Global patterns of asthma burden related to environmental risk factors during 1990-2019: An age-period-cohort analysis for Global Burden of Disease Study 2019

doi:10.21203/rs.3.rs-2483171/v1

Background: Change in asthma burden attributed to specific environmental risk factor has not been evaluated.

Objective: We aimed to explore the age, period, and cohort effects on asthma burden attributable to smoking and occupational asthmagens in different socio-demographic index (SDI) areas and the regional and gender differences.

Methods: Risk factor-specific asthma death and disability-adjusted life years (DALYs) rates were extracted from Global Burden of Disease study 2019, estimated by standard Combined Cause of Death Model and DisMod-MR 2.1 modeling tool. Age-period-cohort analysis was conducted to decompose age, period, and cohort effects on asthma burden.

Results: Smoking- and occupational asthmagens-related asthma death and DALYs rates dropped by >45% during 1990-2019. In 2019, Africa, South and Southeast Asia had higher asthma burden than other regions, and male had higher asthma burden than female. Among nearly all age groups, low-middle SDI region had the highest smoking-related asthma burden, and low SDI region had the highest occupational asthmagens-related asthma burden. Inverse “V” shaped trend was observed in the above regions with increasing age. For smoking-related asthma death and DALYs rates, the most significant improvement of period rate ratios (RRs) occurred in high SDI area, decreased from 1.67 (1.61, 1.74) to 0.34 (0.33, 0.36) and 1.61 (1.57, 1.66) to 0.59 (0.57, 0.61), respectively, as well as the cohort effect on smoking-related asthma burden. For occupational asthmagens-related asthma death and DALYs rates, the most sharply decrease of period and cohort RRs appeared in the high and high-middle SDI region, respectively. Low SDI area showed least progress in period and cohort RRs of smoking- and occupational asthmagens-linked asthma burden.

Conclusion: Smoking and occupational asthmagens-related asthma burden sharply decreases, but region and sex disparities exist. Policy makers from low SDI region should reinforce tobacco control and make more effort in occupational protection.

asthma

disability-adjusted life years

smoking

occupational asthmagens

age-period-cohort analysis

Asthma is a common chronic respiratory disease, especially in children and elders, leading to 461.1 thousand deaths and 21.6 million disability-adjusted life years (DALYs) in 2019 [1]. Prevalence of asthma in children was higher in developed countries than developing countries [2, 3], but most asthma-related death occurred in low- and middle-income countries. Benefiting from the use of inhaled corticosteroids, asthma mortality decreased over the last few decades worldwide. However, that in the low and low-middle socio-demographic index (SDI) regions remained well ahead of other regions [4]. Although asthma prevalence was low in the Sub-Saharan Africa and South Asia, these areas have higher asthma DALYs rates due to high risk of asthma death than other regions [5]. Relationship between SDI and asthma DALYs rate for all ages showed a decreasing trend as the SDI-level increasing [1]. Besides, there is sex disparity in asthma burden due to differences in environmental exposures, including smoking and occupational exposure [6].

According to the Global Burden of Disease (GBD) study 2019 on asthma, smoking and occupational asthmagens contributed 11.93% and 7.47% to the global asthma death, respectively; 9.9% and 8.8% to the global asthma DALYs, respectively [1]. Smoking is an important risk factor for asthma, and the causal association has been well documented [7]. High SDI countries have achieved outstanding tobacco control, but in the lower SDI countries, tobacco industry activity increased aggressively and the smoking rate rose rapidly [8]. Smoking causes corticosteroid insensitivity, which could affect asthma control. In addition, smokers with asthma have the accelerated decline of lung function that occurs in early adulthood and the higher prevalence of comorbidities, further increased asthma burden [9]. Approximately 25%-50% of occupational asthma had exacerbated respiratory symptoms associated with exposure to asthmagens in the workplace [10], like low-molecular-height compounds (mostly isocyanates) and high-molecular-weight agents (mostly flour) [11]. Difficulties in the diagnosis, poor awareness of occupational protection, and absence of well-defined occupational exposure limits led to underestimation and underreporting of occupational asthma [12, 13]. Recently, Safiri et al analyzed the global epidemic pattern of asthma, but age, period, and cohort effects were not clarified, as well as the changes in contribution of specific risk factor [1]. Bai et al conducted an age-period-cohort (APC) analysis of asthma death only in four countries (Brazil, Russia, China, and South Africa), global pattern of asthma DALYs was not evaluated, and they also did not perform risk factor-specific analyses [14]. Therefore, it is necessary to execute risk factor-specific APC analysis of asthma death and DALYs at global and regional levels, which would instruct public intervention in a precise manner.

This study aimed to evaluate the updated global patterns of asthma death and DALYs rates related to different environmental risk factors during 1990–2019. GBD 2019 used the standard Combined Cause of Death Model (CODEm) approach and the DisMod-MR 2.1 modeling tool to estimate risk factors attributable asthma death and DALYs rates at the global, regional, and country levels. The APC model was used to explore changes in the risk factor-specific asthma burden associated with age, period, and cohort effects in the worldwide and five SDI regions, which would help to evaluate the difference in the improvement of asthma burden across different SDI areas.

