Global, regional, and national burden of chronic respiratory diseases and associated risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

doi:10.21203/rs.3.rs-1726573/v2

Background

The burden of chronic respiratory diseases has changed over three decades.To describe the spatiotemporal trends of prevalence, mortality and disability adjusted life years (DALY) due to chronic respiratory diseases(CRDs), across the globe during 1990–2019 using data from the Global Burden of Disease Study 2019(GBD 2019).

Methods

The prevalence, mortality and DALY attributable to CRDs, and risk factors from 1990 to 2019 were estimated. We also assessed the driving factors and potentiality for improvement by decomposition analyses and frontier analyses.

Results

In 2019, 454.56 (95% uncertainty interval(UI): 417.35-499.14) million individuals worldwide had a CRD, showing a 39·8% increase compared with 1990. Deaths due to CRDs numbered 3.97(95% UI: 3.58–4.30) million, and DALY in 2019 was 103.53 (95% UI:94.79-112.27) million. Declines by average annual percent change(AAPC) were observed in age-standardized prevalence rates(ASPR) (0.64% decrease), age-standardized mortality rates(ASMR) (1.92%), and age-standardized DALY rates(ASDR) (1.72%) globally and in 5 socio-demographic index (SDI) regions. Decomposition analyses represented that the increase in overall CRDs DALY was driven by aging and population growth. Chronic obstructive pulmonary disease (COPD) was the leading drivers globally. Frontier analyses witnessed significant improvement opportunities at all levels of the development spectrum. Smoking remained a leading risk factor(RF) for the mortality and DALY, although, it showed a downward trend. Air pollution, a growing factor especially in relatively low SDI regions, deserves our attention.

Conclusion

Our study clarified that CRDs remain a leading cause of prevalence, mortality and DALY worldwide, with growth in absolute numbers but declines in several age-standardized estimators since 1990. The estimated contribution of risk factors to mortality and DALY supports the need for urgent measures to improve them.

Chronic respiratory diseases(CRDs) are chronic, commonly occurring, persistent diseases affecting the airways and the lung, among the leading causes of morbidity and mortality globally. It was estimated that 4 million people died prematurely yearly in globe(1) and costs of about €380 billion were paid for CRDs patients care among European Union states in 2019(1), posing a major public health problem. CRDs mainly include chronic obstructive pulmonary disease (COPD), asthma, interstitial lung disease and pulmonary sarcoidosis(ILD&PS)and pneumoconiosis. Although COVID-19 is still spreading globally, the prevention and treatment of non-communicable diseases (NCDs) cannot be compromised(2). CRDs are nonnegligible contributors to the rising burden of NCDs worldwide(3). These types of CRDs are associated with socioeconomic development, demographic trends and risk factors(RFs), including smoking, air pollution, non-optimal temperature and so on. Over the past 30 years, the aging population has increased rapidly(4), moreover, economic shifts, sociodemographic development and risk factors have changed dramatically over the decades(5). In consequence, the situation of CRDs has altered drastically.

Some preceding analyses of disease burden from CRDs were published. For instance, a systematic analysis for GBD 2017 performed by Xiaochen Li et al(5) reported the mortality, DALY and associated ASR, however, this study lacked the prevalence and age-standardized prevalence rates(ASPR) analysis. Another systematic analysis for GBD 2017 written by GBD CRDs Collaborators(1), added the prevalence and ASPR component, but its analysis was limited in GBD region, the association with socio-demographic index (SDI) not further analyzed. A relatively recent article performed by Hui Gan et al(6) derived from GBD 2019 only discussed incidence, mortality and corresponding age-standardized rates(ASR), without national level. In our study, we updated the geographical and time trends of prevalence, mortality and disability adjusted life years (DALY) due to CRDs, by age, sex and SDI, using data from the GBD 2019, Meanwhile, we innovatively introduced decomposition analysis and frontier analysis method. Affecting factors for CRDs, including the SDI and multiple risk factors, were applied to inform policy makers and services reduce mortality due to CRDs, and achieve the health-related Sustainable Development Goals (SDGs) by 2030(7).

Data source

Data on prevalence, mortality, DALY, ASR (ASPR, ASMR and age-standardized DALY rates [ASDR]) of CRDs in 204 countries and territories during 1990-2019 were obtained from the Global Burden of Disease Study 2019 (http://ghdx.healthdata.org/gbd-results-tool). Five categories of chronic respiratory diseases—COPD, pneumoconiosis, asthma, ILD&PS and other CRDs—were identified based on the International Classification of Diseases and Injuries-10(ICD-10) diagnostic codes. Covariates included the SDI and risk factors. The detailed information of SDI and risk factors were explained in the supplementary material.

Statistical analyses

The standardized methods of the GBD 2019 have been extensively published(8, 9). Prevalence, mortality and DALY from CRDs—COPD, pneumoconiosis, asthma, ILD&PS and others—were estimated for 204 countries and territories, by age and sex, from 1990 to 2019 using DisMod-MR 2.1, a Bayesian meta-regression tool. All estimates were derived from the mean of 1000 draws, and 95% uncertainty intervals(UI) were determined using the 2.5th and 97.5th centiles of the ordered draws. Spearman’s rank order correlation was utilized to evaluate the strength and direction of the association between the SDI and age-standardized rates(ASMR&ASDR), the change in the SDI between 1990 and 2019 (the ratio of 2019 index to 1990 index), and the average annual percentage change(AAPC) of ASR during 1990-2019 was calculated. To assess the magnitude and direction of trends in the ASR of CRDs over time, we adopted JoinPoint software (Version 4.9.0.0) to calculate the AAPC and the corresponding 95% confidence interval(CI) by joinpoint regression analysis. By comparing AAPC with 0, we ascertained whether the variation trend in different sections is statistically significant. The detailed description of the decomposition assessment and frontier analysis was described in the Supplementary Method.

