ILD, and pulmonary sarcoidosis, as well as pneumoconiosis. The ASIR for CRDs indicated a declining trend (Figure 2), with the exception of ILD and pulmonary sarcoidosis. The EAPC of ASIR was 0.72 (95% CI: 0.62, 0.82) (Table 1). Overall, the ASIR of CRDs decreased from 1990 to 2021, with a global EAPC of approximately −0.79 (95% CI: −0.90, −0.68).

Figure 2.

Temporal trends in age-standardized incidence rates (ASIR) of chronic respiratory diseases by disease group, 1990–2021.

Compared to 1990, the age-standardized prevalence of CRDs exhibited a decreasing trend (Figure S1), with the exception of ILD and pulmonary sarcoidosis, which exhibited an increase of 8.74%. Among these diseases, asthma demonstrated the most significant decline, with an EAPC of −1.59 (95% CI: −1.75, −1.42) from 1990 to 2021. By 2021, CRDs were most prevalent in the high-income super-region of North America, with the United States ranking first globally, reporting an ASPR of 13,315.98 per 100,000 population (Table 1).

COPD has been the leading cause of death from CRDs worldwide. In 2021, the global number of deaths attributable to CRDs was approximately 4.41 million (95%UI 40.05 × 10⁵,48.66 × 10⁵), with an ASMR of 53.56 per 100,000 people (95% UI 48.45–59.09). Between 1990 and 2021, the global ASMR for COPD exhibited a decreasing trend with an EAPC of −1.75 (95% CI: −1.84 to −1.65).

By 2021, the ASDR for CRD had declined significantly from 1990, from 2075.20 (95% UI 1893.96,2237.61) per 100,000 to 1294.59 (95% UI 1196.60,1412.08) per 100,000. The EAPC analysis demonstrated an increasing trend in ASDR for ILD and pulmonary sarcoidosis [EAPC = 0.95 (95% CI: 0.85–1.05)], while asthma [EAPC = −1.91(95% CI: −1.98, −1.83)], COPD [EAPC = −1.71 (95% CI: −1.79 to −1.63)], pneumoconiosis [EAPC = −2.63 (95% CI: −2.69 to −2.57)], and other CRDs [EAPC = −1.47 (95% CI: −1.60 to −1.34)] exhibited declining ASDR trends. And, for all CRDs, the trend of ASDR was consistent with ASMR (Figure S2).

Regional incidence, prevalence, mortality and DALYs

In both 1900 and 2021, the highest ASIR for CRD was observed in high-income North America. Over the past three decades, ASIRs for CRDs have declined across 21 regions worldwide. The regions with the most significant reductions in ASIR since 1990 include high-income Asia Pacific, Andean Latin America, and Tropical Latin America (Figure S3).

The ASPR for CRDs has exhibited trends similar to those of the ASIR in Region 21. While the global prevalence of asthma remained high for nearly three decades, the prevalence of COPD rose dramatically in southern sub-Saharan Africa and East Asia by 2021. By 2021, COPD had surpassed asthma in terms of ASPR in these regions, accounting for 52.6% and 54.5% of CRDs, respectively (Figure 3).

Figure 3.

Percentage of causes of age-standardized prevalence rate (ASPR) in chronic respiratory diseases: a comparison of 1990 and 2021. A: All chronic respiratory diseases ASPR in 1990 as a percentage of 21 districts. B: All chronic respiratory diseases ASPR in 2021 as a percentage of 21 districts.

Among these diseases, of particular concern to us is the increasing trend in ASMR for ILD and Pulmonary Sarcoidosis, especially in the High-income Asia pacific region (From 10.9% of all CRDs in 1990 to 35.9% in 2021) and the Andean Latin America region (From 21.5% of all CRDs in 1990 to 42.8% in 2021) (Figure S4). ASMRs for CRDs showed closer alignment with overall population patterns in males compared to females across 21 global regions in 2021 (Figure 4).

Figure 4.

Age-standardized mortality rate (ASMR) rankings for all chronic respiratory diseases in region 21 in 2021. A: Both sexes combined B: females C: males.

In 1990, Oceania, East Asia, and South Asia exhibited the three highest ASDR for CRDs among 21 global regions. By 2021, South Asia and Oceania remained the top two regions, while East Asia demonstrated a substantial decline compared with 1990 levels. Compared with 1990, the ASDRs for CRDs declined across all 21 global regions in 2021 (Figure 5).

Figure 5.

Age-standardized DALYs rates of chronic respiratory diseases in region 21: a comparison between 1990 and 2021.

National incidence, prevalence, mortality and DALYs

In 2021, the ASIR of CRD displayed significant geographic disparities globally. According to our data, Haiti, the United States, and Poland ranked among the top three countries for ASIR (Table S1). Among the 204 countries and territories analyzed (Table 2), Japan (EAPC = −2.84, 95% CI: −3.05 to −2.63), Singapore (EAPC = −2.36, 95% CI: −2.59 to −2.13), and New Zealand (EAPC = −2.32, 95% CI: −2.58 to −2.06) exhibited the most significant decreases in ASPR for CRDs. In terms of ASMR for CRDs, the Republic of Belarus (EAPC = −6.14, 95% CI: −6.68 to −5.60), Singapore (EAPC = −5.47, 95% CI: −5.70 to −5.23), and Ukraine (EAPC = −5.05, 95% CI: −5.65 to −4.45) had the most notable reductions. Furthermore, Ukraine (EAPC = −4.96, 95% CI: −5.36 to −4.56), Belarus (EAPC = −4.83, 95% CI: −5.24 to −4.42), and Singapore (EAPC = −4.55, 95% CI: −4.74 to −4.35) demonstrated significant decreases in ASDR for CRDs.

Table 2.

EAPCs For incidence, prevalence, mortality, and disability-adjusted life years (DALYs) for chronic respiratory diseases in 204 countries, 1990–2021.