2.1 Data Sources

Data used in this study are from the GBD 2019 Study, which estimates the incidence, prevalence, death, and DALYs for 369 diseases and injuries in 204 countries and territories, and provides disease burden attributed to 87 risk factors [15]. Multi-source data were used to evaluate the distribution of asthma burden by age, sex, region, and year, including censuses, disease registries, epidemiological surveys, health service use, vital statistics, disease and risk factors surveillance systems, air pollution monitors, and other sources [16]. Detailed data sources are available on the GBD 2019 data input sources tool (https://ghdx.healthdata.org/gbd-2019/data-input-sources). The World Cancer Research Fund criteria have been used to evaluate credible or possible proof of risk-asthma pairs since 2010, and only the pairs with strong evidence were included. Asthma death and DALYs rates ascribed to smoking and occupational asthmagens were available from GBD 2019 database. Risk factor-specific asthma burden was explored in the whole world and countries and territories of different SDIs. Components of SDI included income per capita, the fertility rate in females under 25 years old, and average years of schooling for individuals aged 15 years and above. All the countries and territories were divided into quintile groups based on SDI, including low, low-middle, middle, high-middle, and high SDI (SDI quintiles were listed in Table S1, and countries and regions of different SDI groups were included in Table S2) [16, 17].

2.2 Definition and estimation of asthma burden

Asthma is defined as a doctor’s diagnosis and wheezing in the past year. The definition of asthma is based on the International Statistical Classification of Diseases and Related Health Problems (J45-J46 in ICD-10, and 493 in ICD-9). There were four alternative asthma definitions: self-reported asthma in the past year, self-reported lifetime asthma, only a doctor’s diagnosis in the past year, and only wheezing in the past year.

To estimate death due to asthma, GBD 2019 applied the CODEm approach. Death models were established for both sexes and age groups ranging from 1 to over 95 years old. DisMod-MR 2.1 was the main modeling tool for asthma and maximum remission was set as 0.3 (revealing the upper bound of the largest observed data) and no data for 0-0.5 age group because the diagnosis of asthma in young infants was unable. Log lag distributed income, asthma standardized exposure variables, healthcare services and quality index were included in the DisMod model as covariates to improve comprehensiveness and robustness of estimation (Table S3). The DALYs are the sum of years lived with disability and years of life lost due to premature death. The proportion of asymptomatic (36.2%), controlled (19.9%), partially controlled (20.6%) and uncontrolled asthma (23.3%) was estimated using data from USA Medical Expenditure Panel Surveys during 2000–2014, and the disability weights were assigned to 0, 0.015, 0.036, and 0.133, respectively (Table S4). Based on the population size and asthma prevalence of different SDI subgroups, total and severity-specific numbers of asthma cases were calculated, and then severity-specific disability weights were assigned to calculate the number of years lived with disability with comorbidity correction. Sex-specific death rates in the different age groups were estimated by using CODEm and then multiplied by the number of people in each age groups to obtain the number of deaths, and years of life lost could be calculated according to the life expectancy of the subgroup in the standard life table. In the maps showing age-standardized death and DALYs rates of asthma attributed to different risk factors, 204 countries and territories were stratified by sextile of risk factor-specific death and DALYs rates of asthma, respectively.

2.3 Definitions of Risk factors

Smokers including current smokers and former smokers. Current smokers were the individuals who occasionally or daily smoked, and former smokers were these who have quitted tobacco products for at least six months. Data for occupational risks were from the International Labor Organization, in which 22 occupational asthmagens were recorded (Table S5). Proportion of exposure to asthmagens of workers was estimated according to the population distribution of nine occupational groups [15].

2.4 Statistical Analysis

APC model was used to explore the age, period, and cohort effects on the asthma death and DALYs rates attributed to smoking and occupational asthmagens. Age effect showed variation in asthma burden with increasing age. Period effect represented the influence of technological factors on asthma burden, including screening method, disease definition, and treatment, which would affect all age groups. And cohort effect indicated the difference in asthma burden across different birth cohorts [18]. APC model expresses as follows:

Ln (Y_ij) = µ + α_i + β_j + γ_k

Here Y_ij represents risk factors-specific rates of asthma death and DALYs, α_i shows the age effect in the age group i, β_j represents the period effect in the period group j, and γ_k indicates the risk factors-specific rate ratio of asthma in the birth cohort k related to age group i and period group j (k = j - i), and µ denotes the intercept.

An online tool for APC analysis was provided by the National Institutes of Health (https://analysistools.cancer.gov/apc/) [18], and risk factors specific-asthma rates of death and DALYs were assumed to follow Poisson distribution. Intrinsic estimator was applied to address multicollinearity problem, which was built on singular value decomposition of matrices. Local drifts reflected annual percent change of age group-specific rates over time, and net drift reflected overall annual percent change. Age and period were both divided by 5-year interval and there were 13 (30–34 to 90–94) and 14 (15–19 to 80–84) age groups for smoking- and occupational asthmagens-related asthma death and DALYs rates, respectively. Six period groups were from 1990–1994 to 2015–2019. Birth cohort = period − age, 18 (1900–1904 to 1985–1989) and 19 (1910–1914 to 2000–2004) birth cohorts were produced for smoking- and occupational asthmagens-related asthma death and DALYs rates, respectively. The medians of period groups (2000–2004) and birth cohorts (1940–1944 for smoking; 1955–1959 for occupational asthmagens) were defined as reference, and rate ratios (RRs) of risk factors attributable asthma death and DALYs rates in the given period and birth cohort were calculated. Two-sided P < 0.05 was regarded as statistically significant.

3.1 Change of asthma burden during 1990–2019

Global death from asthma attributable to smoking and occupational asthmagens occurred at 54,850 [95% uncertainty interval (UI)=(29,150, 78,010)] and 34,400 [95% UI=(27,830, 42,610)] cases in 2019, respectively (Table S6). Age-standardized death percent of asthma related to smoking and occupational asthmagens declined by 23.01% and 7.79% (Table S7). Percent change for smoking- and occupational asthmagens-related asthma death rate was −62.43% (declined from 1.81 per 100,000 in 1990 to 0.68 per 100,000 in 2019) and −55.43% (declined from 0.92 per 100,000 in 1990 to 0.41 per 100,000 in 2019). The drop of asthma death rate attributable to each risk factor was higher in males than that in females. Furthermore, smoking- and occupational asthmagens-related asthma death rates experienced significant declines in all SDI regions during 1990–2019 (Table 1).

