Global burden and trends of tracheal, bronchus, and lung cancer attributed to occupational exposure to polycyclic aromatic hydrocarbons in regions with different sociodemographic index, 1990–2021

Jiansheng Lin; Xiaowei Xie; Xinyang Zheng; Haizhan Shi

doi:10.1371/journal.pone.0342250

. 2026 Feb 12;21(2):e0342250. doi: 10.1371/journal.pone.0342250

Global burden and trends of tracheal, bronchus, and lung cancer attributed to occupational exposure to polycyclic aromatic hydrocarbons in regions with different sociodemographic index, 1990–2021

Jiansheng Lin ¹, Xiaowei Xie ^1,^*, Xinyang Zheng ¹, Haizhan Shi ¹

Editor: Igor Burstyn²

PMCID: PMC12900364 PMID: 41678518

Abstract

Background

Occupational exposure to polycyclic aromatic hydrocarbons (PAHs) is a known risk factor for tracheal, bronchus, and lung (TBL) cancer. However, evidence of its global burden particularly across different socio-demographic index (SDI) regions has been limited.

Methods

Based on results from the global burden of disease (GBD) study, we conducted a comprehensive analysis of age-standardized death rates (ASDR) and age-standardized disability-adjusted life-years (DALYs) rates due to TBL cancer attributed to occupational exposure to PAHs. This study examined the trends, sex differences, age-specific burden, and regional disparities in TBL cancer burden attributed to occupational PAH exposure from 1990 to 2021 globally and across different SDI regions. Age-period-cohort analysis was also performed to evaluate the influence of age, cohort, and period effects.

Results

Globally, both ASDR and DALYs rates showed slight increases from 1990 to 2021, with estimated annual percentage changes (EAPCs) of 0.76% and 0.54%, respectively. Low and middle SDI regions experienced more significant increases in death rates and health burden, while high SDI regions exhibited declining trends. Age-specific analyses revealed higher death rates in older populations, particularly those aged 55–74 years, with rising trends observed in low and middle SDI regions. For high SDI regions, younger age groups (<60 years) showed declining trends, while older age groups (>75 years) showed increasing trends. Age-period-cohort analyses indicated that the period effect contributed substantially to rising death rates in low and middle SDI regions, while high SDI regions experienced slower increases in the period effect.

Conclusions

The study highlights a widening disparity in the burden of TBL cancer due to occupational exposure to PAHs, with lower SDI regions facing greater increases in death rates and DALYs, especially among older populations. Nevertheless, given the inherent limitations of GBD estimation methods and data scarcity in LMICs, the observed disparities should be interpreted with caution and warrant further primary research.

Introduction

Tracheal, bronchus, and lung (TBL) cancer is one of the leading causes of cancer-related mortality and morbidity worldwide [1,2]. According to the Global Burden of Disease (GBD) study, TBL cancer consistently ranks among the top contributors to cancer-related deaths and burden [3]. TBL cancer poses a significant burden on healthcare systems and economies, particularly in low- and middle-income countries (LMICs) [4]. An increasing number of studies have reported disparities in incidence and survival rates driven by occupational and socio-economic factors [5,6], which underscore the need for a more comprehensive understanding of the occupational factors influencing the TBL cancer burden.

Polycyclic aromatic hydrocarbons (PAHs) are a group of hazardous, lipophilic compounds generated during the incomplete combustion of organic materials [7,8]. PAHs are carcinogens known to increase the risk of TBL cancer [7,9,10]. Occupational exposure to PAHs occurs in industries such as coal mining, petroleum refining, and road paving, where workers are routinely exposed to high concentrations of PAHs [8,11]. Chronic exposure to PAHs has been linked to DNA damage, oxidative stress, and inflammatory responses, all of which contribute to the progression of TBL cancer [7,9,12,13].

Previous studies examining the health burden of TBL cancer have predominantly focused on outdoor air pollution (e.g., emissions from vehicles and industrial activities) and other occupational hazards (e.g., asbestos, silica) [6,14–16]. However, occupational PAH exposure, a significant although overlooked risk factor, remains underexplored at the global scale to date. Related evidence would provide critical insights for occupational health policies aimed at reducing PAH exposure and associated cancer burden.

In addition, socio-economic status significantly influences the health burden of TBL cancer [17,18]. Meanwhile, regions with lower socio-economic development often face higher exposure to environmental and occupational hazards, leading to increased health disparities [18,19]. While existing research highlights the role of socio-economic status in explaining global TBL cancer disparities [17–20], evidence linking socio-economic status to occupational PAH exposure-related TBL cancer burden remains limited. Addressing this gap is essential for understanding inequities in occupational health and guiding targeted interventions.

This study aimed to provide a detailed evaluation of the global burden of TBL cancer attributable to occupational PAH exposure, particularly across regions with different socio-economic levels, using data from the GBD study. Our study aimed to support the development of evidence-based policies that reduce TBL cancer disparities and improve occupational health globally.

Methods

Study design and population

The GBD provides global insights into the prevalence and mortality of diseases, injuries, and risk factors, allowing for comparisons across age groups, sexes, regions, and periods [21]. The national-level study data of GBD can facilitate comparison of health outcomes across countries, enabling researchers and decision-makers to identify leading health challenges and is particularly important for understanding the impact of risk factors [21].

The input data for the GBD study is compiled from a variety of sources, including hospitals, surveys, and government databases. This data is processed and standardized to create consistent, comparable estimates of disease burden across different regions. Regular updates to these estimates ensure that the GBD remains the most accurate and current source of global health data [21]. All data used in this study were publicly available online from the GBD study (https://vizhub.healthdata.org/gbd-compare/).

Occupational exposure to PAHs and TBL cancer

Data on occupational exposure to PAHs were derived from the GBD study (https://vizhub.healthdata.org/gbd-compare/), with detailed information on data sources and methodology publicly available online (https://www.healthdata.org/gbd/methods-appendices-2021/occupational-risk-factors).

In brief, occupational exposure to PAHs was quantified within the GBD framework using a multi-step process integrating economic activity classifications, occupation distributions, and exposure risk levels [22]. The input data were primarily sourced from the International Labor Organization (ILO). Occupational PAH exposure levels (high/low) were categorized based on the 17 International Standard Industrial Classification (ISIC) economic activities and 9 International Standard Classification of Occupations (ISCO) occupational categories. According to the GBD framework, the Theoretical Minimum Risk Exposure Level (TMREL) of occupational exposure to PAHs was set to zero (no thresholds). High-exposure industries (e.g., coal mining, asphalt production) were assigned elevated exposure rates using the CARcinogen Exposure database and expert-derived thresholds, while low-exposure industries (e.g., education, finance) received lower rates [22,23].

For this study, cancer mortality data were sourced from both cancer registries and the cause of death database. Cancer registry data from 2019 were supplemented with additional data. The inclusion criteria for cancer registries were stringent, focusing on population-based registries that reported data for all cancer types, age groups (excluding pediatric cancers), and both sexes. Priority was given to registries with national coverage, except where the GBD study provides reliable subnational estimates [24,25]. More detailed information is provided online at https://www.healthdata.org/.

Metrics and variables

In this study, we included death rate and disability-adjusted life years (DALYs) to characterize the health burden of TBL cancer [2,21,23]. Age-standardized death rate (ASDR) represents the death rate attributed to TBL cancer due to occupational exposure to PAHs, adjusted for potential age differences across populations. DALYs is a comprehensive measure of disease burden by combining both years of life lost (YLLs) due to premature death and years lived with disability (YLDs) due to TBL cancer [21]. One DALY is equivalent to one year of healthy life lost, making it a universal metric to compare the health impact of different diseases across different populations and time frames [21]. Both ASDR and DALYs estimates are reported with 95% uncertainty intervals (UIs), which reflect the certainty of these estimates. The 95% UI is derived by running the estimation model 1,000 times, each time sampling from distributions for data inputs, model choices, and data transformations [21].

The Socio-Demographic Index (SDI) is used to categorize regions with different levels of development in this study [26,27]. SDI combines three factors, income per capita, average years of schooling for individuals aged 15 and older, and the total fertility rate (TFR) for females under age 25 into a single index score ranging from 0 to 100. The data were obtained directly from the GBD study, which aggregates data from standardized international sources, including the World Bank, Demographic Health Surveys, and others. The GBD study provides complete SDI values for all regions and time periods covered in this analysis, with no missing data requiring imputation or adjustment. According to the scores, the SDI regions were categorized into low, low-middle, middle, high-middle, and high SDI regions. Higher SDI values indicate regions with higher levels of socio-economic development [26,27].

Methodology to calculate attributable burden in the GBD framework

To quantify the disease burden attributable to a specific risk factor, the GBD study used a validated comparative risk assessment (CRA) framework [23]. In brief, the first step is quantifying the relative risks (RRs) of the health outcome (TBL cancer) as a function of exposure to the risk factor (occupational PAHs). This was done using a meta-regression in a “burden of proof” approach, which synthesizes data from systematic reviews. Second, exposure data are projected to the global scale from various sources using Bayesian statistical models, specifically spatiotemporal Gaussian process regression. Third, by integrating projected exposure levels, attributable burden could be calculated to quantify the proportional change in TBL cancer burden that would occur if occupational PAH exposure was reduced to the TMREL. Fourth, the GBD framework accounts for the joint effects of risk factors by assuming that RRs are multiplicative. For risk factors without mediating pathways, such as occupational PAH exposure and confounding factors (e.g., smoking and other occupational carcinogens), their independent contributions were calibrated to avoid overestimation of joint effects [23]. More detailed descriptions of the statistical methods, input data, and exposure-response functions are publicly accessible online (https://ghdx.healthdata.org/record/ihme-data/gbd-2021-burden-by-risk-1990-2021).

Data collection and processing

Data was gathered from the GBD 2021 result tools. Occupational exposure to PAHs occurs primarily during working-age years (15–75 years). However, due to a prolonged induction-latency period of typically over 10 years from initial exposure to cancer diagnosis [28,29], the attributable mortality burden is observed at older ages. In this study, we collected death and DALYs attributed to TBL cancer due to occupational exposure to PAHs, across specific years (1990–2021), ages (from 25 years to >95 years, at 5-year intervals), sexes, all countries, 21 GBD regions, and different SDI regions. No disease burden was reported for ages <25 years in the GBD 2021 result tools and was not included in the final analyses.

Statistical analyses

The estimated annual percent change (EAPC) was calculated to quantify the rate of change in ASDR and age-standardized DALYs rate, as well as sex and age-specific death rate and DALYs rate between 1990 and 2021. The EAPC was derived using a log-linear regression model [21,27]. The 95% uncertainty intervals (UIs) for each EAPC were calculated using bootstrapping methods, providing a range of values that reflect the uncertainty in the estimate [21].

The J-point method was used to identify turning points in the temporal trends of TBL cancer burden due to occupational exposure to PAHs, specifically to detect shifts from decreasing to increasing trends (or vice versa) [30,31]. This method involves fitting a series of segmented regression models to identify the point at which the trend of the annual changes most significantly [30,31].

To capture the non-linear association between SDI and ASDR and age-standardized DALYs rate of TBL cancer attributed to occupational exposure to PAHs, we conducted the local polynomial regression (Loess) fit [32,33]. The loess fit does not assume a specific functional form for the relationship between SDI and ASDR/DALYs, allowing for a more flexible representation of the local association between SDI and ASDR and DALYs [32,33].

In addition, to characterize the effects of age, cohort, and period on TBL cancer death rates, an age-period-cohort (APC) model was applied [34,35]. This model helps separate the influence of the age effect (the age at which individuals are with higher risks), the generation or cohort effect (the impact of birth year on risks), and the period effect (the impact of time-specific changes on risks) [34,35]. It is mathematically expressed as $Y_{i j k} = α + f (A_{i}) + g (P_{j}) + h (C_{k}) + ε_{i j k}$ , where $Y_{i j k}$ represents the outcome, α is the intercept, $f (A_{i})$ captures the age effect, $g (P_{j})$ accounts for period effects, $h (C_{k})$ denotes cohort effects, and $ε_{i j k}$ is the error term. To estimate these effects, the APC model employs the intrinsic estimator method to deal with the collinearity in the cohort constraint equation and allows for robust estimation of independent effects while addressing the identifiability problem [34,35].