		EAPCs
Location	ASIR	ASPR	ASDR	APMR
American Samoa	−1.09(–1.27, –0.91)	−1.07(–1.20, –0.94)	−1.71(–1.81, –1.61)	−1.69(–1.76, –1.61)
Antigua and Barbuda	−0.20(–0.36, –0.04)	−0.25(–0.44, –0.06)	−0.03(–0.15,0.09)	0.90(0.71,1.09)
Arab Republic of Egypt	−0.78(–0.85, –0.72)	−0.61(–0.67, –0.56)	−2.94(–3.23, –2.66)	−2.48(–2.67, –2.28)
Argentine Republic	−0.17(–0.23, –0.11)	−0.67(–0.71, –0.63)	−0.74(–0.88, –0.61)	0.39(0.12,0.66)
Australia	−1.26(–1.51, –1.01)	−1.58(–1.85, –1.32)	−1.65(–1.77, –1.53)	−1.36(–1.54, –1.18)
Barbados	0.47(0.33,0.60)	0.22(0.10,0.34)	−0.02(–0.15,0.10)	0.06(–0.12,0.23)
Belize	−0.35(–0.44, –0.26)	−0.31(–0.42, –0.21)	0.15(–0.16,0.46)	1.13(0.78,1.49)
Bermuda	0.07(–0.09,0.23)	−0.04(–0.24,0.15)	−0.62(–0.68, –0.55)	−1.03(–1.17, –0.89)
Bolivarian Republic of Venezuela	−1.12(–1.28, –0.96)	−1.03(–1.20, –0.86)	−0.74(–0.91, –0.57)	−0.14(–0.32,0.05)
Bosnia and Herzegovina	−0.09(–0.13, –0.06)	−0.15(–0.20, –0.11)	−1.64(–1.79, –1.49)	−2.72(–2.89, –2.55)
Brunei Darussalam	−0.41(–0.49, –0.34)	−0.95(–0.98, –0.91)	−1.86(–2.01, –1.71)	−1.60(–1.78, –1.43)
Burkina Faso	−0.22(–0.26, –0.18)	−0.26(–0.31, –0.20)	−0.77(–0.86, –0.67)	−1.00(–1.06, –0.94)
Canada	−0.27(–0.41, –0.14)	−0.47(–0.64, –0.29)	−0.70(–0.75, –0.64)	−0.24(–0.41, –0.06)
Central African Republic	−0.48(–0.54, –0.42)	−0.55(–0.60, –0.50)	−0.71(–0.76, –0.67)	−0.48(–0.55, –0.41)
Commonwealth of Dominica	−0.11(–0.28,0.06)	−0.28(–0.47, –0.09)	0.07(0.01,0.12)	0.01(–0.03, 0.05)
Commonwealth of the Bahamas	0.03(–0.16,0.21)	−0.08(–0.28, 0.13)	−0.12(–0.19, –0.05)	0.17(0.03, 0.31)
Cook Islands	−0.74(–1.01, –0.47)	−0.81(–1.05, –0.56)	−2.43(–2.54, –2.31)	−2.67(–2.79, –2.56)
Czech Republic	−0.03(–0.07,0.02)	−0.06(–0.11, –0.02)	0.11(–0.23,0.46)	−1.12(–1.72, –0.50)
Democratic People’s Republic of Korea	−0.71(–0.75, –0.66)	−0.75(–0.78, –0.71)	−1.14(–1.23, –1.04)	−0.93(–1.04, –0.82)
Democratic Republic of Sao Tome and Principe	−0.86(–0.91, –0.81)	−0.72(–0.78, –0.66)	−0.82(–0.97, –0.68)	−0.52(–0.60, –0.44)
Democratic Republic of the Congo	−0.63(–0.69, –0.57)	−0.46(–0.51, –0.41)	−0.48(–0.52, –0.44)	−0.45(–0.48, –0.42)
Democratic Republic of Timor–Leste	−1.15(–1.22, –1.09)	−1.12(–1.20, –1.04)	−1.06(–1.21, –0.90)	−0.87(–0.95, –0.79)
Democratic Socialist Republic of Sri Lanka	−0.66(–0.74, –0.59)	−0.80(–0.84, –0.75)	−1.88(–2.07, –1.70)	−1.73(–1.87, –1.59)
Dominican Republic	−0.69(–0.83, –0.54)	−0.58(–0.71, –0.45)	−1.11(–1.30, –0.91)	−1.63(–1.90, –1.35)
Eastern Republic of Uruguay	−0.45(–0.47, –0.42)	−0.83(–0.87, –0.79)	−0.51(–0.60, –0.42)	0.50(0.32,0.69)
Federal Democratic Republic of Ethiopia	−1.60(–1.72, –1.48)	−1.56(–1.64, –1.47)	−2.53(–2.65, –2.40)	−2.02(–2.16, –1.87)
Federal Democratic Republic of Nepal	−0.32(–0.34, –0.31)	−0.33(–0.36, –0.30)	−0.86(–1.08, –0.65)	−1.01(–1.18, –0.84)
Federal Republic of Germany	−0.62(–0.84, –0.41)	−1.03(–1.31, –0.75)	−0.94(–1.21, –0.67)	−1.21(–1.45, –0.97)
Federal Republic of Nigeria	−0.59(–0.65, –0.52)	−0.61(–0.67, –0.55)	−0.97(–1.00, –0.94)	−0.79(–0.90, –0.69)
Federal Republic of Somalia	−0.71(–0.75, –0.67)	−0.80(–0.85, –0.74)	−1.09(–1.16, –1.02)	−1.11(–1.15, –1.07)
Federated States of Micronesia	−1.38(–1.42, –1.34)	−1.32(–1.35, –1.29)	−1.89(–1.98, –1.80)	−1.82(–1.90, –1.75)
Federative Republic of Brazil	−1.27(–1.40, –1.15)	−1.38(–1.47, –1.28)	−1.95(–2.15, –1.75)	−1.27(–1.54, –1.00)
French Republic	−0.64(–0.74, –0.54)	−1.18(–1.32, –1.05)	−1.77(–1.97, –1.57)	−2.52(–2.75, –2.30)
Gabonese Republic	−0.73(–0.78, –0.68)	−0.81(–0.85, –0.77)	−1.80(–1.85, –1.75)	−1.74(–1.84, –1.63)
Georgia	−0.08(–0.20,0.03)	−0.21(–0.29, –0.14)	−0.18(–0.46,0.10)	−1.41(–2.05, –0.76)
Global	−0.79(–0.91, –0.68)	−0.96(–1.06, –0.87)	−1.68(–1.74, –1.62)	−1.39(–1.49, –1.29)
Grand Duchy of Luxembourg	−0.77(–0.80, –0.74)	−1.39(–1.46, –1.32)	−1.47(–1.55, –1.38)	−1.03(–1.14, –0.91)
Greenland	−1.11(–1.20, –1.02)	−1.43(–1.51, –1.35)	−2.02(–2.10, –1.93)	−1.97(–2.08, –1.85)
Grenada	−0.17(–0.24, –0.10)	−0.41(–0.48, –0.33)	−0.27(–0.46, –0.08)	0.33(0.03,0.62)
Guam	−1.35(–1.62, –1.07)	−1.25(–1.52, –0.99)	−2.10(–2.24, –1.97)	−3.69(–3.89, –3.48)
Hashemite Kingdom of Jordan	−0.33(–0.42, –0.24)	−0.49(–0.58, –0.40)	−2.05(–2.24, –1.85)	−2.48(–2.68, –2.27)
Hellenic Republic	−0.19(–0.26, –0.12)	−0.73(–0.82, –0.65)	0.13(–0.19,0.45)	−0.66(–1.32,0.01)
Hungary	−0.22(–0.36, –0.08)	−0.37(–0.45, –0.28)	−0.24(–0.56,0.08)	−1.15(–1.51, –0.80)
Independent State of Papua New Guinea	−0.99(–1.06, –0.92)	−1.06(–1.12, –1.00)	−0.67(–0.70, –0.64)	−0.55(–0.57, –0.53)
Independent State of Samoa	−0.92(–0.95, –0.88)	−0.79(–0.82, –0.76)	−1.24(–1.33, –1.16)	−1.14(–1.25, –1.04)
Ireland	−0.85(–0.90, –0.79)	−1.60(–1.69, –1.51)	−2.19(–2.35, –2.03)	−1.85(–2.07, –1.64)
Islamic Republic of Afghanistan	−1.22(–1.29, –1.16)	−1.34(–1.41, –1.26)	−1.43(–1.65, –1.21)	−0.94(–1.11, –0.77)
Islamic Republic of Iran	−1.02(–1.06, –0.97)	−1.07(–1.11, –1.02)	−1.30(–1.33, –1.26)	−1.35(–1.40, –1.31)
Islamic Republic of Mauritania	−0.57(–0.62, –0.52)	−0.88(–0.95, –0.80)	−1.80(–2.07, –1.52)	−2.41(–2.66, –2.17)
Islamic Republic of Pakistan	−0.91(–1.03, –0.79)	−0.83(–0.93, –0.74)	−0.85(–1.08, –0.61)	−0.10(–0.34,0.14)