Global DALYs number of asthma attributable to smoking and occupational asthmagens occurred at 2.12 [95% UI=(1.13, 3.01)] million and 1.90 [95% UI=(1.51, 2.33)] million in 2019 (Table S8). Age-standardized DALYs percent of asthma related to smoking and occupational asthmagens declined by 29.52% and 5.62%, respectively (Table S9). Percent change for smoking- and occupational asthmagens-related asthma DALYs rate was −59.60% (declined from 63.21 per 100,000 in 1990 to 25.54 per 100,000 in 2019) and −45.93% (declined from 42.35 per 100,000 in 1990 to 22.9 per 100,000 in 2019), respectively. Compared with females, improvement of asthma DALYs rates attributable to each risk factor was greater in males. Moreover, smoking- and occupational asthmagens-related asthma DALYs rates showed effective descents in all SDI regions during 1990–2019. By contrast, the rates dropped very little in high SDI and middle SDI regions (Table 2).

Table 1

Change in age-standardized asthma deaths rate related to environmental risk factors from 1990 to 2019.
Categories	Smoking-related asthma deaths rate (per 100,000 population)			Occupational asthmagens-related asthma deaths rate (per 100,000 population)
Categories	1990	2019	Percent change	1990	2019	Percent change
Global	1.81 (0.94, 2.74)	0.68 (0.36, 0.96)	−62.43%	0.92 (0.65, 1.26)	0.41 (0.33, 0.51)	−55.43%
Sex
Male	3.44 (1.77, 5.21)	1.21 (0.65, 1.70)	−64.83%	1.37 (0.90, 1.98)	0.60 (0.45, 0.76)	−56.20%
Female	0.56 (0.28, 0.93)	0.23 (0.11, 0.37)	−58.93%	0.51 (0.32, 0.77)	0.24 (0.18, 0.31)	−52.94%
SDI region
High SDI	0.77 (0.44, 1.10)	0.14 (0.08, 0.21)	−81.82%	0.19 (0.18, 0.21)	0.06 (0.05, 0.07)	−68.42%
High-middle SDI	0.95 (0.54, 1.35)	0.24 (0.13, 0.33)	−74.74%	0.29 (0.25, 0.36)	0.08 (0.07, 0.10)	−72.41%
Middle SDI	1.95 (1.04, 2.90)	0.71 (0.39, 0.99)	−63.59%	0.78 (0.62, 1.04)	0.31 (0.26, 0.36)	−60.26%
Low-middle SDI	4.98 (2.37, 8.26)	1.90 (0.95, 2.86)	−61.85%	2.54 (1.63, 3.76)	1.04 (0.76, 1.40)	−59.06%
Low SDI	3.37 (1.49, 5.77)	1.60 (0.75, 2.57)	−52.52%	3.08 (2.12, 4.47)	1.47 (1.11, 2.06)	−52.27%
Abbreviations: DALYs, disability-adjusted life years; SDI, socio-demographic index.

Table 2

Change in age-standardized asthma DALYs rate related to environmental risk factors from 1990 to 2019.
Categories	Smoking-related asthma DALYs rate (per 100,000 population)			Occupational asthmagens-related asthma DALYs rate (per 100,000 population)
Categories	1990	2019	Percent change	1990	2019	Percent change
Global	63.21 (34.83, 90.44)	25.54 (13.57, 36.17)	−59.60%	42.35 (33.02, 53.52)	22.90 (18.21, 28.21)	−45.93%
Sex
Male	103.30 (56.27, 147.54)	40.29 (21.87, 56.08)	−61.00%	58.19 (43.04, 75.79)	30.25 (23.86, 37.37)	−48.02%
Female	28.90 (14.49, 43.18)	12.15 (6.02, 18.37)	−57.96%	27.42 (20.32, 35.61)	15.82 (12.28, 20.31)	−42.30%
SDI region
High SDI	67.10 (35.22, 100.67)	27.82 (13.57, 43.87)	−58.54%	32.98 (23.73, 45.11)	21.79 (14.87, 30.98)	−33.93%
High-middle SDI	42.93 (24.04, 59.99)	15.23 (8.00, 22.26)	−64.52%	21.77 (16.84, 28.12)	10.60 (7.70, 14.63)	−51.31%
Middle SDI	50.22 (27.82, 71.96)	21.55 (11.80, 29.81)	−57.09%	32.78 (26.68, 40.72)	17.27 (14.12, 21.19)	−47.32%
Low-middle SDI	119.38 (57.73, 187.96)	46.50 (23.59, 68.41)	−61.05%	84.37 (58.20, 116.10)	38.76 (30.68, 48.64)	−54.06%
Low SDI	83.27 (39.14, 132.04)	40.28 (19.21, 62.10)	−51.63%	107.40 (79.20, 142.81)	55.42 (43.79, 71.93)	−48.40%
Abbreviations: DALYs, disability-adjusted life years; SDI, socio-demographic index.

3.2 Global burden of asthma in 2019

As shown in Fig. 1, risk factors-related asthma death and DALYs rates had spatial heterogeneity. The top 2 sextiles of asthma death rate attributable to smoking (≥ 0.70 cases per 100,000 population) were seen in North and South Africa, South and Southeast Asia, Kazakhstan in Central Asia, and Mongolia in East Asia. The last two sextiles of asthma death rate attributable to smoking were discovered in Europe, Oceania and most American countries (Fig. 1A). Locations with ≥ 47 per 100,000 of DALYs attributable to smoking included South and Southeast Asia, Botswana, Egypt, Greenland, and Zimbabwe. With the exception of some parts of West and Central Africa, asthma DALYs rates attributable to smoking of most countries in Africa located in the 2nd and 3rd sextiles (23–46 per 100,000), as well as most European countries. Countries with DALYs rates attributable to smoking less than 13 per 100,000 were China, Russia and some countries in Latin America (Fig. 1B).