All statistical analyses were performed using Stata (version 16) and R (version 4.3.2).

Results

Trends of TBL cancer burden attributed to occupational exposure to PAHs in regions with different SDI from 1990 to 2021

As shown in Table 1, globally, the ASDR for TBL cancer due to occupational exposure to PAHs showed a slight increase from 0.05 (95% UI: 0.04, 0.06) per 100,000 in 1990 to 0.07 (95% UI: 0.06, 0.08) per 100,000 in 2021, with an EAPC of 0.76% (95% UI: 0.68, 0.84). The age-standardized DALYs rate increased from 1.65 (95% UI: 1.37, 1.95) per 100,000 in 1990 to 1.98 (95% UI: 1.63, 2.40) per 100,000 in 2021, with an EAPC of 0.54% (95% UI: 0.46, 0.62), indicating an increasing trend in the burden of disease.

Table 1. Burden and trends of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990-2021.

Region	ASDR (per 100,000) and 95% UI			Age-standardized DALYs (per 100,000) and 95% UI
Region	1990 year	2021 year	EAPC (%)	1990 year	2021 year	EAPC (%)
Global	0.05 (0.04, 0.06)	0.07 (0.06, 0.08)	0.76 (0.68, 0.84)	1.65 (1.37, 1.95)	1.98 (1.63, 2.40)	0.54 (0.46, 0.62)
Low SDI	0.04 (0.04, 0.05)	0.03 (0.03, 0.04)	0.44 (0.36, 0.52)	1.23 (1.05, 1.43)	0.92 (0.79, 1.06)	0.39 (0.30, 0.47)
Low-middle SDI	0.07 (0.06, 0.09)	0.11 (0.08, 0.13)	1.28 (1.22, 1.35)	2.38 (1.93, 2.85)	3.07 (2.44, 3.83)	1.19 (1.13, 1.25)
Middle SDI	0.01 (0.01, 0.02)	0.01 (0.01, 0.02)	0.85 (0.78, 0.93)	0.39 (0.30, 0.54)	0.46 (0.36, 0.58)	0.61 (0.53, 0.68)
High-middle SDI	0.02 (0.02, 0.03)	0.03 (0.03, 0.04)	1.05 (0.92, 1.18)	0.69 (0.56, 0.88)	0.98 (0.81, 1.17)	0.74 (0.60, 0.88)
High SDI	0.07 (0.06, 0.08)	0.09 (0.07, 0.12)	−0.72 (−0.79, −0.66)	2.21 (1.81, 2.65)	2.71 (2.17, 3.36)	−0.93 (−1.00, −0.85)

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Note: ASDR, age-standardized death rate; DALYs, disability adjusted life-years; EPAC, estimated annual percentage change; SDI, socio-demographic index; UI, uncertainty interval.

The burden of TBL cancer due to occupational exposure to PAHs varied across regions with different SDI levels, with regions having low and middle SDI showing a more significant increase in both ASDR and age-standardized DALYs rate over time. For instance, in low-middle SDI regions, the ASDR increased from 0.07 (95% UI: 0.06, 0.09) per 100,000 in 1990 to 0.11 (95% UI: 0.08, 0.13) per 100,000 in 2021, with an EAPC of 1.28% (95% UI: 1.22, 1.35). In contrast, high SDI regions showed decreasing trends in ASDR and DALYs rate, with EAPCs of −0.72% (95% UI: −0.79, −0.66) and −0.93% (95% UI: −1.00, −0.85), respectively, indicating decreasing trends over the past 30 years.

We also observed sex differences in the burden of TBL cancer attributed to occupational exposure to PAHs. Generally, males exhibited higher ASDR and age-standardized DALYs rates compared to females (S1 Table). However, the EAPCs for ASDR and age-standardized DALYs rates were higher in females compared to males globally and across regions with different SDI levels, indicating a more apparent increasing trend over time.

Fig 1 further shows the region-specific EAPCs of ASDR and age-standardized DALYs rate of TBL cancer attributed to occupational exposure to PAHs between 1990 and 2021. As for ASDR of both sexes, Solomon Islands, Egypt, and Kenya showed the highest increasing trends, with EAPCs of 4.51% (95% UI: 4.15%, 4.87%), 4.03% (95% UI: 3.52%, 4.54%), 3.58% (95% UI: 3.48%, 3.68%), respectively. Meanwhile, Kazakhstan, Ukraine, and Estonia showed the strongest decreasing trends, with EAPCs of −3.40% (95% UI: −3.55%, −3.25%), −3.34% (95% UI: −3.61%, −3.06%), −3.09% (95% UI: −3.27%, −2.92%), respectively.

Map data were obtained from Resources and Environmental Science Data Platform (public domain, publicly accessible at https://www.resdc.cn/).

Fig 2 shows the annual change in ASDR of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI levels between 1990 and 2021. For both sexes combined, global trends showed a gradual increase in ASDR, although with notable inflection points in 2012 and 2015. Constantly increasing trends were shown in females, while a decreasing trend was shown after 2012 in males. As for disparities across SDI levels, in regions with low SDI, the ASDR increased significantly, especially after 2008. Middle SDI and high-middle SDI regions showed apparent increasing trends after 2015. Conversely, high SDI regions exhibited apparent declines since 1990 and particularly after 2006. For males, middle and high-middle SDI regions showed downward trends after about 2010, and high SDI regions exhibited a decreasing trend since 1990. Nevertheless, for males, only high SDI regions showed a decreasing trend after 2010. The DALYs rate generally showed similar trends, as shown in S1 Fig.

Age-specific TBL cancer burden attributed to occupational exposure to PAHs in regions with different SDI

Fig 3 presents age-specific death rates of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI levels for the years 1990 and 2021. For both sexes, the death rates in 2021 showed a clear shift, with higher rates observed in older age groups compared to 1990, particularly in those aged 55–74 years. Fig 4 further shows the age-specific EAPCs of death rates of TBL cancer attributed to occupational exposure to PAHs. For both sexes, regions with low and middle SDI levels showed upward trends in death rates across all age groups, with higher EAPCs observed in older age groups. In contrast, high SDI regions exhibited different trends, with younger age groups (<60 years) showing negative EAPC values, while older age groups (particularly >75 years) showing positive EAPC values (>0). Similar trends were observed for both females and males, with low and middle SDI regions experiencing more significant increases in death rates over time compared to high SDI regions, which showed declining trends in younger populations and increasing trends in older populations.

Fig 4 — **(A)** Both; **(B)** Female; **(C)** Male. Note: EPAC, estimated annual percentage change; SDI, socio-demographic index.

S2 and S3 Figs also present generally similar trends for age-specific DALYs rate and EAPCs. These results indicated a widening gap across regions with different SDI levels, with low and middle SDI regions experiencing greater increases in death rates and health burden over time, particularly in the older population.

Associations between SDI and TBL cancer burden attributed to occupational exposure to PAHs in 21 GBD regions

Figs 5 and S4 show the associations between ASDR and age-standardized DALYs rate of TBL cancer attributed to occupational exposure to PAHs and SDI across different GBD regions from 1990 to 2021. For both sexes combined, we observed an inverted U-shaped relationship between SDI and both ASDR and DALYs rate. Specifically, ASDR and DALYs initially increased with rising SDI (e.g., in East Asia and South Asia), peaking at an SDI value of around 0.6 to 0.7, before declining in high-SDI regions such as North America and Australasia. The strongest increasing trend was observed in East Asia.

Fig 5 — **(A)** Both; **(B)** Female; **(C)** Male. Note: ASDR, age-standardized death rate; SDI, socio-demographic index.

For males, a similar trend was observed, but the peak ASDR occurred at a slightly lower SDI value (approximately 0.6). Notably, declining trends were observed in Southern Latin America and Central Asia, where ASDR decreased. In contrast, for females, the declining trends in high-SDI regions were less pronounced, suggesting a stable burden in these areas.

APC analysis for TBL cancer burden attributed to occupational exposure to PAHs in regions with different SDI

Fig 6 further shows the effects of age, period, and cohort on the death rates for regions with varying SDI levels from 1990 to 2021. Globally, the ASDR peaked in 55–74 age groups, with an upward trend in the period effect from 1990 to 2021. Notably, the period effect displayed regional variation across different SDI levels. In low and middle SDI regions, the period effect showed a sharp increase, particularly in the most recent years, indicating a rise in death rates linked to occupational exposure to PAHs. In contrast, high SDI regions also exhibited an increasing period effect, but at a slower rate compared to regions with lower SDI. Similar trends were also observed in the APC analyses for DALYs, as shown in S5 Fig.

Discussion

This study for the first time conducted a comprehensive evaluation of the global burden and trends of TBL cancer attributed to occupational exposure to PAHs from 1990 to 2021, with a focus on disparities across different SDI regions. By examining variations in ASDR, age-standardized DALYs rates, age-specific patterns, and the associations between SDI and TBL cancer burden, our findings provide critical insights into the temporal and regional differences in TBL cancer burden associated with occupational exposure to PAHs. The results highlighted a widening disparity in the burden of TBL cancer due to occupational exposure to PAHs, with lower SDI regions facing greater increases in death rates and DALYs, especially among older populations. By identifying populations at heightened risk, our findings provide key evidence to support targeted occupational health interventions and policymaking efforts aimed at reducing occupational exposure to PAHs and related TBL cancer burden. Addressing these disparities is essential for mitigating inequities in occupational health and advancing global cancer prevention efforts. Nevertheless, it is important to note that the comparisons across SDI regions are based on these modelled estimates, and the observed trends should be interpreted considering potential variability in data quality and completeness underlying the models, particularly in LMICs.

Globally, the ASDR and age-standardized DALYs rate of TBL cancer attributed to occupational PAH exposure have increased slightly over the past three decades. However, the trends vary markedly across SDI levels. Regions with low and middle SDI experienced significant increases in both ASDR and DALYs rates, likely due to higher occupational exposure and limited implementation of preventive measures in these areas [6,36,37]. For instance, the ASDR in low-middle SDI regions increased by 1.28% annually, while high SDI regions showed decreasing trends with an EAPC of −0.72% for ASDR and −0.93% for DALYs rates. Geographic disparities were also observed. Countries such as the Solomon Islands, Egypt, and Kenya demonstrated the most pronounced increases in ASDR. These findings emphasize the need for region-specific interventions and policies aimed at mitigating TBL cancer burden associated with PAHs.

Our age-specific analyses revealed a clear trend of increasing death rates of TBL cancer attributed to occupational exposure to PAHs in older age groups, particularly among individuals aged 55–74 years, with notable disparities across regions with different SDI levels. In low and middle SDI regions, this pattern was more pronounced, with rising trends observed across all age groups. This finding could be attributed to several factors. First, older populations in these regions may have experienced prolonged exposure to occupational PAHs due to delayed implementation or absence of strict workplace safety regulations [38–40]. Additionally, limited access to healthcare resources and early detection programs in low and middle SDI regions likely contributes to increasing TBL cancer burden [41,42]. In contrast, in high SDI regions, we observed declining trends among younger age groups (<60 years) and rising trends among older populations (>75 years). This dichotomy may to some extent reflect historical shifts in occupational exposure, as improved workplace safety regulations and stricter environmental policies in high SDI regions likely reduced PAH exposure for the younger [40,43].