Jamaica	−0.52(–0.62, –0.41)	−0.74(–0.87, –0.61)	−0.53(–0.81, –0.24)	0.14(–0.17,0.45)
Japan	−2.13(–2.42, –1.84)	−2.84(–3.05, –2.63)	−2.46(–2.66, –2.25)	−1.85(–2.01, –1.70)
Kingdom of Bahrain	−0.75(–0.82, –0.68)	−0.93(–1.01, –0.84)	−2.46(–2.77, –2.15)	−1.80(–2.13, –1.46)
Kingdom of Belgium	−0.59(–0.77, –0.41)	−1.27(–1.49, –1.06)	−1.68(–1.76, –1.61)	−1.30(–1.49, –1.11)
Kingdom of Bhutan	−0.79(–0.83, –0.75)	−0.89(–0.93, –0.84)	−1.49(–1.62, –1.37)	−1.02(–1.10, –0.95)
Kingdom of Cambodia	−0.81(–0.85, –0.76)	−0.69(–0.72, –0.65)	−1.09(–1.13, –1.04)	−0.70(–0.74, –0.67)
Kingdom of Denmark	−0.53(–0.60, –0.46)	−1.05(–1.21, –0.89)	−1.09(–1.28, –0.90)	0.48(0.20,0.77)
Kingdom of Eswatini	−0.61(–0.68, –0.53)	−0.77(–0.83, –0.70)	−0.47(–0.91, –0.03)	−0.40(–0.66, –0.13)
Kingdom of Lesotho	−0.30(–0.34, –0.25)	−0.11(–0.16, –0.06)	1.05(0.68,1.41)	0.62(0.38,0.85)
Kingdom of Morocco	−0.04(–0.09,0.01)	0.12(0.07,0.16)	−0.43(–0.50, –0.37)	−0.71(–0.85, –0.58)
Kingdom of Norway	−1.08(–1.18, –0.98)	−1.84(–1.92, –1.76)	−0.58(–0.72, –0.43)	1.76(1.54,1.97)
Kingdom of Saudi Arabia	0.42(0.32,0.52)	0.50(0.42,0.58)	−1.11(–1.15, –1.07)	−1.17(–1.28, –1.06)
Kingdom of Spain	0.07(–0.04,0.18)	−0.36(–0.48, –0.25)	−1.37(–1.46, –1.28)	−1.09(–1.22, –0.95)
Kingdom of Sweden	−0.61(–0.92, –0.30)	−1.29(–1.57, –1.00)	−0.76(–0.89, –0.63)	0.89(0.75,1.03)
Kingdom of Thailand	−0.91(–1.04, –0.78)	−1.16(–1.25, –1.06)	−3.39(–3.61, –3.16)	−3.22(–3.51, –2.94)
Kingdom of the Netherlands	−0.31(–0.38, –0.24)	−1.01(–1.19, –0.83)	−0.87(–0.97, –0.76)	0.02(–0.21,0.25)
Kingdom of Tonga	−0.81(–0.92, –0.71)	−0.91(–0.99, –0.82)	−0.94(–1.04, –0.83)	−1.12(–1.23, –1.01)
Kyrgyz Republic	−0.88(–0.95, –0.81)	−1.13(–1.19, –1.06)	−4.35(–4.83, –3.86)	−3.53(–3.97, –3.10)
Lao People’s Democratic Republic	−1.40(–1.45, –1.36)	−1.13(–1.18, –1.07)	−2.09(–2.18, –2.01)	−1.58(–1.69, –1.47)
Lebanese Republic	−0.16(–0.19, –0.13)	−0.24(–0.28, –0.21)	−1.08(–1.18, –0.98)	−1.38(–1.48, –1.28)
Malaysia	−0.80(–0.88, –0.71)	−1.12(–1.18, –1.07)	−2.06(–2.29, –1.83)	−1.52(–1.80, –1.24)
Mongolia	−0.67(–0.70, –0.63)	−0.65(–0.69, –0.62)	−3.03(–3.26, –2.79)	−2.41(–2.71, –2.10)
Montenegro	0.09(0.05,0.13)	0.09(0.03,0.14)	−0.07(–0.11, –0.04)	−0.48(–0.69, –0.27)
New Zealand	−0.91(–1.11, –0.71)	−2.32(–2.58, –2.06)	−2.03(–2.18, –1.88)	−1.30(–1.42, –1.19)
North Macedonia	−0.68(–0.83, –0.52)	−0.96(–1.11, –0.82)	−1.59(–1.67, –1.51)	−0.79(–1.09, –0.50)
Northern Mariana Islands	−0.97(–1.27, –0.66)	−0.96(–1.23, –0.69)	−1.42(–1.50, –1.33)	−1.58(–1.66, –1.49)
Palestine	−0.11(–0.20, –0.01)	−0.31(–0.39, –0.23)	−1.44(–1.59, –1.28)	−1.55(–1.74, –1.35)
People’s Democratic Republic of Algeria	−0.16(–0.21, –0.12)	−0.16(–0.20, –0.12)	−0.87(–0.92, –0.81)	−1.45(–1.65, –1.24)
People’s Republic of Bangladesh	−1.09(–1.14, –1.04)	−0.99(–1.05, –0.93)	−2.15(–2.35, –1.95)	−1.47(–1.75, –1.19)
People’s Republic of China	−1.05(–1.29, –0.80)	−0.87(–1.03, –0.72)	−4.06(–4.24, –3.88)	−3.48(–3.74, –3.22)
Plurinational State of Bolivia	−1.45(–1.51, –1.39)	−1.48(–1.56, –1.41)	−2.10(–2.22, –1.98)	−1.21(–1.30, –1.13)
Portuguese Republic	−0.59(–0.78, –0.40)	−0.97(–1.25, –0.68)	−1.55(–1.66, –1.44)	−1.51(–1.67, –1.36)
Principality of Andorra	−0.61(–0.65, –0.56)	−1.11(–1.19, –1.04)	−1.52(–1.69, –1.36)	−1.58(–1.75, –1.42)
Principality of Monaco	−0.19(–0.22, –0.16)	−0.38(–0.43, –0.33)	−0.36(–0.43, –0.29)	−0.42(–0.52, –0.32)
Puerto Rico	−0.17(–0.29, –0.05)	−0.65(–0.81, –0.49)	−1.17(–1.38, –0.97)	−1.05(–1.33,–0.77)
Republic of Albania	−0.03(–0.14,0.08)	−0.13(–0.24, –0.02)	−2.48(–2.75, –2.21)	−3.32(–3.59, –3.04)
Republic of Angola	−1.52(–1.66, –1.38)	−1.59(–1.70, –1.48)	−2.20(–2.30, –2.10)	−1.67(–1.83, –1.51)
Republic of Armenia	−0.15(–0.26, –0.04)	−0.16(–0.26, –0.06)	−2.76(–3.18, –2.34)	−2.41(–2.81, –2.00)
Republic of Austria	−0.42(–0.50, –0.34)	−1.02(–1.10, –0.93)	−0.68(–0.77, –0.58)	−0.71(–0.92, –0.49)
Republic of Azerbaijan	−0.41(–0.45, –0.37)	−0.59(–0.65, –0.53)	−2.43(–2.68, –2.19)	−2.31(–2.51, –2.10)
Republic of Belarus	−1.19(–1.38, –1.01)	−1.66(–1.83, –1.49)	−4.83(–5.24, –4.42)	−6.14(–6.68, –5.60)
Republic of Benin	−0.74(–0.78, –0.70)	−0.76(–0.80, –0.71)	−1.35(–1.55, –1.15)	−1.74(–1.90, –1.58)
Republic of Botswana	−0.40(–0.47, –0.33)	−0.60(–0.67, –0.54)	−1.98(–2.12, –1.84)	−1.93(–2.06, –1.81)
Republic of Bulgaria	−0.50(–0.59, –0.41)	−0.76(–0.83, –0.69)	−1.86(–1.99, –1.74)	−2.83(–3.00, –2.66)
Republic of Burundi	−1.19(–1.23, –1.14)	−1.44(–1.51, –1.37)	−2.05(–2.22, –1.87)	−1.70(–1.86, –1.55)
Republic of Cabo Verde	−0.64(–0.73, –0.56)	−1.02(–1.16, –0.88)	−2.63(–3.20, –2.05)	−3.74(–4.24, –3.24)
Republic of Cameroon	−0.56(–0.61, –0.52)	−0.46(–0.50, –0.42)	−1.33(–1.44, –1.23)	−1.65(–1.73, –1.57)
Republic of Chad	−0.28(–0.31, –0.24)	−0.21(–0.26, –0.16)	−0.41(–0.48, –0.34)	−0.72(–0.82, –0.62)
Republic of Chile	−0.06(–0.12,0.01)	−0.42(–0.52, –0.32)	−0.68(–0.81, –0.55)	−0.29(–0.48, –0.09)
Republic of Colombia	−0.88(–1.12, –0.64)	−1.00(–1.18, –0.81)	−1.59(–1.70, –1.48)	−0.64(–0.85, –0.43)
Republic of Costa Rica	−0.56(–0.70, –0.42)	−0.84(–0.97, –0.71)	−1.20(–1.50, –0.89)	−0.56(–0.90, –0.22)
Republic of Cote d’Ivoire	−0.41(–0.49, –0.34)	−0.42(–0.47, –0.36)	−1.23(–1.33, –1.13)	−1.41(–1.49, –1.34)