Patient and public involvement

There was no patient or public involvement in the study. No patients were evaluated in the study.

Disease Burden in the global, regional and national levels

Global level

This study identified 454.56 (95% UI: 417.35-499.14) million individuals of CRDs in 2019, with an ASPR of 5789.16(95% UI: 5290.68-6418.14) (Table 1, Table S1-2). The number of prevalence increased by 39.8% (Table S2). Nevertheless, the APSR declined by an average of 0.64% (95% CI: -0.70 to -0.58) annually from 1990 to 2019, and this phenomenon also existed in both sexes and all SDI regions (Table 1, Table S1-2). CRDs was responsible for 3.97(95% UI: 3.58-4.30) million deaths in 2019 globally with an ASMR of 51.28 (95% UI: 45.90-55.51), whose AAPC decreased by 1.92 (95% CI: -2.00 to -1.84) (Table 1, Table S7-8). DALY associated in 2019 was 103.53 (95% UI:94.79-112.27) million, with an ASR of 1293.74 (95% UI: 1182.99-1403.57) that reduced by 1.72% (95% CI: -1.78 to -1.65) (Table 1, Table S14-15 )

Regional level

The ASPR of CRDs in 2019 was discovered to be highest in high-income North America (12449.57 [95% UI: 11351.77-13803.52]) and Australasia (10936.44 [95%UI: 9497.47-12696.54]) (Table 1, Table S1-2). By comparison, East Asia (4352.33 [95% UI: 3867.67-4978.69]) and Central Asia (4412.99 [95% UI: 4024.71-4927.71]) had the lowest ASPR (Table 1, Table S1-2). The ASMR in 2019 was found to be highest in Oceania(166.28 [95% UI: 133.34-202.63]) and South Asia(118.75 [95% UI: 97.56-135.84]), whereas, High-income Asia Pacific (12.31 [95%UI: 10.4-13.74]), Eastern Europe (16.23 [95% UI: 14.22-20.72]) and Central Europe (19.12 [95% UI: 16.63 to 22.05]) had the lowest rates(Table 1, Table S7-8). Meanwhile, the region with the highest ASDR in 2019 was Oceania(161.79[95% UI: 90.64-280.06]), and the region with the lowest rate was High-income Asia Pacific (14.99 [95% UI: 12.56 to 17.57]) (Table 1,Table S14-15 ).

During the three decades, the AAPC in ASPR of CRDs was different across GBD regions with High-income Asia Pacific (-1.81%[95% CI: -1.96 to -1.67]), Eastern Europe (-1.59% [95% CI: -1.66 to -1.51]), and Australasia (-1.02% [95% CI: -1.29 to -0.75]) having significant decreasing trends(Table 1, Table S1-2). In contrast, North Africa and Middle East (0.09% [95% CI: 0.05-0.12]) and Southern Latin America (0.06% [95% CI: -0.02 to 0.13]) had weak increasing trends(Table 1, Table S1-2). Regions with the largest decreasing AAPC trends in ASMR over the past decades included East Asia (-4.15% [95% CI: (-4.49 to -3.81]) and Eastern Europe (-2.90% [95% CI: -3.65 to -2.14]). In contrast, a substantially increasing trend was witnessed in High-income North America (0.47% [95% CI: 0.41-0.53]) (Table 1, Table S7-8). The AAPC of ASDR of High-income North America (0.16% [95% CI: 0.03 to 0.3]) and East Asia (-3.87 %[95% CI: -4.1 to -3.63]) during the period were at the poles of increasing and decreasing trends(Table 1,Table S14-15 ).

National Level

In 2019, the ASPR of CRDs varied notably between countries, such that USA (13030.37 [95% UI: 11908.32-14412.04]), UK (12151.98[95% UI: 10750.52-13846.11]), and Australia (11253.26[95% UI: 9736.76-13143.42]) had the three highest ASPR of all countries(Figure1A, Table S2). By comparison, countries with the lowest rates were Turkmenistan (3297.51 [95% UI: 2839.46-3828.59]), Mongolia (3331.34 [95% UI: 2978.47 to 3764.9]), and Estonia (3429.92 [95% UI: 3017.72-3934.08]) (Figure1A, Table S2). Meanwhile, the results were observed for ASMR in 2019, with Nepal (231.2 [95% UI: 175.79-270.35]), Papua New Guinea (209.49 [95% UI: 162.01-259.45]), and Solomon Islands (145.87 [95% UI: 118.53-169.97]) producing the highest rates and Montenegro (9.32 (95% UI: 7.48–10.91]), Latvia (9.92 [95% UI: 7.94–13.53]), Estonia (10.27[95% UI:8.01–13.09]), and Singapore (10.58 [95% UI: 8.82–14.07]) the lowest rates (Figure1B, Table S8). Papua New Guinea (4452.56 [95% UI: 3566.00-5534.57]) and Nepal (4339.27[95% UI: 3410.62-5078.79]) had the two highest ASDR among all countries in 2019. Conversely, countries with the lowest rates were Estonia (354.15[95% UI: 293.97-425.71]), Montenegro(374.22[95% UI: 302.81-459.92]), and Latvia(390.72[95% UI: 320.13-483.54]) (Figure1C, Table S15).