Although males experienced higher ASDRs and DALYs attributable to occupational PAH exposure, females showed larger increasing trends indicated by higher EAPCs. This observed pattern may be driven by a confluence of socioeconomic, occupational, and biological factors. First, shifting occupational demographics play a role. While female workforce participation in formal sectors with potential PAH exposure has increased, gender-based occupational segregation often places women in different roles within these industries (e.g., administrative or support functions versus direct production labor) [22,44]. More critically, in many LMICs, women are disproportionately represented in the informal economy, including waste picking, small-scale food processing using solid fuels, and home-based manufacturing, where high-intensity PAH exposures are common and outside regulatory frameworks [45]. This unregulated exposure likely contributes substantially to the rising trend. Second, the influence of correlated risk factors, particularly active smoking, warrants consideration [10,23,46]. As highlighted by a study among the ten most populous countries, while tobacco-associated lung cancer mortality rates have declined among males in most countries, they have increased among females from 1990 to 2019 [46]. Notably, historical smoking trends have differed by sex, with female smoking prevalence peaking later than male prevalence in many regions [10,47]. Consequently, recent trends in female lung cancer burden likely reflect the combined effects of both risk factors. Although the GBD framework adjusts for smoking independently [23], residual confounding or interaction between smoking and occupational PAHs at the population level may influence the observed sex-specific trends. Third, biological susceptibility may amplify the trends. A previous pooled analysis of 14 case-control studies in Europe and Canada also reported higher risks of lung cancer for ever‑exposed women (OR=1.20) versus men (OR=1.08), despite lower median cumulative PAH exposure levels among women [44]. Experimental evidence also showed that women might be more susceptible to PAH-related toxicity due to biological differences. Studies have reported higher levels of oxidative stress biomarkers and genotoxic effects in exposed women than in men at similar exposure levels [48]. Furthermore, female lung tissue exhibits higher expression of CYP1A1 and greater accumulation of PAH–DNA adducts, suggesting enhanced metabolic activation of PAHs in women [49]. These biological susceptibilities may amplify the health impact of even modest occupational exposures, contributing to the more significant increasing trends observed in females.

Our analysis identified an inverted U-shaped relationship between SDI and TBL cancer burden, which highlights complex interactions between socioeconomic development and TBL cancer burden associated with occupational exposures to PAHs. The observed inverted U-shaped relationship between SDI and PAH-attributable TBL cancer burden aligns with the framework of the epidemiological transition of occupational risk. In this paradigm, the population-level burden peaks when the pace of industrialization and resultant exposure outstrip the development of protective regulatory frameworks and healthcare capacity. This is evident in low- to middle-SDI regions, where expansion in high-exposure sectors (e.g., manufacturing, construction, informal industry) has driven increased occupational PAH exposure [50,51], while safeguards such as ventilation controls and personal protective equipment remain inadequate. This trend is particularly pronounced in regions such as East Asia, where accelerated industrialization in the late 20th century coincided with the delayed adoption of workplace safety standards [52,53]. The high burden observed today in middle-SDI regions is largely attributable to ongoing, intensive exposures from current industrial activities. In contrast, the contemporary burden in high-SDI regions primarily stems from exposures accumulated in the past, when these regions were at a similar developmental stage. The declining burden trends now seen in high-SDI settings represent the delayed benefit of occupational health policies, technological improvements, and economic shifts implemented in prior decades [38,40].

The APC analyses further provided additional insights into the temporal dynamics of TBL cancer burden attributed to occupational PAH exposure. It should be noted that the interpretation of cohort patterns is inherently limited by the 30-year study observation window and the multi-decadal latency of TBL cancer, meaning the observed cohort effect integrates exposure experiences over a much longer historical timeframe. Particularly, the strong and increasing period effect in low- and middle-SDI regions quantifies the persistent risk from present-day industrial exposure [39,40,54]. In high-SDI regions, a more modest period effect coupled with elevated risk in older cohorts captures the enduring impact of past exposure regimes [55,56]. Meanwhile, the declining cohort effect observed globally suggests that preventive measures may be reducing risk for more recent generations [56]. Together, these APC findings demonstrate that the inverted U-shaped curve is not merely a cross-sectional disparity but a dynamic signature of industrialization waves, exposure histories, and the delayed translation of TBL cancer burden attributed to occupational PAH exposure.

To reduce the global burden of TBL cancer attributable to occupational PAH exposure, policymakers should prioritize interventions that align with the socio-economic and industrial contexts of regions at different SDI levels. Particularly, in low- and middle-SDI regions, where industrialization often outpaces occupational safety infrastructure, governments should enforce stricter workplace exposure limits for PAHs and the use of personal protective equipment. Efforts should prioritize exposure controls within high-risk industries (e.g., small-scale manufacturing, brick kilns, and informal waste recycling) and practical interventions (e.g., improving natural ventilation, providing access to affordable respiratory protection, and delivering basic occupational safety training) [55]. Globally, harmonizing standards should focus on promoting the ratification and implementation of relevant ILO conventions (https://www.ilo.org/), specifically the Occupational Safety and Health Convention (No. 155) and the Chemicals Convention (No. 170), supported by technical cooperation for monitoring and enforcement. Successful PAH exposure reduction strategies from high-SDI settings, such as the use of enclosed processes and local exhaust ventilation in coke oven operations, offer transferable models that could be adapted to local industrial conditions [55,57]. Furthermore, strengthening local data systems is essential to move beyond reliance on modelled estimates. Establishing linked occupational cancer and exposure registries in sentinel industrial zones of middle-SDI countries would generate locally relevant evidence to directly guide and evaluate policy. Finally, given the rising disease trends among females, integrating gender-specific protections such as targeted safety protocols in female-dominated occupations would also be potentially helpful to mitigate related health burden.

This study is the first comprehensive analysis of the global burden of TBL cancer attributed to occupational PAH exposure, incorporating data from the GBD study across the globe and different SDI regions. Our analyses across SDI regions highlighted critical disparities that can inform targeted interventions. However, several limitations should be noted. First, a significant limitation of this study is its reliance on the modelled estimates of the GBD study. While the GBD employs robust methodologies to synthesize data and address gaps, the accuracy of its estimates is contingent on the quality and coverage of underlying source data. Substantial under-reporting of PAH exposures and TBL cancer diagnoses in many LMICs is a recognized issue, potentially leading to underestimation of the burden [23]. Meanwhile, the ecological nature of the analysis could not fully consider the confounders at the individual level (e.g., tobacco smoking, other occupational carcinogens), which means that the direction and magnitude of net bias are difficult to determine precisely [19]. Therefore, the observed disparities across SDI regions should be interpreted considering potential uncertainties. Second, the lack of more detailed data on exposure duration, intensity, and variability limits our ability to evaluate dose-response relationships comprehensively [22]. Third, differences in healthcare access and diagnostic capabilities across regions and SDI levels may lead to underdiagnosis or misclassification of TBL cancer and may potentially bias the estimates. Fourth, the GBD framework aggregates occupational exposure across broad industry categories and does not allow further analysis by specific occupations. Consequently, our results could only reflect the average burden among all workers and do not capture the elevated risks faced by the heavily exposed jobs, such as asphalt workers or coke oven workers. Fifth, while the GBD framework used techniques to adjust major risk factors as independent contributors, it could not fully account for potential interactions between occupational PAHs and other exposures, such as outdoor air pollution and co-occurring occupational carcinogens (e.g., asbestos, silica). Sixth, the interpretation of long-term trends in attributable burden can be influenced by changes in other competing risk factors over time. Shifts in the other major risk factors may alter the underlying population at risk for lung cancer, which could affect trends in attributable burden estimates independent of changes in lung cancer burden attributable to occupational PAH exposure.

Conclusions

The study highlights a widening disparity in the burden of TBL cancer due to occupational exposure to PAHs, with lower SDI regions facing greater increases in death rates and DALYs, especially among older populations. The results underscore the significance of targeted public health interventions in low- and middle-SDI regions to mitigate TBL cancer burden attributed to occupational risks.

Supporting information

S1 Table. Burden and trends of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI by sex, 1990–2021.

(PDF)

pone.0342250.s001.pdf^{(128.2KB, pdf)}

S1 Fig. Annual change in DALYs rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990–2021.

(A-C) Global; (D-F) Low SDI; (G-I) Low-middle SDI; (J-L) Middle SDI; (M-O) High-middle SDI; (P-R) High SDI. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s002.pdf^{(3.8MB, pdf)}

S2 Fig. Age-specific DALYs rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990–2021.

(A-C) 1990; (D-F) 2021. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s003.pdf^{(1.7MB, pdf)}

S3 Fig. EAPC of age-specific DALYs rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990–2021.

(A) Both; (B) Female; (C) Male. Note: DALYs, disability adjusted life-years; EPAC, estimated annual percentage change; SDI, socio-demographic index.

(PDF)

pone.0342250.s004.pdf^{(1.3MB, pdf)}

S4 Fig. Associations between age-standardized DALYs rate of TBL cancer attributed to occupational exposure to PAHs and SDI in GBD regions, 1990–2021.

(A) Both; (B) Female; (C) Male. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s005.pdf^{(1.1MB, pdf)}

S5 Fig. Age-period-cohort analysis for DALYs rate of TBL cancer attributed to occupational exposure to PAHs.

(A) Global; (B) Low SDI; (C) Low-middle SDI; (D) Middle SDI; (E) High-middle SDI; (F) High SDI. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s006.pdf^{(3MB, pdf)}

S1 Data. Minimal anonymized dataset.

(CSV)

pone.0342250.s007.csv^{(17.1MB, csv)}

Acknowledgments

The authors would like to thank the GBD 2021 Collaborators.

Data Availability

The GBD 2021 data used in this study are publicly available online at https://gbd2021.healthdata.org/gbd-results?params=gbd-api-2021-permalink/8f6fe3b99b879062f3cbfe46014c3935. The minimal anonymized dataset necessary to replicate the study findings is provided in the Supporting information (S1 Data).

Funding Statement

The author(s) received no specific funding for this work.