Republic of Croatia	−0.55(–0.69, –0.41)	−0.78(–0.91, –0.65)	−0.87(–1.01, –0.74)	−1.46(–1.78, –1.15)
Republic of Cuba	0.11(0.00,0.21)	−0.18(–0.27, –0.08)	0.13(0.04,0.21)	0.93(0.79,1.07)
Republic of Cyprus	−0.22(–0.29, –0.15)	−0.52(–0.58, –0.46)	−1.96(–2.12, –1.79)	−2.38(–2.59, –2.17)
Republic of Djibouti	−1.06(–1.13, –0.99)	−1.18(–1.24, –1.12)	−1.62(–1.71, –1.52)	−1.43(–1.55, –1.32)
Republic of Ecuador	−1.46(–1.71, –1.21)	−1.48(–1.75, –1.21)	−1.77(–2.24, –1.30)	−1.53(–1.88, –1.17)
Republic of El Salvador	−1.32(–1.41, –1.24)	−1.61(–1.73, –1.49)	−2.10(–2.35, –1.85)	−1.98(–2.18, –1.77)
Republic of Equatorial Guinea	−1.50(–1.55, –1.44)	−1.60(–1.67, –1.54)	−3.19(–3.45, –2.92)	−2.80(–3.02, –2.58)
Republic of Estonia	−0.44(–0.50, –0.38)	−0.67(–0.75, –0.60)	−2.34(–2.51, –2.17)	−3.06(–3.20, –2.92)
Republic of Fiji	−1.66(–1.78, –1.55)	−1.71(–1.80, –1.62)	−2.27(–2.51, –2.03)	−1.52(–1.83, –1.22)
Republic of Finland	−0.16(–0.23, –0.09)	−0.46(–0.50, –0.43)	−0.67(–0.71, –0.62)	−0.81(–0.94, –0.69)
Republic of Ghana	−0.45(–0.48, –0.41)	−0.32(–0.36, –0.29)	−0.20(–0.39, –0.01)	−0.22(–0.36, –0.09)
Republic of Guatemala	−1.95(–2.16, –1.74)	−1.90(–2.11, –1.68)	−3.25(–3.84, –2.66)	−1.84(–2.11, –1.57)
Republic of Guinea	−0.56(–0.60, –0.52)	−0.51(–0.55, –0.46)	−0.58(–0.72, –0.45)	−1.03(–1.20, –0.86)
Republic of Guinea–Bissau	−1.02(–1.06, –0.99)	−0.99(–1.05, –0.93)	−1.19(–1.38, –1.00)	−1.53(–1.71, –1.35)
Republic of Guyana	−0.24(–0.36, –0.11)	−0.32(–0.47, –0.18)	−0.06(–0.19,0.07)	−0.29(–0.46, –0.13)
Republic of Haiti	−0.32(–0.35, –0.29)	−0.74(–0.80, –0.67)	−0.86(–0.92, –0.81)	−0.63(–0.66, –0.59)
Republic of Honduras	−1.50(–1.57, –1.44)	−1.42(–1.48, –1.37)	−0.82(–0.92, –0.73)	0.30(0.16,0.44)
Republic of Iceland	−0.96(–1.00, –0.91)	−1.30(–1.38, –1.21)	−1.01(–1.10, –0.91)	−0.04(–0.16,0.08)
Republic of India	−0.86(–1.14, –0.59)	−0.63(–0.80, –0.46)	−0.57(–0.67, –0.47)	−0.33(–0.44, –0.22)
Republic of Indonesia	−1.17(–1.26, –1.07)	−0.97(–1.05, –0.90)	−0.66(–0.75, –0.56)	−0.16(–0.26, –0.05)
Republic of Iraq	−0.76(–0.81, –0.71)	−0.94(–0.99, –0.88)	−1.35(–1.44, –1.25)	−0.83(–1.01, –0.65)
Republic of Italy	−0.64(–0.80, –0.49)	−1.38(–1.49, –1.26)	−1.65(–1.79, –1.51)	−1.83(–1.98, –1.69)
Republic of Kazakhstan	0.15(0.06,0.25)	0.17(0.06,0.27)	−0.79(–1.36, –0.23)	−0.55(–0.93, –0.16)
Republic of Kenya	−0.92(–1.01, –0.82)	−0.77(–0.83, –0.70)	0.13(–0.04,0.30)	0.26(0.17,0.35)
Republic of Kiribati	−1.22(–1.30, –1.15)	−1.30(–1.37, –1.22)	−1.12(–1.20, –1.05)	−0.60(–0.71, –0.48)
Republic of Korea	−0.48(–0.52, –0.44)	−0.67(–0.81, –0.52)	−3.44(–3.55, –3.33)	−4.61(–4.81, –4.40)
Republic of Latvia	−0.52(–0.62, –0.41)	−1.04(–1.17, –0.90)	−2.41(–2.77, –2.04)	−3.72(–4.04, –3.39)
Republic of Liberia	−0.90(–0.94, –0.85)	−0.67(–0.71, –0.62)	−1.14(–1.36, –0.91)	−1.41(–1.55, –1.26)
Republic of Lithuania	−0.18(–0.28, –0.08)	−0.60(–0.67, –0.53)	−3.08(–3.23, –2.92)	−4.22(–4.33, –4.10)
Republic of Madagascar	−1.17(–1.22, –1.12)	−1.50(–1.58, –1.42)	−0.96(–1.00, –0.92)	−0.39(–0.49, –0.29)
Republic of Malawi	−0.75(–0.80, –0.69)	−0.72(–0.78, –0.66)	−0.83(–0.97, –0.69)	−0.33(–0.49, –0.18)
Republic of Maldives	−1.47(–1.54, –1.41)	−1.39(–1.45, –1.33)	−3.74(–3.87, –3.60)	−3.05(–3.24, –2.86)
Republic of Mali	−0.35(–0.39, –0.30)	−0.27(–0.32, –0.21)	−0.60(–0.72, –0.48)	−0.80(–0.90, –0.69)
Republic of Malta	−0.56(–0.61, –0.50)	−1.06(–1.11, –1.02)	−1.48(–1.56, –1.40)	−1.52(–1.71, –1.33)
Republic of Mauritius	−1.19(–1.25, –1.14)	−1.34(–1.40, –1.27)	−2.41(–2.58, –2.23)	−2.79(–3.00, –2.58)
Republic of Moldova	−0.99(–1.03, –0.96)	−1.35(–1.39, –1.30)	−4.11(–4.41, –3.80)	−4.01(–4.48, –3.53)
Republic of Mozambique	−0.97(–1.03, –0.92)	−0.82(–0.87, –0.76)	−0.17(–0.28, –0.05)	0.13(0.04,0.22)
Republic of Namibia	−0.68(–0.73, –0.63)	−0.68(–0.73, –0.63)	−1.13(–1.34, –0.92)	−0.87(–1.02, –0.71)
Republic of Nauru	−1.48(–1.66, –1.29)	−1.35(–1.49, –1.21)	−1.18(–1.42, –0.95)	−0.66(–0.82, –0.51)
Republic of Nicaragua	−1.18(–1.25, –1.11)	−1.24(–1.31, –1.17)	−1.04(–1.17, –0.91)	−0.58(–0.85, –0.31)
Republic of Niue	−1.29(–1.42, –1.15)	−1.26(–1.36, –1.16)	−1.62(–1.76, –1.47)	−1.42(–1.51, –1.32)
Republic of Palau	−1.30(–1.46, –1.14)	−1.22(–1.35, –1.08)	−0.74(–0.81, –0.68)	−0.72(–0.79, –0.65)
Republic of Panama	−0.83(–0.88, –0.77)	−0.95(–1.00, –0.89)	−1.13(–1.24, –1.01)	−0.32(–0.56, –0.09)
Republic of Paraguay	0.09(0.03,0.15)	0.07(0.01,0.14)	0.16(0.10,0.23)	0.25(0.13,0.37)
Republic of Peru	−1.03(–1.15, –0.92)	−1.18(–1.32, –1.04)	−1.81(–2.14, –1.48)	−1.06(–1.31, –0.81)
Republic of Poland	0.26(–0.13,0.65)	−0.84(–1.18, –0.50)	−1.72(–1.97, –1.46)	−3.25(–3.44, –3.06)
Republic of Rwanda	−0.88(–0.94, –0.83)	−1.54(–1.63, –1.44)	−2.95(–3.24, –2.66)	−2.19(–2.53, –1.85)
Republic of San Marino	−0.15(–0.17, –0.14)	−0.32(–0.35, –0.30)	−0.71(–0.83, –0.59)	−1.48(–1.71, –1.25)
Republic of Senegal	−0.69(–0.74, –0.63)	−0.52(–0.59, –0.45)	−0.92(–1.26, –0.57)	−1.46(–1.74, –1.17)
Republic of Serbia	−0.27(–0.38, –0.17)	−0.32(–0.41, –0.24)	−1.53(–1.63, –1.44)	−1.93(–2.06, –1.79)
Republic of Seychelles	−0.43(–0.53, –0.33)	−0.44(–0.52, –0.35)	−1.42(–1.52, –1.31)	−0.69(–0.98, –0.40)
Republic of Sierra Leone	−0.55(–0.59, –0.51)	−0.47(–0.52, –0.42)	−0.81(–0.97, –0.66)	−1.37(–1.54, –1.20)