The highest proportions (≥ 0.2 cases per 100,000 population) of asthma death rates attributable to occupational asthmagens were seen in almost all of Africa, South and Southeast Asia. Moreover, death rates greater than ≥ 0.2 cases per 100,000 population were seen in Kazakhstan, Mongolia, North Korea. Most of Europe, Oceania and South America, as well as Canada and China, were at the last two sextiles (≤0.05 cases per 100,000 population, Fig. 1C). Locations in top sextiles of occupational asthmagens-related asthma DALYs rates (≥ 49 per 100,000 population) included Southeast Asian countries and Sub-Saharan Africa. The DALYs rates attributable to occupational asthmagens in Afghanistan, India, Pakistan, Uzbekistan, and parts of Africa ranged from 25 per 100,000 to 48 per 100,000, as did in European countries (such as Iceland, Ireland, Oceania and Sweden). The countries with low levels of DALYs rates attributable to occupational asthmagens (≤15 per 100,000 population) were in most countries in East and West Asia, most of the America, and some countries in North Africa (Fig. 1D). The asthma burden attributable to smoking and occupational asthmagents was more severe in males than in females. In contrast to male, females with top 2 sextiles of asthma DALYs rate attributable to smoking were from Argentina, Australia, Europe, Greenland and the United States (Figure S1-S2).

3.3 APC analysis for smoking-related asthma burden

Global net drifts in asthma death and DALYs rates attributable to smoking were −3.54% [95% confidence interval (CI)=(−3.61%, −3.47%)] and −3.34% [95% CI=(−3.40%, −3.28%)], respectively, and local drifts values were below −2.60% for all age groups (Table S10). Low-middle SDI region had the highest asthma death rate attributable to smoking across all the SDI groups, death rate linearly increased from 30–34 to 80–84 age groups (from 0.80 to 21.60 per 100,000), slightly decreased and then kept stable. The second highest asthma death rate attributable to smoking was observed in the low SDI region, pattern change in the low SDI region was similar to that in the low-middle SDI region despite an upward trend from 85–89 to 90–94 age groups, and peak value was 19.75 per 100,000 in the 80–84 age group. In the middle SDI region, smoking-related asthma death rate increased from 30–34 to 85–89 age groups, then decreased, and the rate was higher than the global level in people over 70 years. In comparison, the boost of asthma death rate attributable to smoking in the high-SDI group and the high-middle-SDI group was relatively moderate (Table 1 and Fig. 2A). In each age group, asthma death attributed to smoking in males was higher than those in females in the whole world and five different SDI areas (Figure S3A and S4A). There were nearly inverse “V” shaped trends of asthma DALYs rates attributable to smoking in the low-middle, low, and middle SDI areas, and the highest values were observed in the 70–74, 65–69, and 70–74 age groups, respectively (354.62, 292.60, and 151.93 per 100,000, respectively). Asthma DALYs rate attributable to smoking in the high-SDI group was higher in the 30–44 age groups and showed a monotonic downward trend after the 35–39 age group, a similar pattern occurred in the high-middle SDI region (Fig. 2B). Trends of different SDI subgroups for men were similar to the whole population, but women from the low SDI had the highest smoking-related asthma DALYs rate (Figure S3B and S4B). Asthma death and DALYs rate attributable to smoking decreased during the study period (Fig. 2C-2D, S3C-S3D, and S4C-S4D), the period RR decreasing from 1.32 [95% CI=(1.30, 1.34)] to 0.56 [95% CI=(0.55, 0.57)] and 1.34 [95% CI=(1.32, 1.36)] to 0.59 [95% CI=(0.58, 0.60)] globally. The most significant improvements were both in the high-SDI area, with period RR decreasing from 1.67 [95% CI=(1.61, 1.74)] to 0.34 [95% CI=(0.33, 0.36)] and 1.61 [95% CI=(1.57, 1.66)] to 0.59 [95% CI=(0.57, 0.61)], respectively. Inversely, the low-SDI area had the least improvement that period RR of death and DALYs rate declined from 1.20 [95% CI=(1.16, 1.24)] to 0.64 [95% CI=(0.61, 0.66)] and 1.19 [95% CI=(1.17, 1.21)] to 0.66 [95% CI=(0.64, 0.67)], respectively (Fig. 2C-2D). Additionally, the most conspicuous improvements in asthma death rates were observed in males from high-SDI regions during study period (Figure S3C). There was a downward trend for smoking-related asthma death risk; high SDI countries had the highest RR from 1900–1904 to 1935–1939 birth cohorts, but ranked near bottom after the 1945–1949 cohort. In contrast, low SDI countries had the lowest RR before the 1925–1929 cohort, but ranked highest after the 1950–1954 cohort (Fig. 2E). Asthma DALYs rate attributable to smoking had a similar pattern with asthma death across the studied birth cohorts (Fig. 2F). Sex-specific rate ratio of asthma death and DALYs also monotonically decreased from early to recent birth cohort, especially for males from high SDI countries (Figure S3E-S3F and S4E-S4F).