References

1.Deng Y, Zhao P, Zhou L, Xiang D, Hu J, Liu Y, et al. Epidemiological trends of tracheal, bronchus, and lung cancer at the global, regional, and national levels: a population-based study. J Hematol Oncol. 2020;13(1):98. doi: 10.1186/s13045-020-00915-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Kuang Z, Wang J, Liu K, Wu J, Ge Y, Zhu G, et al. Global, regional, and national burden of tracheal, bronchus, and lung cancer and its risk factors from 1990 to 2021: findings from the global burden of disease study 2021. EClinicalMedicine. 2024;75:102804. doi: 10.1016/j.eclinm.2024.102804 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Global Burden of Disease 2019 Cancer Collaboration, Kocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncol. 2022;8(3):420–44. doi: 10.1001/jamaoncol.2021.6987 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Khanmohammadi S, Saeedi Moghaddam S, Azadnajafabad S, Rezaei N, Esfahani Z, Rezaei N, et al. Burden of tracheal, bronchus, and lung cancer in North Africa and Middle East countries, 1990 to 2019: results from the GBD study 2019. Front Oncol. 2023;12:1098218. doi: 10.3389/fonc.2022.1098218 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Deng Y, Peng L, Li N, Zhai Z, Xiang D, Ye X, et al. Tracheal, bronchus, and lung cancer burden and related risk factors in the United States and China. Am J Transl Res. 2021;13(4):1928–51. [PMC free article] [PubMed] [Google Scholar]
6.Li H, Guo J, Liang H, Zhang T, Zhang J, Wei L, et al. The burden of trachea, bronchus, and lung cancer attributable to occupational exposure from 1990 to 2019. Front Public Health. 2022;10:928937. doi: 10.3389/fpubh.2022.928937 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Moorthy B, Chu C, Carlin DJ. Polycyclic aromatic hydrocarbons: from metabolism to lung cancer. Toxicol Sci. 2015;145(1):5–15. doi: 10.1093/toxsci/kfv040 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Zainal PNS, Alang Ahmad SA, Abdul Aziz SFN, Rosly NZ. Polycyclic aromatic hydrocarbons: occurrence, electroanalysis, challenges, and future outlooks. Crit Rev Anal Chem. 2022;52(4):878–96. [DOI] [PubMed] [Google Scholar]
9.Holme JA, Vondráček J, Machala M, Lagadic-Gossmann D, Vogel CFA, Le Ferrec E, et al. Lung cancer associated with combustion particles and fine particulate matter (PM2.5) - The roles of polycyclic aromatic hydrocarbons (PAHs) and the aryl hydrocarbon receptor (AhR). Biochem Pharmacol. 2023;216:115801. doi: 10.1016/j.bcp.2023.115801 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Olsson AC, Fevotte J, Fletcher T, Cassidy A, ’t Mannetje A, Zaridze D, et al. Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: a multicenter study in Europe. Occup Environ Med. 2010;67(2):98–103. doi: 10.1136/oem.2009.046680 [DOI] [PubMed] [Google Scholar]
11.Ifegwu OC, Anyakora C. Polycyclic aromatic hydrocarbons: part I. Exposure. Adv Clin Chem. 2015;72:277–304. doi: 10.1016/bs.acc.2015.08.001 [DOI] [PubMed] [Google Scholar]
12.Stading R, Gastelum G, Chu C, Jiang W, Moorthy B. Molecular mechanisms of pulmonary carcinogenesis by polycyclic aromatic hydrocarbons (PAHs): implications for human lung cancer. Semin Cancer Biol. 2021;76:3–16. doi: 10.1016/j.semcancer.2021.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Letelier P, Saldías R, Loren P, Riquelme I, Guzmán N. MicroRNAs as potential biomarkers of environmental exposure to polycyclic aromatic hydrocarbons and their link with inflammation and lung cancer. Int J Mol Sci. 2023;24(23). [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Mogaraju JK. Machine learning strengthened prediction of tracheal, bronchus, and lung cancer deaths due to air pollution. Environ Sci Pollut Res Int. 2023;30(45):100539–51. doi: 10.1007/s11356-023-29448-y [DOI] [PubMed] [Google Scholar]
15.Lu J, Zhao X, Gan S. Global, regional and national burden of tracheal, bronchus, and lung cancer attributable to ambient particulate matter pollution from 1990 to 2021: an analysis of the global burden of disease study. BMC Public Health. 2025;25(1):108. doi: 10.1186/s12889-024-21226-w [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Min L, Mao Y, Lai H. Burden of silica-attributed pneumoconiosis and tracheal, bronchus & lung cancer for global and countries in the national program for the elimination of silicosis, 1990-2019: a comparative study. BMC Public Health. 2024;24(1):571. doi: 10.1186/s12889-024-18086-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Redondo-Sánchez D, Petrova D, Rodríguez-Barranco M, Fernández-Navarro P, Jiménez-Moleón JJ, Sánchez M-J. Socio-economic inequalities in lung cancer outcomes: an overview of systematic reviews. Cancers (Basel). 2022;14(2):398. doi: 10.3390/cancers14020398 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Redondo-Sánchez D, Fernández-Navarro P, Rodríguez-Barranco M, Nuñez O, Petrova D, García-Torrecillas JM, et al. Socio-economic inequalities in lung cancer mortality in Spain: a nation-wide study using area-based deprivation. Int J Equity Health. 2023;22(1):145. doi: 10.1186/s12939-023-01970-y [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Afshar N, English DR, Milne RL. Factors explaining socio-economic inequalities in cancer survival: a systematic review. Cancer Control. 2021;28:10732748211011956. doi: 10.1177/10732748211011956 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Exarchakou A, Kipourou D-K, Belot A, Rachet B. Socio-economic inequalities in cancer survival: how do they translate into Number of Life-Years Lost? Br J Cancer. 2022;126(10):1490–8. doi: 10.1038/s41416-022-01720-x [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Murray CJL. Findings from the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2259–62. [DOI] [PubMed] [Google Scholar]
22.GBD 2016 Occupational Carcinogens Collaborators. Global and regional burden of cancer in 2016 arising from occupational exposure to selected carcinogens: a systematic analysis for the Global Burden of Disease Study 2016. Occup Environ Med. 2020;77(3):151–9. doi: 10.1136/oemed-2019-106012 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2162–203. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. doi: 10.3322/caac.21660 [DOI] [PubMed] [Google Scholar]
25.Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5(12):1749–68. doi: 10.1001/jamaoncol.2019.2996 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Chen C, Shi Q, He W, Tian H, Ye T, Yang Y. Global trends in the burden of rheumatoid arthritis by sociodemographic index: a joinpoint and age-period-cohort analysis based on the Global Burden of Disease Study 2019. BMJ Open. 2024;14(11):e082966. doi: 10.1136/bmjopen-2023-082966 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Chen Y, Zhong Y, Wang M, Su X, Li Q, Wang J, et al. Global trends and differences in the burden of alcohol use disorders attributable to childhood sexual abuse by sex, age, and socio-demographic index: findings from the Global Burden of Disease Study 2019. Child Abuse Negl. 2024;153:106818. doi: 10.1016/j.chiabu.2024.106818 [DOI] [PubMed] [Google Scholar]
28.Bilello KS, Murin S, Matthay RA. Epidemiology, etiology, and prevention of lung cancer. Clin Chest Med. 2002;23(1):1–25. doi: 10.1016/s0272-5231(03)00057-1 [DOI] [PubMed] [Google Scholar]
29.Miller BG, Doust E, Cherrie JW, Hurley JF. Lung cancer mortality and exposure to polycyclic aromatic hydrocarbons in British coke oven workers. BMC Public Health. 2013;13:962. doi: 10.1186/1471-2458-13-962 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28(29):3670–82. doi: 10.1002/sim.3733 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51. doi: [DOI] [PubMed] [Google Scholar]
32.Austin PC, Steyerberg EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med. 2014;33(3):517–35. doi: 10.1002/sim.5941 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Liu Q, Deng J, Yan W, Qin C, Du M, Wang Y, et al. Burden and trends of infectious disease mortality attributed to air pollution, unsafe water, sanitation, and hygiene, and non-optimal temperature globally and in different socio-demographic index regions. Glob Health Res Policy. 2024;9(1):23. doi: 10.1186/s41256-024-00366-x [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Yang Y, Fu WJ, Land KC. A methodological comparison of age-period-cohort models: the intrinsic estimator and conventional generalized linear models. Sociol Methodol. 2004;34(1):75–110. doi: 10.1111/j.0081-1750.2004.00148.x [DOI] [Google Scholar]
35.Nasreen S, Wilk P, Mullowney T, Karp I. Age, period, and cohort effects on asthma prevalence in Canadian adults, 1994-2011. Ann Epidemiol. 2020;41:49–55. [DOI] [PubMed] [Google Scholar]
36.Brigham E, Harris D, Carlsten C, Redlich CA. Occupational health disparities: the pandemic as prism and prod. J Allergy Clin Immunol. 2021;148(5):1148–50. doi: 10.1016/j.jaci.2021.09.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Bonney T, Rospenda KM, Forst L, Conroy LM, Castañeda D, Avelar S, et al. Employment precarity and increased risk of hazardous occupational exposures among residents of high socioeconomic hardship neighborhoods. Ann Work Expo Health. 2022;66(9):1122–35. doi: 10.1093/annweh/wxac062 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Louro H, Gomes BC, Saber AT, Iamiceli AL, Göen T, Jones K, et al. The use of human biomonitoring to assess occupational exposure to PAHs in Europe: a comprehensive review. Toxics. 2022;10(8):480. doi: 10.3390/toxics10080480 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.MacDonald RD, Thomas L, Rusk FC, Marques SD, McGuire D. Occupational health and safety assessment of exposure to jet fuel combustion products in air medical transport. Prehosp Emerg Care. 2010;14(2):202–8. doi: 10.3109/10903120903524955 [DOI] [PubMed] [Google Scholar]
40.Jang T-W, Kim Y, Won J-U, Lee J-S, Song J. The standards for recognition of occupational cancers related with polycyclic aromatic hydrocarbons (PAHs) in Korea. Ann Occup Environ Med. 2018;30:13. doi: 10.1186/s40557-018-0224-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Somayaji D, Seo YS, Wilding GE, Noyes E. A multilevel approach to investigate relationships between healthcare resources and lung cancer. Nurs Res. 2022;71(5):360–9. doi: 10.1097/NNR.0000000000000603 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Williams CD, Allo MA, Gu L, Vashistha V, Press A, Kelley M. Health outcomes and healthcare resource utilization among Veterans with stage IV non-small cell lung cancer treated with second-line chemotherapy versus immunotherapy. PLoS One. 2023;18(2):e0282020. doi: 10.1371/journal.pone.0282020 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Xu J, Zhang N, Zhang Y, Li P, Han J, Gao S, et al. Personal exposure to source-specific particulate polycyclic aromatic hydrocarbons and systemic inflammation: a cross-sectional study of urban-dwelling older adults in China. Geohealth. 2023;7(12):e2023GH000933. doi: 10.1029/2023GH000933 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Olsson A, Guha N, Bouaoun L, Kromhout H, Peters S, Siemiatycki J, et al. Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: results from a pooled analysis of case-control studies (SYNERGY). Cancer Epidemiol Biomarkers Prev. 2022;31(7):1433–41. doi: 10.1158/1055-9965.EPI-21-1428 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Badal S, Holland K, Foster M, Le AB. Occupational safety and health risks of informal waste workers in Nepal: a mapping review. Saf Health Work. 2025;16(3):325–32. doi: 10.1016/j.shaw.2025.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Jani CT, Kareff SA, Morgenstern-Kaplan D, Salazar AS, Hanbury G, Salciccioli JD, et al. Evolving trends in lung cancer risk factors in the ten most populous countries: an analysis of data from the 2019 Global Burden of Disease Study. EClinicalMedicine. 2025;79:103033. doi: 10.1016/j.eclinm.2024.103033 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Di Novi C, Marenzi A. The smoking epidemic across generations, genders, and educational groups: a matter of diffusion of innovations. Econ Hum Biol. 2019;33:155–68. doi: 10.1016/j.ehb.2019.01.003 [DOI] [PubMed] [Google Scholar]
48.Guo H, Huang K, Zhang X, Zhang W, Guan L, Kuang D, et al. Women are more susceptible than men to oxidative stress and chromosome damage caused by polycyclic aromatic hydrocarbons exposure. Environ Mol Mutagen. 2014;55(6):472–81. doi: 10.1002/em.21866 [DOI] [PubMed] [Google Scholar]
49.Mollerup S, Berge G, Baera R, Skaug V, Hewer A, Phillips DH, et al. Sex differences in risk of lung cancer: expression of genes in the PAH bioactivation pathway in relation to smoking and bulky DNA adducts. Int J Cancer. 2006;119(4):741–4. doi: 10.1002/ijc.21891 [DOI] [PubMed] [Google Scholar]
50.Sun Y, Kan Z, Zhang Z-F, Song L, Jiang C, Wang J, et al. Association of occupational exposure to polycyclic aromatic hydrocarbons in workers with hypertension from a northeastern Chinese petrochemical industrial area. Environ Pollut. 2023;323:121266. doi: 10.1016/j.envpol.2023.121266 [DOI] [PubMed] [Google Scholar]
51.Borgulat J, Staszewski T. Fate of PAHs in the vicinity of aluminum smelter. Environ Sci Pollut Res Int. 2018;25(26):26103–13. doi: 10.1007/s11356-018-2648-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Rehman MYA, Taqi MM, Hussain I, Nasir J, Rizvi SHH, Syed JH. Elevated exposure to polycyclic aromatic hydrocarbons (PAHs) may trigger cancers in Pakistan: an environmental, occupational, and genetic perspective. Environ Sci Pollut Res Int. 2020;27(34):42405–23. doi: 10.1007/s11356-020-09088-2 [DOI] [PubMed] [Google Scholar]
53.Yuan H, Wang Y, Duan H. Risk of lung cancer and occupational exposure to polycyclic aromatic hydrocarbons among workers cohorts - worldwide, 1969-2022. China CDC Wkly. 2022;4(17):364–9. doi: 10.46234/ccdcw2022.085 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Wang W, Xu J, Qu X, Lin D, Yang K. Current and future trends of low and high molecular weight polycyclic aromatic hydrocarbons in surface water and sediments of China: insights from their long-term relationships between concentrations and emissions. Environ Sci Technol. 2022;56(6):3397–406. doi: 10.1021/acs.est.1c05323 [DOI] [PubMed] [Google Scholar]
55.Driscoll TR, Carey RN, Peters S, Glass DC, Benke G, Reid A, et al. The Australian work exposures study: occupational exposure to polycyclic aromatic hydrocarbons. Ann Occup Hyg. 2016;60(1):124–31. doi: 10.1093/annhyg/mev057 [DOI] [PubMed] [Google Scholar]
56.Bottai M, Selander J, Pershagen G, Gustavsson P. Age at occupational exposure to combustion products and lung cancer risk among men in Stockholm, Sweden. Int Arch Occup Environ Health. 2016;89(2):271–5. doi: 10.1007/s00420-015-1070-x [DOI] [PubMed] [Google Scholar]
57.Vimercati L, Bisceglia L, Cavone D, Caputi A, De Maria L, Delfino MC, et al. Environmental monitoring of PAHs exposure, biomarkers and vital status in coke oven workers. Int J Environ Res Public Health. 2020;17(7):2199. doi: 10.3390/ijerph17072199 [DOI] [PMC free article] [PubMed] [Google Scholar]

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Additional Editor Comments:

Reviewers commend the study but request clearer model assumptions and sensitivity analyses for LMIC data, fuller explanation of GBD exposure attribution, discussion of co-exposure confounding, mechanistic insight into the SDI-disease burden pattern, control of smoking confounding, and rationale for sex differences. They also urge stronger policy and intervention recommendations, especially for low- and middle-SDI regions. These revisions will improve clarity and relevance.