Republic of Singapore	−1.43(–1.62, –1.24)	−2.36(–2.59, –2.13)	−4.55(–4.74, –4.35)	−5.47(–5.70, –5.23)
Republic of Slovenia	−0.47(–0.50, –0.44)	−0.80(–0.85, –0.76)	−2.23(–2.41, –2.05)	−2.46(–2.78, –2.15)
Republic of South Africa	−1.88(–2.13, –1.63)	−1.48(–1.66, –1.30)	−0.89(–1.28, –0.50)	−0.48(–0.77, –0.18)
Republic of South Sudan	−0.91(–0.97, –0.84)	−1.00(–1.08, –0.92)	−1.05(–1.34, –0.77)	−0.98(–1.13, –0.82)
Republic of Sudan	−1.22(–1.32, –1.12)	−1.15(–1.22, –1.07)	−1.93(–1.99, –1.87)	−1.64(–1.72, –1.57)
Republic of Suriname	−0.12(–0.25,0.01)	−0.23(–0.36, –0.10)	−0.70(–0.82, –0.57)	−0.78(–0.91, –0.65)
Republic of Tajikistan	−0.32(–0.34, –0.30)	−0.56(–0.59, –0.53)	−2.10(–2.30, –1.91)	−1.53(–1.77, –1.29)
Republic of the Congo	−0.97(–1.06, –0.89)	−1.11(–1.16, –1.05)	−2.01(–2.13, –1.89)	−1.61(–1.76, –1.47)
Republic of the Gambia	−0.54(–0.59, –0.50)	−0.46(–0.49, –0.43)	−0.73(–0.96, –0.51)	−0.93(–1.10, –0.76)
Republic of the Marshall Islands	−1.22(–1.33, –1.11)	−1.21(–1.29, –1.14)	−1.19(–1.25, –1.13)	−0.94(–1.03, –0.85)
Republic of the Niger	−0.69(–0.73, –0.64)	−0.58(–0.65, –0.51)	−1.09(–1.31, –0.87)	−1.54(–1.76, –1.33)
Republic of the Philippines	−0.99(–1.08, –0.91)	−1.01(–1.08, –0.95)	−0.78(–0.86, –0.70)	−1.28(–1.44, –1.13)
Republic of the Union of Myanmar	−1.08(–1.13, –1.03)	−0.90(–0.94, –0.86)	−1.76(–1.88, –1.64)	−1.08(–1.20, –0.96)
Republic of Trinidad and Tobago	−0.25(–0.69,0.18)	−0.32(–0.76,0.13)	−0.94(–1.10, –0.77)	−1.64(–1.78, –1.51)
Republic of Tunisia	−0.26(–0.32, –0.21)	−0.17(–0.21, –0.13)	−0.83(–0.86, –0.80)	−1.35(–1.51, –1.18)
Republic of Turkey	−1.02(–1.16, –0.89)	−1.14(–1.26, –1.02)	−1.41(–1.63, –1.20)	−1.19(–1.42, –0.96)
Republic of Uganda	−0.75(–0.80, –0.69)	−0.95(–1.01, –0.88)	−1.71(–1.86, –1.56)	−1.40(–1.58, –1.23)
Republic of Uzbekistan	−0.76(–0.83, –0.70)	−1.17(–1.26, –1.07)	−4.00(–4.60, –3.40)	−4.27(–4.83, –3.71)
Republic of Vanuatu	−1.23(–1.33, –1.13)	−1.22(–1.30, –1.15)	−1.17(–1.25, –1.10)	−0.99(–1.05, –0.93)
Republic of Yemen	−1.08(–1.12, –1.04)	−1.21(–1.26, –1.16)	−1.45(–1.53, –1.37)	−1.35(–1.41, –1.29)
Republic of Zambia	−0.76(–0.82, –0.70)	−0.59(–0.64, –0.53)	−0.94(–1.07, –0.81)	−0.23(–0.43, –0.03)
Republic of Zimbabwe	−0.21(–0.24, –0.17)	−0.02(–0.05,0.01)	0.83(0.51,1.15)	0.38(0.16,0.60)
Romania	−0.34(–0.45, –0.23)	−0.62(–0.71, –0.54)	−2.45(–2.68, –2.21)	−4.43(–4.70, –4.17)
Russian Federation	−1.73(–1.81, –1.64)	−1.93(–2.02, –1.84)	−3.27(–3.56, –2.98)	−3.27(–3.55, –3.00)
Saint Kitts and Nevis	0.07(–0.11,0.26)	−0.09(–0.28,0.10)	−0.19(–0.28, –0.09)	−0.07(–0.21,0.08)
Saint Lucia	−0.38(–0.50, –0.26)	−0.56(–0.67, –0.44)	−0.80(–0.93, –0.67)	−0.70(–0.93, –0.46)
Saint Vincent and the Grenadines	−0.06(–0.21,0.10)	−0.17(–0.31, –0.02)	−0.02(–0.09,0.06)	0.43(0.31,0.55)
Slovak Republic	−0.05(–0.09, –0.01)	−0.10(–0.14, –0.05)	−0.69(–0.77, –0.61)	−1.14(–1.22, –1.06)
Socialist Republic of Viet Nam	−0.46(–0.60, –0.31)	−0.39(–0.48, –0.30)	−0.91(–0.97, –0.84)	−0.81(–0.87, –0.75)
Solomon Islands	−0.90(–0.98, –0.82)	−0.88(–0.93, –0.83)	−0.89(–0.96, –0.83)	−0.74(–0.80, –0.68)
State of Eritrea	−1.20(–1.26, –1.13)	−1.35(–1.41, –1.29)	−1.53(–1.61, –1.45)	−1.17(–1.23, –1.11)
State of Israel	−0.74(–0.81, –0.67)	−1.26(–1.36, –1.17)	−1.67(–1.80, –1.54)	−2.16(–2.30, –2.02)
State of Kuwait	−0.22(–0.31, –0.14)	−0.31(–0.39, –0.23)	−1.43(–1.60, –1.27)	−2.91(–3.18, –2.64)
State of Libya	−0.32(–0.37, –0.27)	−0.37(–0.41, –0.32)	−0.16(–0.26, –0.05)	−0.60(–0.80, –0.39)
State of Qatar	−0.33(–0.39, –0.28)	−0.37(–0.42, –0.32)	−2.31(–2.67, –1.95)	−2.45(–2.97, –1.93)
Sultanate of Oman	0.49(0.40,0.59)	0.32(0.25,0.38)	−0.83(–1.00, –0.65)	−1.25(–1.48, –1.02)
Swiss Confederation	−0.60(–0.63, –0.58)	−1.07(–1.13, –1.02)	−1.45(–1.51, –1.40)	−1.39(–1.49, –1.29)
Syrian Arab Republic	0.45(0.24,0.67)	0.34(0.19,0.50)	−0.60(–0.74, –0.46)	0.07(–0.18,0.32)
Taiwan (Province of China)	−0.15(–0.39,0.09)	−0.57(–0.75, –0.39)	−2.06(–2.18, –1.93)	−2.69(–2.81, –2.57)
Togolese Republic	−0.32(–0.37, –0.27)	−0.27(–0.31, –0.24)	−0.70(–0.82, –0.57)	−1.04(–1.15, –0.93)
Tokelau	−1.45(–1.53, –1.36)	−1.47(–1.54, –1.40)	−1.60(–1.74, –1.46)	−1.47(–1.54, –1.41)
Turkmenistan	−1.99(–2.11, –1.86)	−2.20(–2.33, –2.07)	−3.90(–4.42, –3.38)	−4.75(–5.19, –4.30)
Tuvalu	−1.95(–2.00, –1.90)	−1.80(–1.85, –1.75)	−2.15(–2.24, –2.07)	−1.93(–2.00, –1.87)
Ukraine	−1.22(–1.27, –1.17)	−1.55(–1.61, –1.48)	−4.96(–5.36, –4.56)	−5.05(–5.65, –4.45)
Union of the Comoros	−0.81(–0.87, –0.75)	−0.97(–1.04, –0.90)	−1.89(–2.12, –1.67)	−1.93(–2.07, –1.78)
United Arab Emirates	−0.44(–0.51, –0.36)	−0.75(–0.81, –0.68)	−0.69(–1.05, –0.32)	−0.08(–0.47,0.32)
United Kingdom of Great Britain and Northern Ireland	−0.63(–0.74, –0.52)	−1.27(–1.42, –1.12)	−0.92(–1.00, –0.84)	−0.23(–0.30, –0.15)
United Mexican States	−1.13(–1.38, –0.89)	−1.05(–1.20, –0.90)	−1.46(–1.54, –1.38)	−1.58(–1.65, –1.51)
United Republic of Tanzania	−0.15(–0.21, –0.10)	−0.26(–0.31, –0.21)	−0.99(–1.03, –0.95)	−1.01(–1.11, –0.91)
United States of America	0.69(0.42,0.97)	0.77(0.53,1.02)	0.40(0.32,0.48)	1.01(0.81,1.20)
United States Virgin Islands	0.03(–0.08,0.13)	−0.13(–0.25, –0.02)	−0.87(–0.96, –0.78)	−1.44(–1.57, –1.32)