The AAPC in ASPR altered significantly between countries over decades, with Japan (-2.16% [95% CI: -2.31 to -2.02]), New Zealand (-2% [95%CI: -2.13 to -1.88]), and Singapore (-1.92% [95% CI: -2.04 to -1.8]) having the largest decreases. In contrast, Omen(1.09% [95% CI: 0.94 to 1.25]) and Saudi Arabia (0.93% [95% CI:0.77-1.09]) had increasing trends (Table S2). The AAPC in ASMR also altered across countries. The largest decreases existed in Singapore (-5.85% [95% CI: -6.17 to -5.53]) and Turkmenistan (-5.16% [95% CI: -5.82 to -4.50]). Instead, the largest increases were found in Belize (1.3% [95% CI: 0.69-1.92]), Nicaragua (1.1% [95% CI: 0.64-1.55]), and Cuba (0.99% [95% CI: 0.71-1.27]) (Table S8). Notably, the largest decreases in ASDR were found in Turkmenistan (-4.16% [95% CI: -4.71 to -3.60]), Singapore (-4.00% [95% CI: -4.20 to -3.80]) and China(-3.93% [95% CI: -4.17 to -3.68]). In contrast, Belize(0.70% [95% CI: 0.42-0.99]), Cuba (0.40% [95% CI: 0.27-0.54]), and Kazakhstan (0.37% [95% CI: -0.14 to 0.88]) had largest increasing trends (Table S15).

The data of prevalence, mortality and DALY of overall CRDs, COPD, asthma, pneumoconiosis, ILD&PS and other CRDs in global, regional and national levels can be seen in supplemental tables and figures. (Table S1-S6 prevalence; Table S7-S13 deaths; Table S14-S20 DALY; Figure S1-S5 national-)

Disease burden by age, sex, year and SDI

The detailed description was described in the supplementary materials.

Decomposition analysis by epidemiology drivers and cause of CRDs

A decomposition analysis of raw DALY was developed in order to explore what extent the forces, including aging, population growth and epidemiologic changes, shaped CRDs epidemiology (1990-2019) (Table S21). As a whole, there was an increase in CRDs DALY worldwide and in SDI regions except high-middle SDI, and it was most prominent in low-middle SDI region, which showed the largest increase (Figure 2A). Globally, population growth followed by aging contributed 202.17% and 172.91%, respectively, to the increased burden (Table S18). The aging contribution was most prominent in the middle-SDI region (532.71%), and decreased where it was 102.28% in the low-middle–, 66.27% in the middle, 96.64% in the high–, 5.73% in the low-SDI region and even negative contribution value in the high-middle SDI region (-297.03%). The contribution of population growth was similar (increase: middle-SDI (354.31%), low-SDI (179.16%),low-middle SDI (127.01%, high SDI (68.79%); decrease: high-middle SDI(-156.99%)).The epidemiologic changes have decreased globally, and it was least pronounced in high- and low-SDI regions, and more evident in middle-, high middle–, and low-middle–SDI regions (Figure 2A, Table S21). Decomposition analysis in GBD regions exhibited substantial heterogeneity. Especially, although most GBD regions revealed a decrease in epidemiologic changes—there was a GBD region (high-income North America) that showed a significant deviation from normal trend (Table S18). Simultaneously, as for aging, western, eastern and central Sub-Saharan Africa exhibited deviations and showed decreases. Central Europe and Eastern Europe showed a decrease trend in population growth that was contrary to the general trend.

Decomposition analyses by the five causes of CRDs were also developed (Table S21). From 1990 to 2019, COPD was the primary drivers of increased DALY worldwide and in SDI regions except high-middle SDI (Figure 2B). Globally, CRDs due to COPD and ILD&PS contributed 85.19% and 11.66%, respectively. Instead, asthma presented as a decreasing factor contributing -4.32%. Interestingly, high-middle SDI region showed a downward trend inversely, where COPD and asthma both played important roles in the decline, taking up 78.89% and 31.89%. However, in other SDI regions, COPD existed as a growing force. In the increasing SDI regions, the contribution of COPD was lowest in the low-SDI region(68.69%) and highest in the high-SDI region(97.73%) (Figure 2B and Table S21). A similar dual function was seen in asthma. In GBD regions, COPD was still the primary driver of change in CRDs DALY (Table S21), but its correlative contribution varied greatly geographically: it was high in High-income Asia Pacific (226.37%), and Western Europe (197.28%), and it was least pronounced in the Central Europe (20.84%).

Frontier analysis of CRDs

A frontier analysis was developed based on ASDR and SDI using data (1990-2019) so as to acquire a better realization of the potential improvement in CRDs DALY rates which are potentially achievable considering the national development status (Figure3 and Table S22). The top 15 countries with highest effective difference from the frontier (range of effective difference: 3599.56–1657.5) included Papua New Guinea, Nepal, Kiribati, Solomon Islands, Palau, Vanuatu, India, Lesotho, Nauru, Federated States of Micronesia, Myanmar, Marshall Islands, North Korea, Bhutan and Tuvalu. Compared to other countries, these owned disproportionally higher CRDs DALY rates with comparable socio-demographic resources. The frontier countries with low SDI (<0.5) and low effective difference included Somalia, Burkina, Ethiopia, Mozambique and Liberia. USA, Ireland, Netherlands, Denmark and United Arab Emirates were the examples with high SDI (>0.85) and relatively high effective difference at their development level.