Reviewers' comments:

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: 1. The assumptions for the model may be clearly delineated in the methods. Also, the quality of the data from LMICs may be poor, hence sensitivity analyses may be conducted to unravel effect of the assumptions made

2. The manuscript would benefit from more detail on the methodology used by GBD to assign causality between occupational PAH exposure and TBL cancer.

3. Consider briefly discussing confounding by other occupational exposures (e.g., asbestos, silica), which are common co-exposures and may overlap in industrial settings.

4. The finding of an inverted U-shaped relationship between SDI and disease burden is interesting but underexplored. The authors should provide greater mechanistic or contextual explanation—e.g., linking it to patterns in industrialization, occupational regulation, and latency of cancer onset.

5.The paper would benefit from a stronger policy or practical intervention focus, especially in the conclusion. For example, what are the implications for workplace safety standards, surveillance, or global occupational health policy?

Reviewer #2: This study provides a timely and comprehensive analysis of the global burden of tracheal, bronchus, and lung (TBL) cancer attributable to occupational polycyclic aromatic hydrocarbon (PAH) exposure, highlighting disparities across socio-demographic index (SDI) regions from 1990 to 2021. Its strengths include rigorous use of Global Burden of Disease (GBD) data, detailed age-period-cohort analyses, and insightful stratification by SDI, revealing concerning trends in low- and middle-income regions. However, limitations such as reliance on modeled estimates, potential confounding by smoking, and lack of occupational exposure details (e.g., duration, industry-specific risks) warrant clarification. The findings underscore the need for targeted workplace interventions but would benefit from deeper discussion on mechanistic pathways and policy implications to strengthen impact. Overall, the study advances understanding of occupational cancer disparities but requires minor methodological refinements and expanded contextualization for broader relevance.

The study relies on GBD estimates. Could you elaborate on how occupational PAH exposure was specifically measured and attributed to TBL cancer cases in the GBD data? What validation exists for these exposure estimates?

The APC analysis is interesting but complex. Could you provide more details on how the age, period, and cohort effects were separated, and what assumptions were made in this modeling?

You note higher EAPCs in females compared to males globally. What might explain this gender difference, given that occupational PAH exposure has traditionally been higher in male-dominated industries?

The inverted U-shaped relationship between SDI and TBL cancer burden is intriguing. Could you discuss potential mechanisms behind why middle SDI regions show the highest burden?

The manuscript acknowledges uncertainties in GBD estimates. How might underreporting of occupational exposures in low/middle SDI regions affect your findings?

Smoking is a major confounder for lung cancer. How was smoking controlled for in the attribution of TBL cancer to occupational PAH exposure?

Given your findings, what specific interventions would you recommend for low/middle SDI regions to reduce occupational PAH exposure?

How might your results inform international occupational health standards for PAH exposure limits?

Have you considered analyzing specific high-risk occupations (e.g., asphalt workers, coke oven workers) where PAH exposure is particularly high?

Were you able to examine dose-response relationships given the available data?

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Reviewer #2: No

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PLoS One. 2026 Feb 12;21(2):e0342250. doi: 10.1371/journal.pone.0342250.r002

Author response to Decision Letter 1

5 Aug 2025

Editor

Response: Thank you for your thoughtful comments and considerations. We have improved our manuscript following your and the reviewers’ comments. The detailed responses to the reviewers are provided in this letter.

Reviewer 1

1. The assumptions for the model may be clearly delineated in the methods. Also, the quality of the data from LMICs may be poor, hence sensitivity analyses may be conducted to unravel effect of the assumptions made

Response: Thank you for this important comment. We agree that a more detailed explanation of the modeling assumptions is crucial. As our study is a secondary analysis of the Global Burden of Disease (GBD) data, the core assumptions and limitations are inherent to the GBD framework itself, and we could not directly calculate the estimates after excluding the original data from LMICs. We have added more content on assumptions for the model and overall quality of the data from LMICs.

In addition, the GBD framework utilizes models like spatiotemporal Gaussian process regression (ST-GPR) to pool this heterogeneous data. These models are crucial for controlling and adjusting for bias and for generating estimates in locations or time periods where direct data may be limited [1]. Nevertheless, it is true that one inherent limitation of the GBD study is the relatively poor data in LMICs.

We have added related content as:

2.4 Methodology to calculate attributable burden in the GBD framework

To quantify the disease burden attributable to a specific risk factor, the GBD study used a validated comparative risk assessment (CRA) framework [1]. In brief, the first step is quantifying the relative risks (RRs) of the health outcome (TBL cancer) as a function of exposure to the risk factor (occupational PAHs). This was done using a meta-regression in a “burden of proof” approach, which synthesizes data from systematic reviews. Second, exposure data are projected to the global scale from various sources using Bayesian statistical models, specifically spatiotemporal Gaussian process regression. Third, by integrating projected exposure levels, attributable burden could be calculated to quantify the proportional change in TBL cancer burden that would occur if occupational PAH exposure was reduced to the TMREL. Fourth, the GBD framework accounts for the joint effects of risk factors by assuming that RRs are multiplicative. For risk factors without mediating pathways, such as occupational PAH exposure and confounding factors (e.g., smoking and other occupational carcinogens), their independent contributions were calibrated to avoid overestimation of joint effects [1]. More detailed descriptions on the statistical methods, input data, and exposure-response functions are publicly accessible online (https://ghdx.healthdata.org/record/ihme-data/gbd-2021-burden-by-risk-1990-2021). Finally, all related estimates were recorded in the GBD 2021 result tools (https://vizhub.healthdata.org/gbd-results/). (Method Section)

First, a key limitation of our study is that the GBD framework is subjected to relatively incomplete and heterogeneous data from LMICs. While the use of spatiotemporal Gaussian process regression helps mitigate data gaps by integrating multiple sources and adjusting for bias, substantial underreporting of occupational exposures and TBL cancer cases in LMICs may lead to underestimations of the true burden [1]. (Discussion Section)

2. The manuscript would benefit from more detail on the methodology used by GBD to assign causality between occupational PAH exposure and TBL cancer.

Response: We agree and appreciate your suggestion, and have added related content as: 2.4 Methodology to calculate attributable burden in the GBD framework

3. Consider briefly discussing confounding by other occupational exposures (e.g., asbestos, silica), which are common co-exposures and may overlap in industrial settings.

Response: Thank you for this comment. We have added related content: Previous studies examining the health burden of TBL cancer have predominantly focused on outdoor air pollution (e.g., emissions from vehicles and industrial activities) and other occupational hazards (e.g., asbestos, silica) [2-5]. (Introduction Section) & Last, while the GBD framework used techniques to adjust major risk factors as independent contributors, it could not fully account for potential interactions between occupational PAHs and other exposures, such as outdoor air pollution and co-occurring occupational carcinogens (e.g., asbestos, silica). (Discussion Section)

Response: We agree and appreciate your comments. We have added related discussions according to your comments: First, older populations in these regions may have experienced prolonged exposure to occupational PAHs due to delayed implementation or absence of strict workplace safety regulations [6-8]. & In low to middle SDI regions, rapid industrialization particularly in sectors with high PAH exposure risks, such as manufacturing (e.g., aluminum production, coal tar distillation), construction, and informal waste management drives elevated occupational exposure [9, 10]. Meanwhile, inadequate protective measures such as poor ventilation and limited personal protective equipment may also aggravate related disease burden. This trend is particularly pronounced in regions such as East Asia, where accelerated industrialization in the late 20th century coincided with delayed adoption of workplace safety standards [11, 12]. The less apparent decline in females in high SDI regions also suggests potential gender-related disparities in occupational exposure patterns [12]. As regions transition to high SDI, advancements in occupational health policies, automation, and cleaner technologies reduce direct PAH exposure. Stricter regulatory frameworks combined with shifts toward service-oriented economies may also mitigate the TBL cancer burden attributed to occupational exposure to PAHs [6, 8]. (Discussion Section)

Response: Thank you for this important comment. We have related content in Discussion as: To reduce the global burden of TBL cancer associated with occupational PAH exposure, policymakers should prioritize interventions that align with the socio-economic and industrial contexts of regions at different SDI levels. Particularly, in low- and middle-SDI regions, where industrialization often outpaces occupational safety infrastructure, governments should enforce stricter workplace exposure limits for PAHs and the use of personal protective equipment. Globally, harmonizing occupational safety standards through frameworks such as the ILO conventions and promoting technology transfer from high- to low-resource settings could mitigate disparities. Additionally, integrating gender-specific protections such as targeted safety protocols in female-dominated occupations would also be potentially helpful to mitigate related health burden. (Discussion Section)

Reviewer 2

This study provides a timely and comprehensive analysis of the global burden of tracheal, bronchus, and lung (TBL) cancer attributable to occupational polycyclic aromatic hydrocarbon (PAH) exposure, highlighting disparities across socio-demographic index (SDI) regions from 1990 to 2021. Its strengths include rigorous use of Global Burden of Disease (GBD) data, detailed age-period-cohort analyses, and insightful stratification by SDI, revealing concerning trends in low- and middle-income regions. However, limitations such as reliance on modeled estimates, potential confounding by smoking, and lack of occupational exposure details (e.g., duration, industry-specific risks) warrant clarification. The findings underscore the need for targeted workplace interventions but would benefit from deeper discussion on mechanistic pathways and policy implications to strengthen impact. Overall, the study advances understanding of occupational cancer disparities but requires minor methodological refinements and expanded contextualization for broader relevance.

1. The study relies on GBD estimates. Could you elaborate on how occupational PAH exposure was specifically measured and attributed to TBL cancer cases in the GBD data? What validation exists for these exposure estimates?

Response: We appreciate you for this important comment. We have added more information on the details related data we used in the Method Section.

2.2 Occupational exposure to PAHs and TBL cancer

In brief, occupational exposure to PAHs was quantified within the GBD framework using a multi-step process integrating economic activity classifications, occupation distributions, and exposure risk levels [13]. The input data were primarily sourced from the International Labor Organization (ILO). Occupational PAH exposure levels (high/low) were categorized based on the 17 International Standard Industrial Classification (ISIC) economic activities and 9 International Standard Classification of Occupations (ISCO) occupational categories. According to the GBD framework, the Theoretical Minimum Risk Exposure Level (TMREL) of occupational exposure to PAHs was set to zero (no thresholds). High-exposure industries (e.g., coal mining, asphalt production) were assigned elevated exposure rates using the CARcinogen Exposure database and expert-derived thresholds, while low-exposure industries (e.g., education, finance) received lower rates [1, 13].

2.4 Methodology to calculate attributable burden in the GBD framework

2. The APC analysis is interesting but complex. Could you provide more details on how the age, period, and cohort effects were separated, and what assumptions were made in this modeling?