ASIR, age-standardized incidence rate; ASPR, age-standardized prevalence rate; ASMR, age-standardized mortality rate; ASDR, age-standardized DALYs rate.

Burden of chronic respiratory diseases, analyzed by age and sex

In our study, we categorized patients with CRDs aged 0 to 100 years globally into 20 age groups based on 5-year intervals (Figure 6). The results of our etiological analyses for ASIR, ASPR, ASDR and ASMR in 2021 indicated minimal overall change compared to 1990. Notably, the ASIR and ASPR of CRDs among the 0–14 years age group revealed a higher incidence in males compared to females. Conversely, in older age groups, the trend reversed, with higher rate observed in females (Figure 6A–C). In the analysis of disease etiology for 2021, we found a decline in the prevalence of asthma among children, particularly in the <5 years age group, where the decrease was most pronounced. The prevalence of asthma tends to decrease gradually with age in all childhood age groups (<14 years), remains stable in the 15–40 age group, and increases gradually in the 45+ age group (including the elderly). Conditions such as COPD, interstitial pneumonia, pulmonary nodular disease, and pneumoconiosis were absent in the pediatric age group, and these diseases consistently exhibited rising ASIRs with advancing age, particularly in individuals aged 40 years and above (Figure 7A). By 2021, asthma prevalence was notably higher among children and middle-aged to older adults, while the prevalence of asthma, COPD, interstitial pneumonia, and pneumoconiosis was predominantly observed in middle-aged and older adults over 50 years, with an increasing trend in prevalence associated with advancing age (Figure 7B).

Figure 6.

Age-specific burden, stratified in age groups of 5 years, with comparisons across gender and age groups. (A) incidence and age-standardized incidence, (B) prevalence and age-standardized prevalence, (C) mortality and age-standardized mortality, (D) DALYs and age-standardized DALYs for chronic respiratory diseases in 2021. Uncertainty interval (UI).

Figure 7.

Global age-specific, stratified by an age group of 5 years, different CRDs were compared between age groups 1990–2021. (A) age-standardized incidence, (B) age-standardized prevalence, and (C) age-standardized mortality of chronic respiratory diseases by disease category.

In 2021, the ASDR and ASMR of CRDs were substantially higher in males than in females across most age groups. This trend is particularly evident in the 70–74, 75–79 and 80–84 age groups. A comparison of the ASMR for different causes of CRDs between 1990 and 2021 reveals noteworthy trends. In 1990, asthma mortality peaked in the 90–95 age cohort. However, by 2021, the ASMR for asthma and other causes of CRDs showed a consistent increase with advancing age, especially for interstitial pneumonia, which predominantly affected individuals over 60 years. Moreover, the ASIR for asthma and COPD was significantly higher among those aged 50 years and older (Figure 7C).

Chronic respiratory disease burden by SDI

From 1990 to 2021, the high SDI quintile has the highest ASIR for CRDs. For example, Poland and the United States, which are high SDI regions, and Haiti, a medium SDI region, exhibit relatively high ASIR (Figure 8). Similarly, over the past three decades, despite the overall downward trend in ASPR in CRD, ASPR has remained higher in areas with higher SDI (Figure S5). It is worth noting that while ASIR and ASPR are relatively high in areas with high levels of SDI, ASDR and ASMR are relatively low in these areas (Figure S6). Among 204 countries and territories, Papua New Guinea’s ASMR and ASDR for CRDs are much higher than expected in 2021 (Figure S7).

Figure 8.

Age-standardized incidence rate (ASIR) for chronic respiratory diseases in 21 regions and 204 countries and territories according to the sociodemographic index (SDI). A: 21 regions counted by the SDI from 1990 to 2021. B: 204 countries and territories by the SDI in 2021.

Risk factors

The global percentage of deaths from CRDs attributable to household air pollution from solid fuels significantly decreased from 37.83% in 1990 to 15.48% in 2021. Likewise, the proportion of CRD deaths linked to smoking decreased from 36.58% to 30.86% during the same period. In the High-SDI region, the percentage of deaths among CRD patients attributed to household solid fuel air pollution plummeted from 2.68% to 0.02%, while deaths related to particulate matter pollution also declined from 15.83% in 1990 to 7.05% in 2021. In contrast, attributable risk ratios in other SDI quintiles are roughly comparable to global levels (Figure S8).

By 2021, the percentage of deaths and disabilities attributable to solid fuel household air pollution is higher for women with CRD than for men, especially in low and middle SDI regions (Figure S9 and Figure S9). In the same year, globally, 40.19% of male CRD patients had DALYs attributable to smoking (S10). In age-specific analyses of risk factors for CRD mortality, a notable trend was observed in high BMI, which increased rapidly among adults aged 20–24 years (10.36%) and peaked at age 45–49 years (18.15%), subsequently declining to 11.76% at age 80–84 years before rising again. Occupational exposure accounted for over 15% of the population in age groups 25–54 years, while smoking predominantly affected those aged 50–64 years (more than 10%).