Risk Factors

Smoking remained the leading factor in 2019 globally in comparison to the top-ranking RFs for ASMR in 1990. Meanwhile, household air pollution from solid fuels (HAPSF) lost his second place in 1990 and fell to third place in 2019, and ambient particulate matter pollution (APMP) took its second place. Generally, the ASMR attributable to RFs except high temperature decreased over three decades (Figure 4A). In all SDI regions（1990-2019）, smoking was the top one RF in accord with globe, though, which rapidly decreased especially in high-middle-, middle-, and low-middle-SDI regions (Figure 4A). Contrary to overall downtrend, the ASMR attributable to some RFs bucked the trend. For instance, the ASMR attributable to APMP, ambient ozone pollution (AOP) and high temperature had played increasingly important roles especially in low- and low-middle-SDI regions. Fortunately, the ASMR attributable to HAPSF dropped off dramatically in all SDI regions except high-SDI region, where it was insignificant at first and slightly decreased.
Globally, male had a higher ASMR, which could be attributed to all the above-mentioned RFs except high BMI (ratio＜1). The sex ratio for occupational carcinogens (>19) was much higher than other RFs’, indicating that occupational carcinogens was a non-negligible source of the sex-dependent differences (Figure 4B). The role of smoking came second, however there were downtrends of the ratios globally and SDI regionally except high-middle and middle-SDI regions. Meanwhile, the gaps enlarged in high-middle and middle-SDI regions among the some RFs (secondhand smoke, APMP, HAPSF, AOP and high&low temperature). It's worth noting that secondhand smoke did not just affect female, and it affected male more actually, with uptrends of ratios (apart from high SDI) indicating that the sex gap may further widen.

We know that the overall percent changes in death cases attributable to RFs were on the decline in most age groups and SDI regions over decades (figure 4C). The RFs of smoking in high SDI region and HAPSF in the other SDI regions played a crucial role in reducing trends. However, there came some RFs on the rise. At the global level, APMP, AOP, high body-mass index（BMI）and high temperature by all age groups were increased RFs sequentially. And high-BMI played a larger part in the youth periods. There was heterogeneity in the RFs contributing to deterioration in different SDI regions. For example, compared with general situation, high temperature in the high-SDI and secondhand smoke in the low- and low-middle SDI regions played unique roles.

The situation of predominant contribution of RFs to CRDs-related DALY similar to deaths approximately (Figure S12). Here we share a few differences: (1) Smoking was not the most significant RF for ASDR in low SDI regions until 2018, surpassing the RF of HAPSF; (2) Except that the ratio of high-BMI was less than 1 in middle- and low-middle SDI region, so was the factor of secondhand smoke.

The detailed percentages of mortality and DALY owing to CRDs attributable to RFs for 5 SDI regions and 21 GBD regions and attributable to RFs by age for both sexes in 1990 and 2019 were described in the supplementary materials (Figure S12,13).

Our results showed increasing trends for the number of prevalence, deaths and DALY due to CRDs. The most likely cause of the growth was mostly population growth and aging. Quite the contrary, the ASPR, ASMR and ASDR decreased, which can be owed to gradually improving treatments and interventions. However, ILD&PS showed increasing trends for ASPR, ASMR and ASDR(10), in contrast to other types, which may attribute to the increasing awareness and diagnosis level.

Interestingly, Australasia with the second highest ASPR, whose AAPC was the third fastest decline, showed that although Australasia's base value was large, the situation improved significantly. The virtuous circle may exist in Eastern Europe with the second lowest ASMR as well as the second significant decrease in AAPC. High-income North America was the only region with weak growth in ASDR AAPC, contrary to other regions with downward. Meanwhile, an uptrend of ASMR in High-income North America was also witnessed. In conclusion, there was heterogeneity in the performance of each GBD region, which requires local measures to solve the problem.

Consistent with previous studies(11–13), China or India tended to be NO.1 in absolute numbers of prevalence, mortality and DALY due to their huge population base. When the data of mortality and DALY were age-standardized, Nepal's and Papua New Guinea's top two spots were highlighted. As for ASPR, developed countries such as USA, UK and Australia led the way. Prevalence was still very high in developed countries, but thanks to good health systems, their ASMR and ASDR were relatively low. Most developing countries did the opposite (e.g., Nepal). Singapore's efforts and gains were seen in past 30 years, with decreased AAPC of all rates (ASPR, ASMR and ASDR). China's efforts were laudable concurrently (11, 14), although its absolute value was still high, whose AAPC of ASDR dropped significantly in the top three.

Hearteningly, the overall trends of CRDs were down from 1990 to 2019, regardless of SDI and ASR. These were thanks to the efforts of the WHO and various countries in prevention and treatment. When it came to specific SDI regions and disease levels, it was not always perfect. In low SDI region, the ASPR of COPD continued to climb over the past few years(5, 15). But of greater concern was the overall increase in ASR of ILD&PS in any SDI regions, obtained from this study and previous studies(10). The level of SDI is related to population health directly, and the huge gap between poverty and wealth in many parts has an effect on health equity(16). This deserves our attention and targeted assistance.