3.4 APC analysis for occupational asthmagens-related asthma burden

Global net drifts in asthma death and DALYs rates attributable to occupational asthmagens were −2.60% [95% CI=(−2.68%, −2.53%)] and −2.31% [95% CI=(−2.37%, −2.26%)], respectively, and local drifts were below −1.53% for all age groups (Table S11). Low SDI region, with the growing tendency from 15–19 to 60–64 age group (0.32 to 7.53 cases per 100,000) and then decreasing. Low SDI region had the highest asthma death rate attributable to occupational asthmagens, followed by the low-middle SDI region, and the rate of the remaining regions was below the global level. In the middle SDI region, occupational asthmagens-related death was higher in 50–79 age groups than other age groups, with death rates fluctuating around 1.25. In contrast, asthma death rates in the high and high-middle SDI areas were far below other areas and remained flat (Fig. 3A). The trends of males and females were broadly similar to that of the general population, and male adults from low and low-middle SDI areas had higher asthma death rates attributable to occupational asthmagens than female adults from the same area (Figure S5A and S6A). There were nearly inverse “V” shaped trends of asthma DALYs rates attributable to occupational asthmagens in the low, low-middle and middle SDI areas. In the low and low-middle regions, the highest values were both observed in the 60–64 age group, and the peak of middle SDI regions occurred in the 55–59 age group (237.26, 169.54, and 52.70 per 100,000, respectively). In the high SDI region, the highest occupational asthmagens-associated DALYs rates were in the 45–49 age group and then showed a monotonic downward trend, with a similar pattern occurred in the high-middle SDI region (Fig. 3B). The trends were roughly the same for both sexes, males had higher asthma DALYs rates due to occupational asthmagens than females (Figure S5B and S6B). In all SDI regions, the risks of death and DALYs rates attributable to occupational asthmagens decreased over time, and period risk was similarly controlled for both sexes (Fig. 3C-3D, S5C-S5D, and S6C-S6D). From 1990 to 2019, the period RR of asthma death rate owing to occupational asthmagens reduced from 1.48 [95% CI=(1.37, 1.59)] to 0.47 [95% CI=(0.43, 0.51)] in the high-SDI group and the RR of asthma DALYs rate reduced from 1.27 [95% CI=(1.24, 1.29)] to 0.55 [95% CI=(0.54, 0.56)] in the high-middle SDI group, which were the most significant downward trend of all SDI groups. On the contrary, the improvement in asthmagens-related asthma death rates was much smaller in low SDI area, with RR of deaths rate decreasing from 1.18 [95% CI=(1.14, 1.22)] to 0.63 [95% CI=(0.61, 0.65)] and RR of DALYs rate decreasing from 1.16 [95% CI=(1.15, 1.17)] to 0.66 [95% CI=(0.66, 0.67)] (Fig. 3C-3D). Cohort RRs decreased monotonically in all SDI regions (Fig. 3E-3F, S5E-S5F, and S6E-S6F). Similarly, the progress of cohort RR in high SDI area was more significant than low SDI area (Fig. 3E). The RR of asthma DALYs rate in the high-middle SDI area had the highest decline, from 6.73 [95% CI=(5.48, 8.28)] in 1910 − 1014 birth cohort to 0.28 [95% CI=(0.25, 0.32)] in 2000–2004 birth cohort (Fig. 3F). The pattern of period and cohort RRs of asthma death and DALYs in males and females was similar to that of the global population, but cohort RRs declined more in males than females (Figure S5C-S5F and S6C-S6F).

This was the first study to perform APC analysis of risk factors-specific (smoking and occupational asthmagens) asthma death and DALYs rates during 1990–2019 at global and regional levels. There were striking improvements in smoking- and occupational asthmagens-related asthma burden worldwide. Results from APC analysis showed that asthma death and DALYs rates attributable to smoking and occupational asthmagens sharply increased in the middle-aged adults and peak value occurred in the elders, and low and low-middle SDI regions had higher asthma death and DALYs rates than other regions in nearly all age groups. For the period and cohort RRs of smoking-related asthma burden, the most significant improvement occurred in high SDI area. High SDI area also showed the most significant improvements of asthma death rates attributable to occupational asthmagens, but high-middle SDI regions showed more dramatic improvements in these RRs of DALYs rates. Low SDI area showed least progress in period and cohort RRs of smoking- and occupational asthmagens-linked asthma death and DALYs rates than other areas. Besides, there was a sex disparity in asthma burden, male had higher asthma burden than female despite their similar pattern. Our findings show that tobacco control and occupational protection help to reduce asthma burden, and policymakers from lower SDI regions should reinforce tobacco control and make more effort in occupational protection.