Response: Thank you for this valuable comment. We have added more details on this: In addition, to characterize the effects of age, cohort, and period on TBL cancer death rates, an age-period-cohort (APC) model was applied [14, 15]. This model helps separate the influence of the age effect (the age at which individuals are with higher risks), the generation or cohort effect (the impact of birth year on risks), and the period effect (the impact of time-specific changes on risks) [14, 15]. It is mathematically expressed as Y_ijk=α+f(A_i)+g(P_j)+h(C_k)+ε_ijk, whereY_ijk represents the outcome, α is the intercept, f(A_i) captures the age effect, g(P_j) accounts for period effects, h(C_k) denotes cohort effects, and ε_ijk is the error term. To estimate these effects, the APC model employs the intrinsic estimator method to deal with the collinearity in the cohort constraint equation and allows for robust estimation of independent effects while addressing the identifiability problem [14, 15]. (Method Section)

3. You note higher EAPCs in females compared to males globally. What might explain this gender difference, given that occupational PAH exposure has traditionally been higher in male-dominated industries?

Response: Thank you for this important comment. It is true that although we found males had higher ASDR and DALYs than females, while females presented more significant increasing trends (EAPC). There might be several explanations. In many regions over the past three decades, women’s participation in industries with PAH exposure might have grown [16, 17]. In addition, differences in background lung cancer trends and biological reactions by sex may also amplify the apparent increase in PAH-attributable burden among women [16-18]. We have added potential explanations in the Discussion Section:

Although males experienced higher ASDRs and DALYs attributable to occupational PAH exposure, females showed larger increasing trends indicated by higher EAPCs. This might be driven by several factors. First, over the past three decades, female workforce participation in sectors with incidental PAH exposure has increased in many countries [13, 16]. A previous pooled analysis of 14 case-control studies in Europe and Canada also reported higher risks of lung cancer for ever‑exposed women (OR=1.20) ver

Attachment

Submitted filename: Response letter.docx

pone.0342250.s009.docx^{(420.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342250.r003

Decision Letter 1

Igor Burstyn

26 Dec 2025

Dear Dr. Xie,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Feb 09 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Igor Burstyn

Academic Editor

PLOS One

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Partly

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: Yes

Reviewer #4: Yes

**********

Reviewer #3: Major:

1. Over-reliance on GBD Modelling and Data Limitations:

While the authors have expanded the limitations section, the core issue remains that the study's findings are entirely dependent on the GBD's modelled estimates. The acknowledgement of incomplete and heterogeneous data from Low- and Middle-Income Countries (LMICs) is appropriate. However, the statement that findings for lower SDI regions are "conservative estimates" is speculative. The direction of bias (underestimation vs. overestimation) is difficult to ascertain due to competing factors: underreporting of occupational exposures and cancer cases versus potential over-attribution of TBL cancer to PAHs in the absence of robust confounder control at the individual level. This fundamental uncertainty should be more prominently and forcefully stated in the Abstract, Results, and Discussion, framing the entire interpretation of SDI disparities. A sensitivity analysis, though ideal, may not be feasible; therefore, the discussion of this limitation must be exceptionally strong.

2. Superficial Exploration of Gender Differences:

The explanation for higher Estimated Annual Percentage Change (EAPC) in females, while improved, leans heavily on biological susceptibility. This requires more critical balance. The authors should delve deeper into potential socio-occupational factors:

Segregation within industries: Are women increasingly entering specific roles within high-PAH industries that might have different exposure profiles (e.g., administrative roles in manufacturing vs. direct labor)?

Informal sector work: In many LMICs, women are disproportionately represented in informal waste management or small-scale industries where PAH exposure is unmeasured and unregulated. Could this contribute to the trend?

Interaction with other risk factors: The discussion should explicitly consider if trends in female smoking (which vary dramatically by region) could interact with or confound the observed PAH-attributable burden trends. The GBD's adjustment method is described, but its effectiveness in disentangling these correlated risks at the population level warrants a more cautious tone.

3. Mechanistic Explanation of the SDI-Burden Relationship:

The added discussion on the inverted U-shaped curve is helpful but still somewhat descriptive. To strengthen the mechanistic insight:

Link to Economic Transition Theories: Frame the findings within established theories of epidemiological or risk transition. The peak burden in middle-SDI regions mirrors patterns seen with other occupational and environmental hazards, where industrialization outpaces regulatory capacity and health infrastructure.

Latency and Temporal Misalignment: Emphasize that the current burden in high-SDI regions reflects historical exposures from decades past (when their SDI was middle-range). Conversely, the current high burden in middle-SDI regions is a "real-time" consequence of present-day exposures. This temporal disconnect between exposure (past/present) and outcome (present/future) is crucial for interpreting the APC results and forecasting future burdens. The discussion should more explicitly connect the period/cohort effects to these historical industrialization waves.

4. Policy Recommendations Lack Specificity and Feasibility Analysis:

The policy section remains generic. To enhance impact, recommendations should be more targeted and actionable:

Beyond PPE and Limits: While important, recommending "stricter exposure limits and PPE" in low-resource settings is often infeasible without parallel investments in enforcement, monitoring, and worker education. Suggest concrete, incremental steps (e.g., prioritizing exposure control in 2-3 key industries, promoting simple ventilation improvements, developing low-cost exposure biomarkers for surveillance).

Leveraging Existing Frameworks: Mentioning ILO conventions is good. Specify which conventions (e.g., C139, C155, C170) are most relevant and propose a tangible pathway for their adoption and monitoring in target regions.

Case Examples: Briefly reference a successful intervention from a specific country (e.g., reduction in PAH exposures in a particular industry in a high-SDI setting) and discuss its potential for adaptation.

Research-to-Policy Pipeline: Recommend establishing linked occupational cancer registries in sentinel middle-SDI industrial zones to improve data quality and directly inform local policy, moving beyond reliance on global models.

Minor:

1. APC Analysis Interpretation: The interpretation of cohort effects ("recent generations might have lower death rates") is challenging given the 30-year study window and the long latency of TBL cancer. The observed cohort effect likely reflects exposures from the mid-20th century onwards. This complexity should be acknowledged to avoid oversimplification.

2. Figure and Table Presentation:

Table 1: The table is dense. Consider creating a separate, simplified summary table for the main global and SDI-region results in the main text and moving the full sex-stratified table to the supplement.

Figure 1 (Maps): Ensure the color scales are perfectly intuitive and include a clear note in the caption that the maps depict EAPC, not absolute burden.

3. Language and Flow: The manuscript is generally well-written. A final careful edit for concise phrasing and to avoid minor repetitions (e.g., the GBD methodology is described in very similar terms in multiple sections) would enhance readability.

Reviewer #4: Thank you for the opportunity to review your work and I hope you find my comments of assistance. The manuscript addresses an important topic TBL cancer which has an increasing incidence, The attribution to preventable causes such as work is important.

The striking finding for me is the sex differences which show a change in the direction of the usually expected ratios for occupational diseases. These changes in EAPC appear to be marked by country. Noting that the GBD methods for confounders were applied to what extent do the authors feel smoking rates in females may be contributory in comparison to those of males? The paper Evolving trends in lung cancer risk factors in the ten most populous countries: an analysis of data from the 2019 Global Burden of Disease Study Jani, Chinmay T. et al. ClinicalMedicine, Volume 79, 103033 may be helpful although it only examines a limited number of countries. However it is noted, "Among males, tobacco-associated

ASMR fell over time across all countries, excluding China, Indonesia, and Pakistan. Among females, the reverse pattern was observed with overall increasing rates of tobacco-associated ASMR in most countries, with only rates in the USA and Mexico falling between 1990 and 2019".

In addition to the above to what extent is proportional mortality playing a role in survival to die of TBL?

L169 "While occupational exposure primarily occurs during working-age years (typically 15–75 years), the disease burden manifests later in life due to the long latency period of TBL cancer." Figure 3 demonstrates that actually the majority of deaths occur <75 years age. It is perhaps more useful to state the typical latency of TBL cancer.

L49 "Increasing studies have reported disparities in incidence...", suggest "An increasing number of studies have reported disparities in incidence ..."

L55 "PAHs are known carcinogens to increase the risk of TBL cancer" suggest "PAHs are carcinogens known to increase the risk of TBL cancer"

L64 "PAH exposure, a significant while overlooked risk factor" suggest "PAH exposure, a significant although overlooked risk factor"

L446 "subjected to relatively incomplete and heterogeneous data from LMICs." suggest "subject to relatively incomplete and heterogeneous data from LMICs."

Figure 5 is interesting but particularly difficult to distinguish the markers for "Both" and "Female" even when I zoom

**********

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Reviewer #3: No

Reviewer #4: No

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PLoS One. 2026 Feb 12;21(2):e0342250. doi: 10.1371/journal.pone.0342250.r004

Author response to Decision Letter 2

19 Jan 2026

PONE-D-25-19840R1

Global burden and trends of tracheal, bronchus, and lung cancer attributed to occupational exposure to polycyclic aromatic hydrocarbons in regions with different sociodemographic index, 1990-2021

PLOS ONE

Response letter

Dear Dr. Igor Burstyn,

We appreciate your insightful feedback. We have carefully revised the manuscript in accordance with recommendations and believe that these comments have significantly enhanced the manuscript. A point-by-point response to the comments is attached along with the revised manuscript.

We appreciate your guidance and look forward to your response.

Sincerely,

Xiaowei Xie

Department of Thoracic Surgery, The First Hospital of Putian City, Putian, 351100, China

Email: xxw_biolab@163.com

Journal Requirements:

Response: Thank you for your remind. We have carefully reviewed the suggested publication to ensure appropriate citations.

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

________________________________________

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Partly

Reviewer #4: Yes

________________________________________

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: Yes

________________________________________

4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #3: Yes

Reviewer #4: Yes

________________________________________

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

________________________________________

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3:

1. Over-reliance on GBD Modelling and Data Limitations:

Response: We would like to thank you for your thoughtful considerations on the limitation of our manuscript. We agree with your comments and have expanded and added related sections.

Revisions:

Abstracts: “Nevertheless, given the inherent limitations of GBD estimation methods and data scarcity in LMICs, the observed disparities should be interpreted with caution and warrant further primary research.”

Results: In the first paragraph of the Discussion Section, after the overview of the results, we have added “Nevertheless, it is important to note that the comparisons across SDI regions are based on these modelled estimates, and the observed trends should be interpreted considering potential variability in data quality and completeness underlying the models, particularly in LMICs.”

Discussion: We have removed the “conservative estimates”, and have added related content: “First, a significant limitation of this study is its reliance on the modelled estimates of the GBD study. While the GBD employs robust methodologies to synthesize data and address gaps, the accuracy of its estimates is contingent on the quality and coverage of underlying source data. Substantial under-reporting of PAH exposures and TBL cancer diagnoses in many LMICs is a recognized issue, potentially leading to underestimation of the burden [1]. Meanwhile, the ecological nature of the analysis could not fully consider the confounders at the individual level (e.g., tobacco smoking, other occupational carcinogens), which means that the direction and magnitude of net bias are difficult to determine precisely [2]. Therefore, the observed disparities across SDI regions should be interpreted considering potential uncertainties.”

2. Superficial Exploration of Gender Differences:

Response: We agree with your comments and thank you for this remind. We have improved this section as suggested.