Positive correlations between the SDI and ASPR of overall CRDs, COPD, Asthma, ILD&PS were seen. Nevertheless, the ASMR and ASDR of CRDs, COPD and asthma were negatively associated with the SDI. It was proved that countries with a high SDI showed lower ASMR and ASDR even with higher ASPR, reflecting more accessible to medical care and health services. As for COPD, so what countries with low SDI have to do is reverse the uptrend, while these countries with high SDI have to control the huge cases.

Although there were general downward trends of ASDR AAPC in globe and SDI regions, our decomposition analyses based on raw value of increased DALY also obtained corresponding gains. The overall increase was actuated by aging and population growth in large part, but epidemiologic changes played negative roles. But there were always some regions that deviate from the general trend. Overall, the globe and most SDI regions showed raw DALY growth, except high-middle SDI region, a real double-reduction region(raw value and AAPC). Similarly, we developed decomposition analyses by the five causes. Globally, COPD was the leading drivers of increased DALY, but asthma was present as the only decreasing factor. But roles were not set in stone. COPD attributed to decline in high-middle SDI(15), and asthma boosted with uptrends in low and low-middle SDI region. These regions certainly deserve more attention where all causes were drivers for increased DALYs, showing general inability to cope with CRDs (17).

As for national development status, a frontier analysis was built derived from ASDR and SDI. Despite our analysis highlighted a formidable challenge, this study provided a more optimistic evaluation, suggesting unrealized opportunities to narrow the DALYs differences. Notably, those countries with low SDI existed as frontier countries, which showed surprisingly outstanding performances despite limited resources and conditions, and deserved to be set up as exemplars on optimization of health outcomes in low-resource environment. Inversely, some high SDI countries underperformed(e.g., USA). Future work should be undertaken to identify drivers of success in exemplar countries and forces hampering progress in laggard countries; addressing this knowledge gap will likely be useful in informing effort to alleviate the burden.

Although many measures and policy actions have been taken within and across countries(18, 19), the leading cause of CRDs mortality and DALY remained smoking globally in GBD 2019. Smoking not only affects the health of smokers, but also endangers other innocent people due to second-hand smoke(20). New assessments on the effects of exposure to second-hand smoke(21), and of new smoking forms(electronic cigarettes, for instance) (22), deserve heightened public awareness. Meanwhile, as for air pollution in ASMRs, APMP and HAPSF took second and third place, switching the order over decades. With regard to HAPSF, the percent of ASMR&ASDR surpassed smoking in low SDI regions in study, consistent with previous studies(23, 24). The burning of solid fuels(25–27), high population and limited medical support in countries with lower SDI have led to an uptrend in APMP(27, 28). In this study, the ASMR&ASDR attributable to high temperature increased over decades. Low temperature was just the opposite, although, its percentage was still higher. The shift might be attributed to climate change(16, 29). Previous studies have shown that high BMI had a great effect in CRDs (e.g., COPD and asthma) (30). High-BMI played a larger part in the young and middle-aged periods from this study.

Prior to this study, previous published papers clarified the disease burden to some extent. A systematic analysis(GBD 2017)performed by Xiaochen Li et al.(5) focused on the mortality, DALY and risk factors, without the prevalence analysis unfortunately. Some GBD CRDs Collaborators(1) co-created another systematic analysis (GBD 2017), adding the prevalence component, but its analysis was limited in GBD region, the association with SDI not further analyzed. Hui Gan et al.(6) performed a relatively recent article derived from GBD 2019, which only discussed incidence, mortality without national level as well as the association with SDI, sex and age. Our study updated comprehensive estimates of prevalence, mortality and DALYs attributable to CRDs by age, sex, region, and SDI in global, regional and national levels. Composition analysis and frontier analysis were introduced innovatively.

Although there is some progress in our study, the common limitation of GBD study specific to chronic respiratory diseases was inevitable(31, 32). Firstly, CRDs diagnoses require clear diagnostic criteria, which are often not standardized worldwide. And if estimates were all recorded in a standard procedure, uncertainty could be diminished(31). Secondly, CRDs definition is highly dependent on medical expertise and equipment, especially for ILD&PS, and it is likely that a significant source of geographic heterogeneity is the lack of adequate diagnosis in underdeveloped regions(5). Additionally, statistics systems and civil registration are pivotal sources of vital statistics for mortality rates(33), but the population coverage with these systems has been disappointing(5). These aspects deserve further attention in future GBD construction.

This study revealed that the ASR (ASPR, ASMR and ASDR) decreased over three decades, with aging and population growth, nevertheless, the number of global prevalence, mortality and DALY from CRDs continued to climb from 1990 to 2019. Meanwhile, geographical heterogeneity can be witnessed in the CRDs burden, which was closely relevant to the SDI level. Smoking and air pollution were the most predominant risk factors for mortality and DALY, equally important in other risk factors(non-optimal temperature, occupational exposure and high BMI). No matter where we are, we all have to receive more attention with supportive policies and strive forward, and we’ll all have a better future.