For COPD, six primary risk factors were identified: smoking, secondhand smoke, household air pollution, ambient particulate matter, ozone, and occupational particulate matter (including coal dust). Smoking emerged as the most significant risk factor, particularly affecting individuals over 30 years of age. For pneumoconiosis, three main risk factors were assessed, with occupational asbestos having a substantial impact on those under 35 years, especially among younger age groups. In contrast, the influence of occupational particulate matter was notably significant for young people aged 15–24 years, while occupational silica exposure was most pronounced among middle-aged individuals aged 35–59 years (Figure 9).

Figure 9.

Analysis of risk factors for chronic respiratory diseases attributable to death in 2021: (A)three key risk factors for asthma (high body mass index BMI, smoking, and occupational exposures), (B) six major risk factors for COPD (smoking, secondhand smoke, household air pollution, ambient particulate matter, ozone, and occupational particulate matter, including coal dust), (C) three major risk factors for pneumoconiosis (occupational particulate matter, occupational silica, and occupational asbestos).

Future projections of global chronic respiratory diseases from the BAPC model

Figure S11 shows future projections of ASIR and ASMR for CRDs from the GBD study. As shown in Figure S11, the ASIR for CRDs is projected to continue to decline globally, with a projected decline to 517.25 per 100,000 people and an ASMR of 39.21 per 100,000 people by 2035 (Figure S11; Table S3). Over time, globally, the new ASIR for asthma is projected to be 387.85 per 100,000 people, the ASIR for COPD will decline to 266.41 per 100,000 people, and the incidence rate of Pneumoconiosis is expected to decline modestly within a stabilized range to 0.89 per 100,000 people. Particularly noteworthy is that the ASIR for ILD and Pulmonary Sarcoidosis experienced a sustained and rapid increase between 1990 and 2013, but has shown a slow downward trend since 2013. The rate is projected to decline further to 6.20 per 100,000 by 2035 (Figure 10; Table 2).

Figure 10.

The BAPC model projects to 2035. (A) Asthma, (B) COPD, (C) pneumoconiosis, and (D) ILD and pulmonary sarcoidosis.

Discussion

This study provides comprehensive global, regional, and national estimates of the CRD burden from 1990 to 2021, quantifying the impact of the full range of attributable risk factors on mortality and life expectancy. While the overall global burden of CRD declined during this period, epidemiological trends exhibited significant variations across different sexes and geographic regions, aligning with findings from prior research [14,15]. The increase in global population from 5.3 billion in 1990 to 7.9 billion in 2021 is the main cause of the overall increase in the number of people burdened.

Globally, pneumoconiosis, which constitutes the smallest proportion of CRDs, demonstrated the most substantial declines in ASIR, ASMR, and ASDR (EAPC = −1.14), alongside a notable reduction in asthma ASPR (EAPC = −1.59). Despite moderate prevalence ASRs and low SDIs, Oceania and South Asia exhibited the highest ASRs for CRD-related mortality and disability, potentially underscoring disparities in disease management and healthcare quality across income levels [26,27]. Low- and middle-income countries face numerous challenges in managing CRD, including limited preventive strategies and higher cumulative lifetime exposure to risk factors. Underdiagnosis remains prevalent, as patients in these regions are often diagnosed only when symptoms become severe. Limited access to essential diagnostic tools, such as pulmonary function tests and chest imaging, at the primary care level, coupled with a shortage of trained clinical staff, hinders accurate diagnosis [28]. Thus, enhancing healthcare quality, expanding professional training, and improving diagnostic accessibility are critical for increasing CRD diagnostic rates and enhancing patient outcomes [29]. Concurrently, efforts to promote education and awareness about CRD prevention should be intensified to reduce incidence and overall disease burden.

Our study highlighted an intriguing trend: despite relatively high age-standardized asthma prevalence, mortality rate from asthma remain low in middle- and high-SDI regions and high-income countries. This may be attributable to advanced healthcare systems, heightened awareness of disease self-management, and more effective asthma care and control in these regions [30–32]. While industrial development and urbanization have contributed to increased asthma prevalence, mortality has been effectively managed. Over the past three decades, high BMI—the only metabolic risk factor for CRD analyzed—has emerged as a significant global risk factor for asthma. This trend likely correlates with the rising incidence of metabolic diseases, such as type 2 diabetes, during periods of social and economic transition [33]. To mitigate obesity’s impact on asthma burden, evidence-based strategies such as adopting balanced dietary patterns (e.g. reducing ultra-processed foods, added sugars and saturated fats while increasing plant-based nutrients) combined with regular physical activity show particular promise [14,34,35]. These interventions underscore the importance of global efforts toward promoting healthy lifestyles to reduce asthma and obesity prevalence in affected populations. Establishing long-term healthcare strategies, enhancing public awareness of asthma, advocating for healthy living, and developing standardized management guidelines are critical steps to improve asthma outcomes and reduce its societal burden [30].

In 2021, COPD was responsible for approximately 85.25% of all chronic respiratory disease-related deaths, highlighting its substantial impact on global health. Elevated ASIR and ASPR persist in resource-limited regions, including southern Sub-Saharan Africa, parts of Asia, Oceania, and other developing areas—exemplified by Papua New Guinea, Nepal, China, and India where COPD remains a significant public health challenge. In low-middle SDI regions, limited socioeconomic development, inadequate healthcare resources, and low public awareness of health maintenance collectively contribute to the persistently high ASMR of COPD. However, in higher-SDI regions, despite generally advanced healthcare systems and resources, the challenge of COPD mortality is intensified primarily by population aging [36,37]. There is a need for targeted interventions for older populations to address these growing health problems.

Primary risk factors for COPD include tobacco smoking, ozone depletion, and environmental pollution. Regions with high SDI and early industrialization have implemented more robust environmental protections and public health systems. Such policies, alongside stricter environmental regulations and improved health resources, contribute to relatively lower ASPR for COPD in these areas compared to economically underdeveloped regions [14,36,38]. Consequently, recommendations for COPD prevention and management are especially relevant and urgently needed for low-SDI areas.

Our study indicates that from 1990 to 2021, the ASIR and ASPR for pneumoconiosis decreased annually by 1.14% and 0.48%, respectively, with ASMR and ASDR showing even more pronounced annual reductions of over 2.5%. These trends reflect significant global progress in pneumoconiosis prevention and control. The high incidence of pneumoconiosis remains closely associated with industrial dust exposure, particularly in sectors like mining, metallurgy, and construction materials, where coal and metal mining contribute a substantial proportion of occupational disease cases [39,40]. The burden of silicosis is predominantly concentrated in countries with high-middle and medium SDI, such as China, where rapid industrialization and an extensive industrial system contribute to crystalline silica dust exposure in various workplaces. Although the burden of pneumoconiosis shows an overall declining trend, it remains a significant concern. Health authorities and policymakers must prioritize and implement effective preventive strategies to enhance the quality of life for individuals with pneumoconiosis. Employers should reinforce dust exposure controls in the workplace and ensure that workers properly use personal protective equipment (PPE), as this is a critical measure for pneumoconiosis prevention. Additionally, regular medical checkups are essential for the early detection and intervention of pneumoconiosis and should be conducted systematically [41–43]. Health education and training are also vital, particularly to raise public awareness of pneumoconiosis in economically disadvantaged areas, enhance prevention awareness, and promote healthy lifestyle choices.