Revision: “Although males experienced higher ASDRs and DALYs attributable to occupational PAH exposure, females showed larger increasing trends indicated by higher EAPCs. This observed pattern may be driven by a confluence of socioeconomic, occupational, and biological factors. First, shifting occupational demographics play a role. While female workforce participation in formal sectors with potential PAH exposure has increased, gender-based occupational segregation often places women in different roles within these industries (e.g., administrative or support functions versus direct production labor) [3, 4]. More critically, in many LMICs, women are disproportionately represented in the informal economy, including waste picking, small-scale food processing using solid fuels, and home-based manufacturing, where high-intensity PAH exposures are common and outside regulatory frameworks [5]. This unregulated exposure likely contributes substantially to the rising trend. Second, the influence of correlated risk factors, particularly active smoking, warrants consideration [1, 6, 7]. As highlighted by a study among the ten most populous countries, while tobacco-associated lung cancer mortality rates have declined among males in most countries, they have increased among females from 1990 to 2019 [7]. Notably, historical smoking trends have differed by sex, with female smoking prevalence peaking later than male prevalence in many regions [6, 8]. Consequently, recent trends in female lung cancer burden likely reflect the combined effects of both risk factors. Although the GBD framework adjusts for smoking independently [1], residual confounding or interaction between smoking and occupational PAHs at the population level may influence the observed sex-specific trends. Third, biological susceptibility may amplify the trends. A previous pooled analysis of 14 case-control studies in Europe and Canada also reported higher risks of lung cancer for ever‑exposed women (OR=1.20) versus men (OR=1.08), despite lower median cumulative PAH exposure levels among women [3]. Experimental evidence also showed that women might be more susceptible to PAH-related toxicity due to biological differences. Studies have reported higher levels of oxidative stress biomarkers and genotoxic effects in exposed women than in men at similar exposure levels [9]. Furthermore, female lung tissue exhibits higher expression of CYP1A1 and greater accumulation of PAH–DNA adducts, suggesting enhanced metabolic activation of PAHs in women [10]. These biological susceptibilities may amplify the health impact of even modest occupational exposures, contributing to the more significant increasing trends observed in females.”

3. Mechanistic Explanation of the SDI-Burden Relationship:

The added discussion on the inverted U-shaped curve is helpful but still somewhat descriptive. To strengthen the mechanistic insight:

Response: Fully agree. We have added this content in revised manuscript.

Revision: “Our analysis identified an inverted U-shaped relationship between SDI and TBL cancer burden, which highlights complex interactions between socioeconomic development and TBL cancer burden associated with occupational exposures to PAHs. The observed inverted U-shaped relationship between SDI and PAH-attributable TBL cancer burden aligns with the framework of the epidemiological transition of occupational risk. In this paradigm, the population-level burden peaks when the pace of industrialization and resultant exposure outstrip the development of protective regulatory frameworks and healthcare capacity. This is evident in low- to middle-SDI regions, where expansion in high-exposure sectors (e.g., manufacturing, construction, informal industry) has driven increased occupational PAH exposure [11, 12], while safeguards such as ventilation controls and personal protective equipment remain inadequate. This trend is particularly pronounced in regions such as East Asia, where accelerated industrialization in the late 20th century coincided with the delayed adoption of workplace safety standards [13, 14]. The high burden observed today in middle-SDI regions is largely attributable to ongoing, intensive exposures from current industrial activities. In contrast, the contemporary burden in high-SDI regions primarily stems from exposures accumulated in the past, when these regions were at a similar developmental stage. The declining burden trends now seen in high-SDI settings represent the delayed benefit of occupational health policies, technological improvements, and economic shifts implemented in prior decades [15, 16].

The APC analyses further provided additional insights into the temporal dynamics of TBL cancer burden attributed to occupational PAH exposure. It should be noted that the interpretation of cohort patterns is inherently limited by the 30-year study observation window and the multi-decadal latency of TBL cancer, meaning the observed cohort effect integrates exposure experiences over a much longer historical timeframe. Particularly, the strong and increasing period effect in low- and middle-SDI regions quantifies the persistent risk from present-day industrial exposure [16-18]. In high-SDI regions, a more modest period effect coupled with elevated risk in older cohorts captures the enduring impact of past exposure regimes [19, 20]. Meanwhile, the declining cohort effect observed globally suggests that preventive measures may be reducing risk for more recent generations [20]. Together, these APC findings demonstrate that the inverted U-shaped curve is not merely a cross-sectional disparity but a dynamic signature of industrialization waves, exposure histories, and the delayed translation of TBL cancer burden attributed to occupational PAH exposure.”

4. Policy Recommendations Lack Specificity and Feasibility Analysis:

The policy section remains generic. To enhance impact, recommendations should be more targeted and actionable:

Response: We appreciate your comments. According to your comments, we have expanded related content.

Revision: “To reduce the global burden of TBL cancer attributable to occupational PAH exposure, policymakers should prioritize interventions that align with the socio-economic and industrial contexts of regions at different SDI levels. Particularly, in low- and middle-SDI regions, where industrialization often outpaces occupational safety infrastructure, governments should enforce stricter workplace exposure limits for PAHs and the u

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PLoS One. doi: 10.1371/journal.pone.0342250.r005

Decision Letter 2

Igor Burstyn

20 Jan 2026

Global burden and trends of tracheal, bronchus, and lung cancer attributed to occupational exposure to polycyclic aromatic hydrocarbons in regions with different sociodemographic index, 1990-2021

PONE-D-25-19840R2

Dear Dr. Xie,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Additional Editor Comments (optional):

Thank you for undertaking revisions. Hopefully you have better appreciation now of the limited utility of GBD data.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0342250.r006

Acceptance letter

Igor Burstyn

PONE-D-25-19840R2

PLOS One

Dear Dr. Xie,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Burden and trends of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI by sex, 1990–2021.

(PDF)

pone.0342250.s001.pdf^{(128.2KB, pdf)}

S1 Fig. Annual change in DALYs rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990–2021.

(A-C) Global; (D-F) Low SDI; (G-I) Low-middle SDI; (J-L) Middle SDI; (M-O) High-middle SDI; (P-R) High SDI. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s002.pdf^{(3.8MB, pdf)}

S2 Fig. Age-specific DALYs rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990–2021.

(A-C) 1990; (D-F) 2021. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s003.pdf^{(1.7MB, pdf)}

S3 Fig. EAPC of age-specific DALYs rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990–2021.

(A) Both; (B) Female; (C) Male. Note: DALYs, disability adjusted life-years; EPAC, estimated annual percentage change; SDI, socio-demographic index.

(PDF)

pone.0342250.s004.pdf^{(1.3MB, pdf)}

S4 Fig. Associations between age-standardized DALYs rate of TBL cancer attributed to occupational exposure to PAHs and SDI in GBD regions, 1990–2021.

(A) Both; (B) Female; (C) Male. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s005.pdf^{(1.1MB, pdf)}

S5 Fig. Age-period-cohort analysis for DALYs rate of TBL cancer attributed to occupational exposure to PAHs.

(A) Global; (B) Low SDI; (C) Low-middle SDI; (D) Middle SDI; (E) High-middle SDI; (F) High SDI. Note: DALYs, disability adjusted life-years; SDI, socio-demographic index.

(PDF)

pone.0342250.s006.pdf^{(3MB, pdf)}

S1 Data. Minimal anonymized dataset.

(CSV)

pone.0342250.s007.csv^{(17.1MB, csv)}

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Data Availability Statement

[pone.0342250.ref001] 1.Deng Y, Zhao P, Zhou L, Xiang D, Hu J, Liu Y, et al. Epidemiological trends of tracheal, bronchus, and lung cancer at the global, regional, and national levels: a population-based study. J Hematol Oncol. 2020;13(1):98. doi: 10.1186/s13045-020-00915-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref002] 2.Kuang Z, Wang J, Liu K, Wu J, Ge Y, Zhu G, et al. Global, regional, and national burden of tracheal, bronchus, and lung cancer and its risk factors from 1990 to 2021: findings from the global burden of disease study 2021. EClinicalMedicine. 2024;75:102804. doi: 10.1016/j.eclinm.2024.102804 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref003] 3.Global Burden of Disease 2019 Cancer Collaboration, Kocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncol. 2022;8(3):420–44. doi: 10.1001/jamaoncol.2021.6987 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref004] 4.Khanmohammadi S, Saeedi Moghaddam S, Azadnajafabad S, Rezaei N, Esfahani Z, Rezaei N, et al. Burden of tracheal, bronchus, and lung cancer in North Africa and Middle East countries, 1990 to 2019: results from the GBD study 2019. Front Oncol. 2023;12:1098218. doi: 10.3389/fonc.2022.1098218 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref005] 5.Deng Y, Peng L, Li N, Zhai Z, Xiang D, Ye X, et al. Tracheal, bronchus, and lung cancer burden and related risk factors in the United States and China. Am J Transl Res. 2021;13(4):1928–51. [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref006] 6.Li H, Guo J, Liang H, Zhang T, Zhang J, Wei L, et al. The burden of trachea, bronchus, and lung cancer attributable to occupational exposure from 1990 to 2019. Front Public Health. 2022;10:928937. doi: 10.3389/fpubh.2022.928937 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref007] 7.Moorthy B, Chu C, Carlin DJ. Polycyclic aromatic hydrocarbons: from metabolism to lung cancer. Toxicol Sci. 2015;145(1):5–15. doi: 10.1093/toxsci/kfv040 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref008] 8.Zainal PNS, Alang Ahmad SA, Abdul Aziz SFN, Rosly NZ. Polycyclic aromatic hydrocarbons: occurrence, electroanalysis, challenges, and future outlooks. Crit Rev Anal Chem. 2022;52(4):878–96. [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref009] 9.Holme JA, Vondráček J, Machala M, Lagadic-Gossmann D, Vogel CFA, Le Ferrec E, et al. Lung cancer associated with combustion particles and fine particulate matter (PM2.5) - The roles of polycyclic aromatic hydrocarbons (PAHs) and the aryl hydrocarbon receptor (AhR). Biochem Pharmacol. 2023;216:115801. doi: 10.1016/j.bcp.2023.115801 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref010] 10.Olsson AC, Fevotte J, Fletcher T, Cassidy A, ’t Mannetje A, Zaridze D, et al. Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: a multicenter study in Europe. Occup Environ Med. 2010;67(2):98–103. doi: 10.1136/oem.2009.046680 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref011] 11.Ifegwu OC, Anyakora C. Polycyclic aromatic hydrocarbons: part I. Exposure. Adv Clin Chem. 2015;72:277–304. doi: 10.1016/bs.acc.2015.08.001 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref012] 12.Stading R, Gastelum G, Chu C, Jiang W, Moorthy B. Molecular mechanisms of pulmonary carcinogenesis by polycyclic aromatic hydrocarbons (PAHs): implications for human lung cancer. Semin Cancer Biol. 2021;76:3–16. doi: 10.1016/j.semcancer.2021.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref013] 13.Letelier P, Saldías R, Loren P, Riquelme I, Guzmán N. MicroRNAs as potential biomarkers of environmental exposure to polycyclic aromatic hydrocarbons and their link with inflammation and lung cancer. Int J Mol Sci. 2023;24(23). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref014] 14.Mogaraju JK. Machine learning strengthened prediction of tracheal, bronchus, and lung cancer deaths due to air pollution. Environ Sci Pollut Res Int. 2023;30(45):100539–51. doi: 10.1007/s11356-023-29448-y [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref015] 15.Lu J, Zhao X, Gan S. Global, regional and national burden of tracheal, bronchus, and lung cancer attributable to ambient particulate matter pollution from 1990 to 2021: an analysis of the global burden of disease study. BMC Public Health. 2025;25(1):108. doi: 10.1186/s12889-024-21226-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref016] 16.Min L, Mao Y, Lai H. Burden of silica-attributed pneumoconiosis and tracheal, bronchus & lung cancer for global and countries in the national program for the elimination of silicosis, 1990-2019: a comparative study. BMC Public Health. 2024;24(1):571. doi: 10.1186/s12889-024-18086-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref017] 17.Redondo-Sánchez D, Petrova D, Rodríguez-Barranco M, Fernández-Navarro P, Jiménez-Moleón JJ, Sánchez M-J. Socio-economic inequalities in lung cancer outcomes: an overview of systematic reviews. Cancers (Basel). 2022;14(2):398. doi: 10.3390/cancers14020398 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref018] 18.Redondo-Sánchez D, Fernández-Navarro P, Rodríguez-Barranco M, Nuñez O, Petrova D, García-Torrecillas JM, et al. Socio-economic inequalities in lung cancer mortality in Spain: a nation-wide study using area-based deprivation. Int J Equity Health. 2023;22(1):145. doi: 10.1186/s12939-023-01970-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref019] 19.Afshar N, English DR, Milne RL. Factors explaining socio-economic inequalities in cancer survival: a systematic review. Cancer Control. 2021;28:10732748211011956. doi: 10.1177/10732748211011956 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref020] 20.Exarchakou A, Kipourou D-K, Belot A, Rachet B. Socio-economic inequalities in cancer survival: how do they translate into Number of Life-Years Lost? Br J Cancer. 2022;126(10):1490–8. doi: 10.1038/s41416-022-01720-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref021] 21.Murray CJL. Findings from the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2259–62. [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref022] 22.GBD 2016 Occupational Carcinogens Collaborators. Global and regional burden of cancer in 2016 arising from occupational exposure to selected carcinogens: a systematic analysis for the Global Burden of Disease Study 2016. Occup Environ Med. 2020;77(3):151–9. doi: 10.1136/oemed-2019-106012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref023] 23.GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2162–203. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref024] 24.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. doi: 10.3322/caac.21660 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref025] 25.Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5(12):1749–68. doi: 10.1001/jamaoncol.2019.2996 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref026] 26.Chen C, Shi Q, He W, Tian H, Ye T, Yang Y. Global trends in the burden of rheumatoid arthritis by sociodemographic index: a joinpoint and age-period-cohort analysis based on the Global Burden of Disease Study 2019. BMJ Open. 2024;14(11):e082966. doi: 10.1136/bmjopen-2023-082966 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref027] 27.Chen Y, Zhong Y, Wang M, Su X, Li Q, Wang J, et al. Global trends and differences in the burden of alcohol use disorders attributable to childhood sexual abuse by sex, age, and socio-demographic index: findings from the Global Burden of Disease Study 2019. Child Abuse Negl. 2024;153:106818. doi: 10.1016/j.chiabu.2024.106818 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref028] 28.Bilello KS, Murin S, Matthay RA. Epidemiology, etiology, and prevention of lung cancer. Clin Chest Med. 2002;23(1):1–25. doi: 10.1016/s0272-5231(03)00057-1 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref029] 29.Miller BG, Doust E, Cherrie JW, Hurley JF. Lung cancer mortality and exposure to polycyclic aromatic hydrocarbons in British coke oven workers. BMC Public Health. 2013;13:962. doi: 10.1186/1471-2458-13-962 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref030] 30.Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28(29):3670–82. doi: 10.1002/sim.3733 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref031] 31.Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51. doi: [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref032] 32.Austin PC, Steyerberg EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med. 2014;33(3):517–35. doi: 10.1002/sim.5941 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref033] 33.Liu Q, Deng J, Yan W, Qin C, Du M, Wang Y, et al. Burden and trends of infectious disease mortality attributed to air pollution, unsafe water, sanitation, and hygiene, and non-optimal temperature globally and in different socio-demographic index regions. Glob Health Res Policy. 2024;9(1):23. doi: 10.1186/s41256-024-00366-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref034] 34.Yang Y, Fu WJ, Land KC. A methodological comparison of age-period-cohort models: the intrinsic estimator and conventional generalized linear models. Sociol Methodol. 2004;34(1):75–110. doi: 10.1111/j.0081-1750.2004.00148.x [DOI] [Google Scholar]