DALY: Disability adjusted life years; CRDs: chronic respiratory diseases; GBD: Global Burden of Diseases, Injuries, and Risk Factors Study/Global Burden of Disease Study; AAPC: average annual percent change; ASPR: Age-standardized prevalence rate; ASMR: Age-standardized mortality rate; ASDR: Age-standardized DALYs rate; ASR: Age-standardized rate; SDI: Socio-demographic index; UI: Uncertainty interval; CI: Confidence interval COPD: chronic obstructive pulmonary disease; ILD&PS: interstitial lung disease and pulmonary sarcoidosis; PNE: pneumoconiosis ; RF: risk factor; NCDs: non-communicable diseases; SDGs: Sustainable Development Goals; COVID-19: Corona virus disease 2019; ICD-10: International Classification of Diseases and Injuries-10; HAPSF: household air pollution from solid fuels; APMP: ambient particulate matter pollution; AOP: ambient ozone pollution; BMI: Body mass index;

Ethics approval and consent to participate: The GBD study’s protocol has been approved by the research ethics board at the University of Washington. The GBD shall be conducted in full compliance with University of Washington policies and procedures, as well as applicable federal, state, and local laws. All methods were carried out in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guidelines for reporting health estimates.

Data sharing: Data of the GBD study are publicly available at https://www.healthdata.org/results/data-visualizations.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the [Global Health Data Exchange GBD Results Tool] repository, [http://ghdx.healthdata.org/gbd-results-tool].

Competing interests

The authors declare that they have no competing interests.

Funding

GBD is supported by the Bill and Melinda Gates Foundation. This research work was supported by Natural Science Foundation of Zhejiang Province (LY19H290010). The funders were not involved in the collection, analysis, or interpretation of data, or in the writing or submitting of this report. The first author and corresponding author had full access to all data and final responsibility for the decision to submit for publication.

Authors' contributions

Xiang Chen, Yan-Fan Chen, Qing-Wei Zhang, Cheng-Wei Zhou conceived and designed the study. Xiang Chen, Qing-Wei Zhang, Yang-Yang Fu, Yao-Zhe Li, Lei Chen collected and analyzed the data. Xiang Chen, Qing-Wei Zhang, Yan-Fan Chen, Cheng-Wei Zhou drafted the manuscript; Xiang Chen, Qing-Wei Zhang, Yan-Fan Chen, Cheng-Wei Zhou revised the scientific and factual content of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We sincerely appreciate the great works by the Global Burden of Disease study 2019 collaborators.

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Table 1. Prevalence, mortality and DALYs of chronic pulmonary disease in 2019 for both sexes, all SDI quintiles and all regions, with AAPC from 1990 and 2019.

Prevalence(95% UI)

Mortality (95% UI)

DALYs(95% UI)

GBD data

NO.

(95% UI)

Age-standardized prevalence per 100000 population (95% UI)

AAPC % (95% CI) 1990-2019

NO.

(95% UI)

Age-standardized mortality per 100000 population (95% UI)

AAPC %

(95% CI)

1990-2019

NO.

(95% UI)

Age-standardized DALYs per 100000 population (95% UI)

AAPC %

(95% CI)

1990-2019

Global

454557390 (417354403 to 499144380)

5789.16

(5290.68 to 6418.14)

-0.64

(-0.7 to -0.58)

3974315

(3581757 to 4303823)

51.28

(45.9 to 55.51)

-1.92

(-2 to -1.84)

103533107 (94792077 to 112266452)

1293.74

(1182.99 to 1403.57)

-1.72

(-1.78 to -1.65)

COPD

212335951 (200422146 to 225097834)

2638.2

(2492.17 to 2796.14)

-0.34

(-0.4 to -0.28)

3280636 (2902855 to 3572367)

42.52

(37.63 to 46.31)

-1.93

(-2.03 to -1.84)

74432367 (68204127 to 80193347)

926.08

(848.76 to 997.67)

-1.82

(-1.9 to -1.74)

Asthma

262405182 (224047914 to 309452681)

3415.53

(2898.92 to 4066.2)

-0.91

(-1.04 to -0.79)

461069

(366580 to 559006)

5.8

(4.62 to 7.03)

-2.47

(-2.55 to -2.39)

21550977 (17141587 to 26971997)

273.63

(216.71 to 343.38)

-1.94

(-2.07 to -1.81)

ILD and PS

4710180

(4020397 to 5401700)

57.62

(49.42 to 65.67)

0.33

(0.25 to 0.4)

169833

(118756 to 204802)

2.17

(1.5 to 2.62)

0.73

(0.61 to 0.84)

3770894

(2864234 to 4468319)

46.45

(35.12 to 54.98)

0.44

(0.3 to 0.58)

Pneumoconiosis

3072550

(2596999 to 3596518)

36.78

(31.1 to 43.08)

-0.38

(-0.71 to -0.04)

23015

(20348 to 26159)

0.29

(0.26 to 0.33)

-2.6

(-2.74 to -2.46)

919077

(761478 to 1116127)

11.1

(9.23 to 13.45)

-2.03

(-2.19 to -1.87)

Other

/

39761

(31085 to 46581)

0.5

(0.39 to 0.59)

-0.65 (-0.8 to -0.49)

2859792

(2461295 to 3217791)

36.48

(31.41 to 41.1)

0.45

(0.4 to 0.51)

Sex

Female

231152957 (212559495 to 252660333)

5695.91

(5209.31 to 6282.15)

-0.67

(-0.73 to -0.62)

1740667

(1456918 to 1961313)

39.73

(33.24 to 44.75)

-1.92

(-2 to -1.84)

46526316 (41119608 to 51370913)

1093.03

(965.72 to 1208.96)

-1.63

(-1.7 to -1.56)

Male

223404433 (203679747 to 248407883)

5907.65

(5382.04 to 6581.16)

-0.63

(-0.71 to -0.55)