Tobacco smoke is an essential risk factor for asthma burden, and the smoking-related asthma burden dropped by more than half in the past three decades. Tobacco smoke could deteriorate inflammation, aggravate respiratory virus infections, induce airway changes, and skew immune responses [19]. In 2019, asthma burden attributable to smoking was high in Southeast Asia and Africa, but the underlying reasons were different. Southeast Asia was one of the world's most extensive tobacco epidemic areas, and almost 50% of males and 40% of females consumed some forms of tobacco [20]. High smoking rates put them stuck at an enhancive risk of severe asthma symptoms and minor response to steroid therapy [21]. But in Africa, the increase in smoking rates and nicotine exposure, lack of trained medical staff and diagnostic apparatus, and inaccessible and unaffordable inhaled medications contributed to high smoking-related asthma death and DALYs rates [8, 22]. The burden of asthma attributable to smoking in men was similar to that in the general population, and gender differences in the pattern of women were correlated with the distribution of smoking rates, with the highest prevalence of smoking observed in women from Europe and Oceania [23]. According to our results from APC analysis, the asthma death rate increased with age and peaked in elders, which was similar to the previous studies [24]. Most smokers had a stable smoking pattern until later life [25], and underwent progressive and cumulative lung damage [26]. Together with aging-induced decline in immunity and lung function [27], elderly smokers were more likely to develop asthma and die. In all age groups, smoking-related asthma death risks in the middle, low-middle, and low SDI regions showed a similar pattern and were significantly higher than that in the high and high-middle SDI regions. Compared with other regions, smoking intensity was lower and smoking quit rate was higher in the high and high-middle SDI regions [23, 28]. Mattiuzzi et al showed that asthma DALYs attributed to smoking reached their peak between 55 and 70 years old in both men and women in the worldwide [29], and in our study, we found the highest global DALYs rate in the 60–64 age group in both sexes. Asthma DALYs attributed to smoking in low and low-middle SDI regions were mainly from YLLs, but YLDs accounted for a substantial part of DALYs in high and high-middle SDI regions. Asthma DALYs rate attributed to smoking was higher in younger populations from high SDI region than other regions, like Europe and the Americas, which might be associated with an earlier age of smoking initiation [30]. The substantial decline in period RR of smoking-related asthma burden may be related to global smoking prevalence declined since 1990, particularly after the passage of the World Health Organization (WHO) Framework Convention on Tobacco Control in 2005 [31]. Compared with large declines in the Americas and Europe, the Africa and Southeast Asia showed a net increase in male smokers since 2000 [32]. According to the WHO report on the global tobacco epidemic in 2019, 49 of 59 countries that have not taken any MPOWER steps (tobacco control strategies proposed by the WHO) were low-income and middle-income countries, which may lead to less decrease in period and cohort RRs in low-SDI group [33]. Robust tobacco control measures benefited the new birth cohort with a better environment and resulted in lower smoking-related asthma burden. In the future, we should reinforce tobacco control, prevent from initiating smoking in adolescents, and improve accessibility to inhaled medications, especially for people in Africa.

Occupational asthmagens are also important risk factor for asthma, and a survey of 13 countries reported that about 10%-25% of adult asthma was attributed to occupational factors [34]. In developing countries, such as Africa and South Asia, occupational asthma was the second most common occupational lung disease [35], and both occupational asthmagens-related asthma death and DALYs rates were high. In contrast to developed countries, developing countries did not vigorously monitor the workplace or set strict exposure limits. In addition, the constraint of medical resources resulted in healthcare providers not recognizing the key signs and giving definite diagnoses in the early stage of occupational asthma development. What’s worse, some clinicians did not ask about any potential occupational hazards [36]. Furthermore, our results indicated occupational asthmagens-related asthma DALYs rates were higher in Australia, Iceland and Sweden than other developed countries and some developing countries, which depended on the dormant industrial sectors. A review reported Scandinavian countries had higher incidence of occupational asthma than Western Europe, United States of America, South Africa, and Brazil [35]. Diisocyanates is the leading cause of occupational asthma in industrialized countries [37], and diisocyanate-related new cases of occupational asthma was estimated to be > 5000/year in the European Union [38]. We showed that people aged 60–64 years old had the highest asthma death and DALYs rate attributed to occupational asthmagens, which was similar with the previous studies [39], and asthma burden remained high after retirement, suggesting cumulative and lagged effect. After repeating exposure to lower levels of exposure (occupations like professional cleaners, pastry chefs, and oil sprayers), asthma developed insidiously over time [40]. Worse still was even if occupational asthma patients were removed from exposure, respiratory symptoms persisted and aggravated [41]. For occupational asthma death and DALYs rates, our findings demonstrated that the period and cohort RRs showed a declining trend in all regions. Promotion of respiratory protection precautions in industry, introduction of sensitive diagnostic methods, and implementation of occupational avoidance could make conspicuous improvement, and thus lead to sharp decrease in the burden of occupational asthma [37]. In 1970, the Occupational Safety and Health Administration was introduced in the USA, and it was the first occupational regulation. In 1978, the European Community conducted harmonizing measures to protect workers' health, and adopted a legal framework for workplace chemicals in 1980, setting a range of indicative exposure limits [42]. These policies have also achieved some results, for example, in France, the number of cases compensated for occupational asthma fell from 142 in 1991 to 23 in 2016 [37]. Besides, Australian Aluminium Council held regular health panel meetings since 1990, educated employees about occupational asthma and conducted regular health examination to reduce asthma burden⁴⁴. However, until 2010, Asian organizations did not have harmonized regulations on occupational exposure limit settings or workplace protection [37, 43, 44]. All these may explain why period and cohort RRs decreased more in the high and middle-high SDI areas than other areas. With the rapid development of industrialization in low SDI regions, the related occupational risk factors should be addressed. Creating a good working environment where the exposure value is below the defined hazard range, early recognition of workers with occupational symptoms and diminishment in occupational exposure may reduce the burden of occupational asthma. Although the burden of occupational asthma continued to decrease, there are also massive opportunities to achieve conspicuous improvement by maintaining and reinforcing control of asthmagens. There may be many neglected agents with strong stimulatory effects on the airway, and comprehensive recording and monitoring of traditional and novel irritants in the workplace are necessary.

The present study had several advantages. Firstly, we provided new insights into the global patterns of asthma death and DALYs rates related to environmental risk factors, which would benefit public health policy making and prevent asthma development in a precise manner. Secondly, we analyzed time trends of asthma burden in the five SDI regions, and corresponding findings helped to demonstrate regional disparity and difference in the decrease of asthma death and DALYs rates. Thirdly, the APC model was applied to decompose age, period, and cohort effects on asthma burden, and net and local drifts were generated to evaluate overall and age-specific annual percent changes. However, there were some limitations. Firstly, this study only carried out a descriptive analysis of GBD 2019 data and only two environmental risk factors were available. Other risk factors for asthma, such as air pollutants, environmental allergens, and respiratory viruses, were not included. Secondly, in the GBD 2019, definition of asthma was based on clinical assessment and/or self-report [16], so it is probably influenced by recall bias, acquisition of health services, and different origins of survey questions. Thirdly, because of the fixed and equal age and period intervals required by APC tool, data of people aged ≥ 95 years for smoking-related asthma death and DALYs rates and of people aged ≥ 85 years for occupational asthmagens-related asthma burden were not included in the analyses. And APC analyses for the smoking-related asthma burden of 0–29 age group and occupational asthmagens-related asthma burden of 0–14 age group were not performed due to data availability.