The disease burden of ILD and Pulmonary Sarcoidosis, though lower than that of COPD and asthma, has shown an upward trend over the past three decades, with ASMR and ASDR increasing by an average of 1.55% and 0.95% per year, respectively. This phenomenon, associated with population aging, is closely linked to age-related biological processes. These include telomere attrition, proteostasis dysfunction, and oxidative stress, collectively impairing alveolar epithelial integrity [44,45]. Furthermore, elderly ILD patients exhibit a higher prevalence of comorbid cardiovascular and autoimmune diseases, reflecting wider connections between chronic respiratory conditions and aging [46,47]. Additionally, suboptimal medication adherence in this population may further diminish clinical efficacy [48,49]. Also, advancements in diagnostic criteria and medical technology, particularly the expanded use of imaging techniques such as CT [50–52], have significantly improved the detection of these conditions. This improvement is especially pronounced in high-SDI regions, likely contributing to the observed increase in reported cases [53].

The global decline in ASR for CRD from 1990 to 2021 likely reflects a combination of factors. Key influences include the success of global and regional tobacco control initiatives, such as the WHO Framework Convention on Tobacco Control, and national strategies like the ‘Healthy China 2030’ plan, which aims to reduce smoking prevalence to 20%, significantly contributing to global smoking reduction efforts. Additional contributors to this trend include lower urban pollution levels, the adoption of electric vehicles, improved occupational environments, better understanding and prevention of allergens, more effective management of respiratory and non-respiratory comorbidities, and a reduction in underdiagnosis of CRDs [36,54,55]. While smoking remains the primary risk factor for CRDs in men, household air pollution—primarily from solid fuels—poses an even greater health risk in low-SDI areas, particularly impacting women. This is often due to the widespread use of solid fuels like wood and coal for cooking and heating, combined with women’s prolonged exposure to indoor smoke from these activities. Promoting clean energy usage in low-SDI regions is therefore essential to reducing CRD burden, especially among women. By improving cooking and heating practices, indoor air pollution can be markedly reduced, offering critical protection for women’s health and helping to alleviate the global CRD burden [33,56].

Our analysis of age- and sex-specific patterns reveals that CRD mortality and reductions in life expectancy are highest globally among those aged 95 years and older, with these rates generally increasing with age. In addition, in 2021, males showed higher incidence and prevalence of CRD than females across the three age groups, particularly in those aged 0–14 years. These patterns offer valuable insights for developing targeted screening and prevention strategies. The observed age- and sex-specific trends in asthma incidence, prevalence, mortality, and life expectancy reduction appear to be influenced by multiple risk factors, the mechanisms of which remain incompletely understood. It has been suggested that the higher prevalence of asthma in females post-puberty may relate to the effects of sex hormones [15,57]; however, further studies are needed to substantiate these findings. The increase in the absolute crude prevalence of COPD in the elderly population, especially in areas with high SDI, may be related to population ageing and longer life expectancy.

Despite projections indicating that the global incidence of CRD will decrease to approximately 517.25 per 100,000 people by 2035, the absolute number of affected individuals remains substantial due to the large population base. Among these, the predicted prevalence of asthma in the population ranked first, and the role of high-BMI in the burden of asthma is becoming increasingly prominent, especially affecting the young and middle-aged groups. Consequently, policymakers should advocate for healthy lifestyles, including balanced diets, avoiding sedentary behavior, and maintaining regular physical activity [58–60]. COPD followed in second place. For international health organizations, prioritizing tobacco control and CRD management in low SDI regions is crucial. Proven effective measures include implementing national restrictions and public smoking bans, increasing tobacco taxes to deter consumption, and prohibiting tobacco marketing and smoking in public places—all effective strategies for reducing smoking rates and secondhand smoke exposure [61,62]. Furthermore, pneumoconiosis, as well as ILD and pulmonary sarcoidosis, should not be overlooked. Within existing policy frameworks, greater efforts should focus on finding asbestos substitutes, strengthening environmental protection, and conducting regular monitoring of the living environments surrounding mining communities [63,64].

A comprehensive understanding of the multifaceted factors driving the global burden of CRDs is crucial for developing precise prevention strategies and optimizing resource allocation. Our evaluation indicates that policy interventions over the past three decades have yielded significant progress in addressing CRDs. This underscores the continued necessity for an integrated approach encompassing modifiable risk factors, enhanced screening and early diagnosis programs, and equitable access to quality healthcare services across all SDI regions. Furthermore, the findings highlight the importance of optimizing resource allocation and tailoring interventions to the unique needs of specific populations and regions. These insights provide key recommendations for next-step policy formulation, advocating for more targeted measures to effectively combat the ongoing global challenge posed by CRDs.

This study has several limitations that warrant further exploration. First, the accuracy of the GBD data is heavily reliant on the quality of raw data from individual countries, and disparities in data quality may significantly influence the study outcomes, particularly as smaller or less developed nations often face challenges such as weak health infrastructure, inadequate data collection systems, and limited resources, which can compromise the completeness, consistency, and timeliness of their data, undermining the reliability of the findings and potentially obscuring the true disease burden in specific regions or populations, thereby impacting global health policy formulation and resource allocation; second, variations in case-reporting methodologies and diagnostic definitions across countries may introduce systematic biases, for instance, some countries may employ more stringent diagnostic criteria, while others, constrained by limited medical resources, may struggle to fully detect and report cases, and cultural, economic, and social factors can further influence the accuracy and completeness of disease reporting, potentially leading to either underestimation or overestimation of the disease burden and complicating cross-national comparisons and trend analyses; third, although COPD and ILD have been identified as significant predictors of disease progression and mortality in COVID-19 patients [65–67], the current study was unable to examine the association between COVID-19 and the burden of CRD due to the separate classification of COVID-19 within the GBD framework; fourth, the use of population-level summary data precludes mediation analysis of variables like sex in exposure–outcome relationships; finally, the analysis of associations between risk factors and health outcomes in GBD studies is built on a modifiable risk-outcome pairwise framework, which was constructed following systematic evidence from the literature and the feasibility of adding new risk factors is continuously assessed.

Conclusion

Although the overall burden of CRD is decreasing, significant epidemiologic differences remain between regions and sexes. The number of annual deaths due to CRD remains large, and long-term follow-up and observation of the impact of CRD, is still ongoing. Reducing the prevalence of modifiable risk factors and improving the clinical effectiveness and cost-efficiency of screening are key strategies for reducing the burden of CRD. For policymakers considering extending screening to high-risk populations with a non-smoking history, it is important to assess the local risk factor context, current epidemiologic trends, and economic burden, particularly in low-SDI areas(This includes consideration of factors such as genetic variation, familial inheritance, passive smoking, and indoor air pollution). Therefore, establishing long-term health care measures, raising population awareness of the various CRDs, promoting healthy lifestyles, and developing a set of standards for their management are essential for reducing the burden of CRDs. This involves not only improving public health literacy, but also effectively curbing the harm caused by the growth of CRDs through policy guidance and resource allocation.

Funding Statement

This study was supported by the Natural Science Foundation of Education Department of Anhui Province (No.2022AH051221), the Youth Teacher Training Project of Education Department of Anhui Province (No.JWFX2024028), and Anhui Province Key Clinical Specialist Construction Programs.

Ethics approval and consent to participate

The Institutional Review Board of the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital) concluded that this study did not need to be approved because it used publicly available data.

Authors contributions

Ying Zhai: Conceptualization, Methodology, Software, Writing–Original Draft, Visualization, Chuanmiao Zhu: Software, Tengxiao Zhu: Data Curation, Visualization, Wenjing Song: Validation, Visualization, Yu Tang: Software, Validation, Visualization, Fengxia Ruan: Formal analysis, Visualization, Luqing Jiang: Methodology, Data Curation, Project administration, Zichen Xu: Investigation, Data Curation, Visualization, Lei Li: Software, Formal analysis, Visualization, Xia Fu: Validation, Daoqin Liu: Methodology, Software, Aidong Chen: Writing–Review and Editing. Qiwen Wu: Writing–Review and Editing, Supervision, Funding acquisition. All authors have read and approved the final work.

Availability of data and materials

The GBD data can be accessed online through the GHDx portal (https://vizhub.healthdata.org/gbd-results/), a comprehensive database for retrieving and analyzing health-related data and the primary data source for this study.

Disclosure statement

No potential competing interest was reported by the author(s).