2233647

(2029782 to 2452532)

66.72

(60.55 to 73.06)

-1.97

(-2.06 to -1.89)

57006791 (51895441 to 62641862)

1538.69

(1399.47 to 1690.3)

-1.82

(-1.9 to -1.74)

SDI

High SDI

111890796 (103862564 to 120447230)

9110.33

(8211.2 to 10240.43)

-0.49

(-0.59 to -0.4)

525019

(453715 to 559024)

24.64

(21.49 to 26.07)

-0.73

(-0.8 to -0.66)

253581

(220120 to 281835)

924.07

(797.2 to 1067.03)

-0.63

(-0.68 to -0.57)

High-middle SDI

86144403 (78876826 to 94478706)

5296.89

(4736.23 to 6016.11)

-0.83

(-0.94 to -0.72)

651323

(580032 to 772576)

33.22

(29.45 to 39.31)

-3.2

(-3.42 to -2.97)

343436

(298812 to 389741)

837

(750.92 to 948.78)

-2.67

(-2.88 to -2.46)

Middle SDI

123815506 (112658899 to 138000392)

5264.17

(4747.44 to 5910.84)

-0.36

(-0.44 to -0.28)

1206232

(1062900 to 1340546)

59.82

(52.3 to 66.61)

-2.83

(-2.98 to -2.67)

790676

(693913 to 895867)

1316.93

(1199.96 to 1449.4)

-2.53

(-2.65 to -2.42)

Low-middle SDI

82481524 (75628261 to 91033633)

5417.1

(5005.46 to 5903.14)

-0.28

(-0.35 to -0.21)

1226245

(1031957 to 1380038)

107.29

(90.1 to 120.82)

-1.36

(-1.57 to -1.15)

882039

(739773 to 1015616)

2314.24

(2029.67 to 2562.08)

-1.44

(-1.68 to -1.2)

Low SDI

49928256 (43435735 to 58192038)

5549.6

(5101.56 to 6120.66)

-0.27

(-0.31 to -0.23)

363857

(310934 to 403593)

87.82

(74.17 to 97.67)

-0.95

(-1.16 to -0.73)

587801

(462330 to 721639)

2048.37

(1802.01 to 2247.14)

-1.05

(-1.21 to -0.89)

Region

High-income Asia Pacific

13524396 (12384865 to 14875991)

5181.21

(4471.8 to 6125.97)

-1.81

(-1.96 to -1.67)

72143

(59578 to 81146)

12.31

(10.4 to 13.74)

-2.37

(-2.52 to -2.22)

29669

(25177 to 35634)

468.37

(392.53 to 561.9)

-2.07

(-2.19 to -1.95)

High-income North America

52638477 (49184741 to 56555381)

12449.57 (11351.77 to 13803.52)

0.38

(0.09 to 0.67)

243409

(196086 to 260316)

36.24

(29.59 to 38.58)

0.47

(0.41 to 0.53)

124425

(100864 to 136499)

1374

(1180.95 to 1570.24)

0.16

(0.03 to 0.3)

Western Europe

49616742 (45577274 to 54278252)

8449.61

(7477.19 to 9614.06)

-0.9

(-1.1 to -0.71)

244861

(210219 to 263528)

22.45

(19.62 to 24.03)

-1.11

(-1.21 to -1)

88544

(76270 to 108721)

769.63

(660.74 to 900.99)

-1.06

(-1.16 to -0.97)

Australasia

3596396

(3240398 to 4002542)

10936.44

(9497.47 to 12696.54)

-1.02

(-1.29 to -0.75)

13599

(11348 to 15144)

24.82

(20.92 to 27.5)

-1.33

(-1.44 to -1.21)

7133

(6172 to 8387)

946.08

(793.37 to 1128.11)

-1.31

(-1.38 to -1.24)

Andean Latin America

3434185

(2812614 to 4232116)

5519.29

(4546.85 to 6761.35)

-0.59

(-0.72 to -0.46)

14209

(11012 to 17013)

26.84

(20.79 to 32.1)

-0.74

(-0.97 to -0.51)

20159

(15929 to 25834)

680.12 (559.51 to 811.82)

-1.45

(-1.6 to -1.29)

Tropical Latin America

15686977 (13554279 to 18289360)

7279.67

(6181.33 to 8633.32)

-0.69

(-0.77 to -0.6)

77698

(69788 to 86968)

34.1

(30.5 to 38.25)

-1.65

(-1.81 to -1.49)

67690

(57936 to 78207)

909.91 (808.6 to 1037.21)

-1.47

(-1.6 to -1.33)

Central Latin America

12802204 (11151611 to 14835688)

5309.65

(4606.64 to 6148.31)

-0.57

(-0.63 to -0.5)

74582

(62845 to 85574)

33.69

(28.27 to 38.66)

-0.8

(-0.97 to -0.64)

82170

(68279 to 95942)

784.43

(686.77 to 890.61)

-0.97

(-1.1 to -0.83)

Southern Latin America

6289607

(5692879 to 7053892)

8716.12

(7771.03 to 10037.53)

0.06

(-0.02 to 0.13)

27893

(24150 to 30844)

32.56

(28.24 to 35.95)

-0.04

(-0.36 to 0.28)

21955

(19087 to 25705)

942.1

(817.88 to 1086.82)

-0.2

(-0.3 to -0.1)

Caribbean

3643997

(3236341 to 4150963)

7804.39

(6858.66 to 8982.39)