Smoking- and occupational asthmagens-related asthma burden dropped by more than 45% during 1990–2019. In 2019, there were region and sex disparities in environmental factors-related asthma burden, and higher burden was observed in Africa, South and Southeast Asia, as well as male. Peaks of smoking- and occupational asthmagens-related asthma burden occurred in the elders, suggesting cumulative and lagged effect of environmental exposures. The most conspicuous improvements in period and cohort effects on asthma burden appeared in areas with higher SDI indexes, and least improvements in low SDI region. Smoking cessation and provision of effective screening methods and treatment in high-risk groups may help to control asthma and reduce asthma burden, and vigorous measures would lead to more health benefit.

APC: age-period-cohort

CODEm: Combined Cause of Death Model

DALYs: disability-adjusted life years

GBD: Global Burden of Disease

RRs: rate ratios

SDI: socio-demographic index

Ethics approval and consent to participate：The GBD data are de-identified and publicly available. Therefore, the study is exempted from institutional ethical board review.

Consent for publication：Not applicable.

Availability of data and materials: All data could be extracted from in the online GBD repository, https://vizhub.healthdata.org/gbd-results.

Competing interests: All authors have declared that no competing financial interests exist.

Funding: This work was supported by the National Natural Scientific Foundation of China (No. 82204072), Anhui Provincial Natural Science Foundation (No. 2208085QH260), and Natural Science Foundation of the Higher Education Institution of Anhui Province, China (No. KJ2021A0229).

Author contributions: ZSY had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. ZSY and WXL designed the study, and wrote the first draft of manuscript. ZSY, GZS, WLH, ZHYM, GH, TFB, WXL contributed substantially to the study design, data analysis and interpretation, and the writing of the manuscript.

Acknowledgements: The authors thanked the University of Washington’s Institute for Health Metrics and Evaluation for sharing the data.