[pone.0342250.ref035] 35.Nasreen S, Wilk P, Mullowney T, Karp I. Age, period, and cohort effects on asthma prevalence in Canadian adults, 1994-2011. Ann Epidemiol. 2020;41:49–55. [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref036] 36.Brigham E, Harris D, Carlsten C, Redlich CA. Occupational health disparities: the pandemic as prism and prod. J Allergy Clin Immunol. 2021;148(5):1148–50. doi: 10.1016/j.jaci.2021.09.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref037] 37.Bonney T, Rospenda KM, Forst L, Conroy LM, Castañeda D, Avelar S, et al. Employment precarity and increased risk of hazardous occupational exposures among residents of high socioeconomic hardship neighborhoods. Ann Work Expo Health. 2022;66(9):1122–35. doi: 10.1093/annweh/wxac062 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref038] 38.Louro H, Gomes BC, Saber AT, Iamiceli AL, Göen T, Jones K, et al. The use of human biomonitoring to assess occupational exposure to PAHs in Europe: a comprehensive review. Toxics. 2022;10(8):480. doi: 10.3390/toxics10080480 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref039] 39.MacDonald RD, Thomas L, Rusk FC, Marques SD, McGuire D. Occupational health and safety assessment of exposure to jet fuel combustion products in air medical transport. Prehosp Emerg Care. 2010;14(2):202–8. doi: 10.3109/10903120903524955 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref040] 40.Jang T-W, Kim Y, Won J-U, Lee J-S, Song J. The standards for recognition of occupational cancers related with polycyclic aromatic hydrocarbons (PAHs) in Korea. Ann Occup Environ Med. 2018;30:13. doi: 10.1186/s40557-018-0224-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref041] 41.Somayaji D, Seo YS, Wilding GE, Noyes E. A multilevel approach to investigate relationships between healthcare resources and lung cancer. Nurs Res. 2022;71(5):360–9. doi: 10.1097/NNR.0000000000000603 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref042] 42.Williams CD, Allo MA, Gu L, Vashistha V, Press A, Kelley M. Health outcomes and healthcare resource utilization among Veterans with stage IV non-small cell lung cancer treated with second-line chemotherapy versus immunotherapy. PLoS One. 2023;18(2):e0282020. doi: 10.1371/journal.pone.0282020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref043] 43.Xu J, Zhang N, Zhang Y, Li P, Han J, Gao S, et al. Personal exposure to source-specific particulate polycyclic aromatic hydrocarbons and systemic inflammation: a cross-sectional study of urban-dwelling older adults in China. Geohealth. 2023;7(12):e2023GH000933. doi: 10.1029/2023GH000933 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref044] 44.Olsson A, Guha N, Bouaoun L, Kromhout H, Peters S, Siemiatycki J, et al. Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: results from a pooled analysis of case-control studies (SYNERGY). Cancer Epidemiol Biomarkers Prev. 2022;31(7):1433–41. doi: 10.1158/1055-9965.EPI-21-1428 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref045] 45.Badal S, Holland K, Foster M, Le AB. Occupational safety and health risks of informal waste workers in Nepal: a mapping review. Saf Health Work. 2025;16(3):325–32. doi: 10.1016/j.shaw.2025.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref046] 46.Jani CT, Kareff SA, Morgenstern-Kaplan D, Salazar AS, Hanbury G, Salciccioli JD, et al. Evolving trends in lung cancer risk factors in the ten most populous countries: an analysis of data from the 2019 Global Burden of Disease Study. EClinicalMedicine. 2025;79:103033. doi: 10.1016/j.eclinm.2024.103033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref047] 47.Di Novi C, Marenzi A. The smoking epidemic across generations, genders, and educational groups: a matter of diffusion of innovations. Econ Hum Biol. 2019;33:155–68. doi: 10.1016/j.ehb.2019.01.003 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref048] 48.Guo H, Huang K, Zhang X, Zhang W, Guan L, Kuang D, et al. Women are more susceptible than men to oxidative stress and chromosome damage caused by polycyclic aromatic hydrocarbons exposure. Environ Mol Mutagen. 2014;55(6):472–81. doi: 10.1002/em.21866 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref049] 49.Mollerup S, Berge G, Baera R, Skaug V, Hewer A, Phillips DH, et al. Sex differences in risk of lung cancer: expression of genes in the PAH bioactivation pathway in relation to smoking and bulky DNA adducts. Int J Cancer. 2006;119(4):741–4. doi: 10.1002/ijc.21891 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref050] 50.Sun Y, Kan Z, Zhang Z-F, Song L, Jiang C, Wang J, et al. Association of occupational exposure to polycyclic aromatic hydrocarbons in workers with hypertension from a northeastern Chinese petrochemical industrial area. Environ Pollut. 2023;323:121266. doi: 10.1016/j.envpol.2023.121266 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref051] 51.Borgulat J, Staszewski T. Fate of PAHs in the vicinity of aluminum smelter. Environ Sci Pollut Res Int. 2018;25(26):26103–13. doi: 10.1007/s11356-018-2648-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref052] 52.Rehman MYA, Taqi MM, Hussain I, Nasir J, Rizvi SHH, Syed JH. Elevated exposure to polycyclic aromatic hydrocarbons (PAHs) may trigger cancers in Pakistan: an environmental, occupational, and genetic perspective. Environ Sci Pollut Res Int. 2020;27(34):42405–23. doi: 10.1007/s11356-020-09088-2 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref053] 53.Yuan H, Wang Y, Duan H. Risk of lung cancer and occupational exposure to polycyclic aromatic hydrocarbons among workers cohorts - worldwide, 1969-2022. China CDC Wkly. 2022;4(17):364–9. doi: 10.46234/ccdcw2022.085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0342250.ref054] 54.Wang W, Xu J, Qu X, Lin D, Yang K. Current and future trends of low and high molecular weight polycyclic aromatic hydrocarbons in surface water and sediments of China: insights from their long-term relationships between concentrations and emissions. Environ Sci Technol. 2022;56(6):3397–406. doi: 10.1021/acs.est.1c05323 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref055] 55.Driscoll TR, Carey RN, Peters S, Glass DC, Benke G, Reid A, et al. The Australian work exposures study: occupational exposure to polycyclic aromatic hydrocarbons. Ann Occup Hyg. 2016;60(1):124–31. doi: 10.1093/annhyg/mev057 [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref056] 56.Bottai M, Selander J, Pershagen G, Gustavsson P. Age at occupational exposure to combustion products and lung cancer risk among men in Stockholm, Sweden. Int Arch Occup Environ Health. 2016;89(2):271–5. doi: 10.1007/s00420-015-1070-x [DOI] [PubMed] [Google Scholar]

[pone.0342250.ref057] 57.Vimercati L, Bisceglia L, Cavone D, Caputi A, De Maria L, Delfino MC, et al. Environmental monitoring of PAHs exposure, biomarkers and vital status in coke oven workers. Int J Environ Res Public Health. 2020;17(7):2199. doi: 10.3390/ijerph17072199 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Global burden and trends of tracheal, bronchus, and lung cancer attributed to occupational exposure to polycyclic aromatic hydrocarbons in regions with different sociodemographic index, 1990–2021

Jiansheng Lin

Xiaowei Xie

Xinyang Zheng

Haizhan Shi

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study design and population

Occupational exposure to PAHs and TBL cancer

Metrics and variables

Methodology to calculate attributable burden in the GBD framework

Data collection and processing

Statistical analyses

Results

Trends of TBL cancer burden attributed to occupational exposure to PAHs in regions with different SDI from 1990 to 2021

Table 1. Burden and trends of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990-2021.

Fig 1. EAPC of ASDR and age-standardized DALYs rate of TBL cancer attributed to occupational exposure to PAHs from 1990 to 2021.

Fig 2. Annual change in ASDR of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990-2021.

Age-specific TBL cancer burden attributed to occupational exposure to PAHs in regions with different SDI

Fig 3. Age-specific death rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990-2021.

Fig 4. EAPC of age-specific death rate of TBL cancer attributed to occupational exposure to PAHs in regions with different SDI, 1990-2021.

Associations between SDI and TBL cancer burden attributed to occupational exposure to PAHs in 21 GBD regions

Fig 5. Associations between ASDR of TBL cancer attributed to occupational exposure to PAHs and SDI in GBD regions, 1990-2021.

APC analysis for TBL cancer burden attributed to occupational exposure to PAHs in regions with different SDI

Fig 6. Age-period-cohort analysis for death rate of TBL cancer attributed to occupational exposure to PAHs.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Onome Oghenetega

Roles

Author response to Decision Letter 1

Decision Letter 1

Igor Burstyn

Roles

Author response to Decision Letter 2

Decision Letter 2

Igor Burstyn

Roles

Acceptance letter

Igor Burstyn

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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