-0.23

(-0.27 to -0.18)

13766

(11412 to 16209)

26.87

(22.23 to 31.67)

-0.05

(-0.21 to 0.11)

19939

(13568 to 31132)

960.2

(797.86 to 1118.46)

-0.35

(-0.42 to -0.28)

Central Europe

9408225

(8600853 to 10356267)

6405.17

(5664.26 to 7297.92)

-0.72

(-0.81 to -0.64)

42773

(37191 to 49286)

19.12

(16.63 to 22.05)

-2.22

(-2.37 to -2.06)

20066

(16782 to 23799)

678.01

(583.53 to 787.39)

-1.57

(-1.64 to -1.5)

Eastern Europe

10862111

(9826188 to 11974988)

4453.83

(3918.48 to 5167.66)

-1.59

(-1.66 to -1.51)

56496

(49473 to 71897)

16.23

(14.22 to 20.72)

-2.9

(-3.65 to -2.14)

35005

(29509 to 42466)

537.15

(467.11 to 633.55)

-2.4

(-2.85 to -1.94)

Central Asia

3671866

(3309535 to 4163424)

4412.99

(4024.71 to 4927.71)

-0.53

(-0.58 to -0.47)

22237

(20056 to 26149)

39.41

(35.47 to 46.52)

-0.62

(-1.03 to -0.21)

33594

(28232 to 44025)

937.22

(847.05 to 1056.72)

-0.99

(-1.24 to -0.73)

North Africa and Middle East

31776197 (28349827 to 36022813)

5891.24

(5320.98 to 6584.28)

0.09

(0.05 to 0.12)

128513

(110781 to 144351)

36.1

(30.9 to 40.31)

-1.38

(-1.51 to -1.25)

171495

(146350 to 197712)

1033.42

(906.68 to 1149.27)

-1.03

(-1.13 to -0.94)

South Asia

81003958 (74252202 to 88513792)

5366.27

(4957.78 to 5815.77)

-0.24

(-0.42 to -0.07)

1363402

(1125162 to 1563756)

118.75

(97.56 to 135.84)

-1.49

(-1.72 to -1.26)

964077

(799166 to 1127515)

2559.27

(2206.93 to 2879.03)

-1.4

(-1.55 to -1.24)

Southeast Asia

35926312 (32695055 to 39797804)

5651.09

(5157.94 to 6262.32)

-0.13

(-0.21 to -0.06)

267847

(233250 to 296152)

53.72

(46.49 to 59.45)

-1.83

(-1.92 to -1.74)

250854

(210194 to 313559)

1383.12

(1235.32 to 1511.65)

-1.57

(-1.64 to -1.51)

East Asia

72603642 (66122037 to 79486904)

4352.33

(3867.67 to 4978.69)

-0.72

(-0.89 to -0.55)

1126946

(969591 to 1362127)

67.48

(57.8 to 82.27)

-4.15

(-4.49 to -3.81)

350308

(285959 to 410212)

1270.89

(1120.48 to 1470.61)

-3.87

(-4.1 to -3.63)

Oceania

699604

(640666 to 766013)

6504.63

(6095.2 to 6939.03)

-0.52

(-0.55 to -0.48)

8940

(7142 to 11103)

166.28

(133.34 to 202.63)

-0.68

(-0.71 to -0.66)

18209

(10600 to 34090)

3677.62

(3020.87 to 4477.19)

-0.72

(-0.77 to -0.67)

Western Sub-Saharan Africa

17677345 (15043894 to 21372019)

4581.4

(4126.53 to 5168.81)

-0.57

(-0.7 to -0.43)

64223

(54089 to 74359)

39.14

(33.48 to 44.62)

-1.15

(-1.21 to -1.09)

292020

(221970 to 364887)

1128.76

(986.27 to 1272.71)

-1.02

(-1.1 to -0.95)

Eastern Sub-Saharan Africa

20536229 (17359042 to 24732550)

5486.86

(4919.39 to 6242.77)

-0.57

(-0.62 to -0.52)

60212

(51794 to 69611)

42.4

(36.86 to 48.27)

-1.39

(-1.43 to -1.35)

170760

(126011 to 223638)

1231.52

(1082.84 to 1392.41)

-1.37

(-1.41 to -1.32)

Central Sub-Saharan Africa

5276142

(4515014 to 6211222)

4876.92

(4434.13 to 5402.1)

-0.26

(-0.31 to -0.22)

26972

(18531 to 40753)

65.8

(44.51 to 104.69)

-0.99

(-1.09 to -0.89)

54480

(40954 to 69999)

1625

(1229.32 to 2246.8)

-1.01

(-1.11 to -0.91)

Southern Sub-Saharan Africa

3882778

(3173435 to 4588692)

5523.82

(4620.04 to 6421.33)

-0.28

(-0.58 to 0.01)

23593

(21463 to 26023)

49.21

(44.37 to 54.24)

-0.79

(-1.11 to -0.47)

37240

(32037 to 44600)

1387.64

(1263.76 to 1520.81)

-0.94

(-1.13 to -0.74)

Generated from data available from http://ghdx.healthdata.org/gbd-results-tool.

GBD, Global Burden of Disease; DALY, disability-adjusted life-year; UI, uncertainty interval, AAPC, average annual percent change, CI: confidence interval;

No competing interests reported.

Global, regional, and national burden of chronic respiratory diseases and associated risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

Status:

Version 2

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 2