Safiri S, Carson-Chahhoud K, Karamzad N, Sullman MJM, Nejadghaderi SA, Taghizadieh A, et al. Prevalence, Deaths, and Disability-Adjusted Life-Years Due to Asthma and Its Attributable Risk Factors in 204 Countries and Territories, 1990–2019. Chest. 2022;161(2):318–29.
Patel SP, Järvelin MR, Little MP. Systematic review of worldwide variations of the prevalence of wheezing symptoms in children. Environ Health. 2008;7:57.
Masoli M, Fabian D, Holt S, Beasley R. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy. 2004;59(5):469–78.
Papi A, Brightling C, Pedersen SE, Reddel HK. Asthma Lancet. 2018;391(10122):783–800.
Prevalence and attributable health burden of chronic respiratory diseases. 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585–96.
Chowdhury NU, Guntur VP, Newcomb DC, Wechsler ME. Sex and gender in asthma. Eur Respir Rev. 2021;30(162):210067.
Piipari R, Jaakkola JJ, Jaakkola N, Jaakkola MS. Smoking and asthma in adults. Eur Respir J. 2004;24(5):734–9.
Chido-Amajuoyi OG, Fueta P, Mantey D. Age at Smoking Initiation and Prevalence of Cigarette Use Among Youths in Sub-Saharan Africa, 2014–2017. JAMA Netw Open. 2021;4(5):e218060.
Thomson NC, Polosa R, Sin DD. Cigarette Smoking and Asthma. J Allergy Clin Immunol Pract. 2022;10(11):2783–97.
Harber P, Redlich CA, Henneberger P. Work-exacerbated Asthma. Am J Respir Crit Care Med. 2018;197(2):1–2.
Maestrelli P, Henneberger PK, Tarlo S, Mason P, Boschetto P. Causes and Phenotypes of Work-Related Asthma. Int J Environ Res Public Health. 2020;17(13):4713–22.
Cullinan P, Vandenplas O, Bernstein D. Assessment and Management of Occupational Asthma. J Allergy Clin Immunol Pract. 2020;8(10):3264–75.
Toskala E, Kennedy DW. Asthma risk factors. Int Forum Allergy Rhinol. 2015;5(Suppl 1):11–6.
Bai J, Zhao Y, Yang D, Ma Y, Yu C. Secular trends in chronic respiratory diseases mortality in Brazil, Russia, China, and South Africa: a comparative study across main BRICS countries from 1990 to 2019. BMC Public Health. 2022;22(1):91.
Global burden. of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49.
Global burden. of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22.
Miethe S, Karsonova A, Karaulov A, Renz H. Obesity and asthma. J Allergy Clin Immunol. 2020;146(4):685–93.
Rosenberg PS, Check DP, Anderson WF. A web tool for age-period-cohort analysis of cancer incidence and mortality rates. Cancer Epidemiol Biomarkers Prev. 2014;23(11):2296–302.
Strzelak A, Ratajczak A, Adamiec A, Feleszko W. Tobacco Smoke Induces and Alters Immune Responses in the Lung Triggering Inflammation, Allergy, Asthma and Other Lung Diseases: A Mechanistic Review. Int J Environ Res Public Health. 2018;15(5):1033–67.
Rani M, Thamarangsi T, Agarwal N. Youth tobacco use in South-East Asia: Implications for tobacco epidemic and options for its control in the region. Indian J Public Health. 2017;61(Suppl 1):12–7.
Sprio AE, Ciprandi G, Riccardi E, Giannoccaro F, Carriero V, Bertolini F, et al. The influence of smoking on asthma in the real-life. Respir Med. 2020;170:106066.
Mariner DC, Ashley M, Shepperd CJ, Mullard G, Dixon M. Mouth level smoke exposure using analysis of filters from smoked cigarettes: a study of eight countries. Regul Toxicol Pharmacol. 2011;61(Suppl 3):39–50.
Spatial temporal. demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet. 2021;397(10292):2337–60.
Li X, Cao X, Guo M, Xie M, Liu X. Trends and risk factors of mortality and disability adjusted life years for chronic respiratory diseases from 1990 to 2017: systematic analysis for the Global Burden of Disease Study 2017. BMJ. 2020;368:m234.
Mathew AR, Garrett-Mayer E, Heckman BW, Wahlquist AE, Carpenter MJ. One-Year Smoking Trajectories Among Established Adult Smokers With Low Baseline Motivation to Quit. Nicotine Tob Res. 2017;20(1):50–7.
Oelsner EC, Balte PP, Bhatt SP, Cassano PA, Couper D, Folsom AR, et al. Lung function decline in former smokers and low-intensity current smokers: a secondary data analysis of the NHLBI Pooled Cohorts Study. Lancet Respir Med. 2020;8(1):34–44.
Cho SJ, Stout-Delgado HW. Aging and Lung Disease. Annu Rev Physiol. 2020;82:433–59.
Chow CK, Corsi DJ, Gilmore AB, Kruger A, Igumbor E, Chifamba J, et al. Tobacco control environment: cross-sectional survey of policy implementation, social unacceptability, knowledge of tobacco health harms and relationship to quit ratio in 17 low-income, middle-income and high-income countries. BMJ Open. 2017;7(3):e013817.
Mattiuzzi C, Lippi G. Estimation of Worldwide Burden of Smoking-Related Asthma. Int Arch Allergy Immunol. 2020;181(4):292–5.
Reitsma MB, Flor LS, Mullany EC, Gupta V, Hay SI, Gakidou E. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and initiation among young people in 204 countries and territories, 1990–2019. Lancet Public Health. 2021;6(7):e472–e81.
Smoking prevalence and attributable disease burden. 195 countries and territories, 1990–2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet. 2017;389(10082):1885–906.
Peruga A, López MJ, Martinez C, Fernández E. Tobacco control policies in the 21st century: achievements and open challenges. Mol Oncol. 2021;15(3):744–52.
The Lancet Respiratory M. Smoking cessation efforts should target LMICs. Lancet Respir Med. 2019;7(9):721.
Kogevinas M, Zock JP, Jarvis D, Kromhout H, Lillienberg L, Plana E, et al. Exposure to substances in the workplace and new-onset asthma: an international prospective population-based study (ECRHS-II). Lancet. 2007;370(9584):336–41.
Jeebhay MF, Quirce S. Occupational asthma in the developing and industrialised world: a review. Int J Tuberc Lung Dis. 2007;11(2):122–33.
Moitra S, Bælum J, Murgia N. Respiratory diseases in the marginal labour sectors: a call to action. Lancet Respir Med. 2017;5(5):370–2.
Coureau E, Fontana L, Lamouroux C, Pélissier C, Charbotel B. Is Isocyanate Exposure and Occupational Asthma Still a Major Occupational Health Concern? Systematic Literature Review. Int J Environ Res Public Health. 2021;18(24):13181.
Rother D, Schlüter U. Occupational Exposure to Diisocyanates in the European Union. Ann Work Expo Health. 2021;65(8):893–907.
Global and regional burden of chronic respiratory disease. in 2016 arising from non-infectious airborne occupational exposures: a systematic analysis for the Global Burden of Disease Study 2016. Occup Environ Med. 2020;77(3):142–50.
Tiotiu AI, Novakova S, Labor M, Emelyanov A, Mihaicuta S, Novakova P, et al. Progress in Occupational Asthma. Int J Environ Res Public Health. 2020;17(12):4553.
Le Moual N, Carsin AE, Siroux V, Radon K, Norback D, Torén K, et al. Occupational exposures and uncontrolled adult-onset asthma in the European Community Respiratory Health Survey II. Eur Respir J. 2014;43(2):374–86.
Ding Q, Schenk L, Malkiewicz K, Hansson SO. Occupational exposure limits in Europe and Asia–continued divergence or global harmonization? Regul Toxicol Pharmacol. 2011;61(3):296–309.
Donoghue AM, Frisch N, Ison M, Walpole G, Capil R, Curl C, et al. Occupational asthma in the aluminum smelters of Australia and New Zealand: 1991–2006. Am J Ind Med. 2011;54(3):224–31.
Salvi SS, Apte KK, Dhar R, Shetty P, Faruqi RA, Thompson PJ, et al. Asthma Insights and Management in India: Lessons Learnt from the Asia Pacific - Asthma Insights and Management (AP-AIM) Study. J Assoc Physicians India. 2015;63(9):36–43.

No competing interests reported.

Supplementalmaterial.pdf

Global patterns of asthma burden related to environmental risk factors during 1990-2019: An age-period-cohort analysis for Global Burden of Disease Study 2019

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1 Data Sources

2.2 Definition and estimation of asthma burden

2.3 Definitions of Risk factors

2.4 Statistical Analysis

3. Results

3.1 Change of asthma burden during 1990–2019

3.2 Global burden of asthma in 2019

3.3 APC analysis for smoking-related asthma burden

3.4 APC analysis for occupational asthmagens-related asthma burden

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1