Health inequities in the comorbid risk of pneumoconiosis and chronic obstructive pulmonary disease: a nationwide study of age-related risk heterogeneity

Health inequities in the comorbid risk of pneumoconiosis and chronic obstructive pulmonary disease: a nationwide study of age-related risk heterogeneity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health inequities in the comorbid risk of pneumoconiosis and chronic obstructive pulmonary disease: a nationwide study of age-related risk heterogeneity Keliang Liu, Yanhong Ren, Lifang Zhou, Xiangpei Lyu, Huanqiang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9064711/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Pneumoconiosis patients are at heightened risk of developing chronic obstructive pulmonary disease (COPD). While age is a known risk factor, its impact across different populations is unclear. Methods We included 9,964 pneumoconiosis patients from 27 Chinese provinces. Factors were identified using multivariable logistic regression, and the non-linear age–COPD risk association was examined with restricted cubic spline (RCS) modelling. Stratified analysis and interaction tests assessed sociodemographic heterogeneity. Sensitivity analyses comprised RCS models with varying knots and propensity score matching. Results The prevalence of COPD among patients with pneumoconiosis was 24.1%. Each additional year of age was associated with a significant increase in COPD risk (adjusted odds ratio [aOR] = 1.03, 95% confidence interval [CI]: 1.03–1.04). RCS analysis revealed a significant nonlinear relationship, with risk increasing steeply beyond age 55 years (P for overall < 0.001; P for nonlinear = 0.017). we identified subgroups with distinct risk patterns. Rural residents and those without work‑related injury insurance (WRII) demonstrated an earlier inflection point (53 years) followed by a steep increase in risk. In contrast, urban residents and individuals with WRII exhibited a later inflection point (at 61 and 59 years, respectively) and a more gradual rise in risk. The inflection point differed by 8 years between urban and rural residents and by 6 years between individuals with and without WRII. Sensitivity analyses verified the robustness of these findings. Conclusion The nonlinear relationship between age and COPD risk in pneumoconiosis is significantly modified by sociodemographic factors. Occupational Health Pneumoconiosis COPD Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pneumoconiosis remains a leading occupational respiratory disease globally, contributing substantially to annual morbidity and mortality rates, particularly in developing countries [ 1 , 2 ]. In China, it has long been the most prevalent occupational illness, pathologically defined as diffuse pulmonary fibrosis caused by occupational dust exposure [ 3 ]. Patients with pneumoconiosis suffer progressive lung function decline and are at high risk of serious complications, notably chronic obstructive pulmonary disease (COPD) [ 4 ]. The coexistence of these conditions accelerates clinical deterioration and multiplies the overall disease burden. COPD, characterized by persistent airflow limitation and chronic respiratory symptoms resulting from chronic airway and parenchymal inflammation, represents a major global public health challenge [ 5 ]. However, important gaps persist in understanding COPD risk in pneumoconiosis patients, especially regarding how risk factors are heterogeneously distributed across different subpopulations [ 6 , 7 ]. Age is a well-established risk factor for both diseases [ 8 – 10 ]. Yet, existing studies are largely limited by the assumption of a homogeneous, linear age effect across the entire population [ 11 , 12 ]. This presumption overlooks marked inter-individual differences in socioeconomic (e.g., residence and work-related injury insurance) and clinical profiles, thereby masking the underlying social determinants of risk and impeding the development of targeted public health interventions. To address these limitations, we conducted a national cross-sectional study that extends beyond conventional linear analyses. We aimed to precisely characterize the nonlinear dose-response relationship between age and COPD risk among patients with pneumoconiosis, and to systematically quantify the heterogeneity of this relationship across key sociodemographic and clinical subgroups. We hypothesized that the age-related risk is not uniform but is significantly modified by multiple contextual factors. Our findings aim to provide actionable evidence for identifying high-risk subgroups and informing stratified prevention strategies. Methods Study design and participants Data for this study were derived from a nationwide cross-sectional survey conducted from December 2017 to June 2021. The survey covered 27 provinces (excluding Tianjin, Shanghai, Hainan, Tibet, Hong Kong, Macau, and Taiwan), yielding a total of 11,181 completed questionnaires [ 13 ]. Survey items included demographic and sociological characteristics, comorbidities, and clinical features. Data were collected via face-to-face interviews conducted by certified occupational disease physicians and nurses who had undergone standardized training. In some instances, patients completed questionnaires in different regions or hospitals, or at different time points within the same hospital, resulting in the exclusion of 378 duplicate questionnaires. Duplicate questionnaires were identified through logic-based verification using multiple identifiers, including ID number, name, and contact information. Additionally, 180 respondents were under medical observation: these individuals had evidence of pulmonary lesions, but the density of small nodules on chest radiographs did not meet the current criteria for stage I pneumoconiosis, requiring further medical follow-up. Furthermore, 659 additional questionnaires were excluded owing to missing key data that could not be supplemented during follow-up. After excluding these 1,217 questionnaires (378 duplicate responses, 180 participants under medical observation, and 659 with missing key data), a total of 9,964 valid questionnaires were included in the final analysis. Based on an expected COPD prevalence of 25%, with α = 0.05, 90% power (β = 0.10), and accounting for 10% loss to follow-up, the minimum required sample size was 8,900. This study enrolled 9,964 participants, exceeding this target and ensuring adequate statistical power. This study was approved by the ethics Committee. Written informed consent was obtained from all participants prior to data collection. Assessment of outcomes The primary outcome of this study was COPD. Eligible participants with pneumoconiosis were required to either hold an official occupational disease diagnosis certificate or have received a clinical diagnosis from a physician qualified in pneumoconiosis assessment. In line with previous studies [ 4 , 13 ], comorbidity data were collected via questionnaire, specifically by querying participants about a history of relevant complications. COPD was diagnosed based on two complementary criteria: documentation of COPD in medical records and a post-bronchodilator forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) ratio < 70% (consistent with the Global Initiative for Chronic Obstructive Lung Disease [GOLD] criteria). Lung function tests were performed by uniformly trained physicians from participating occupational disease prevention and control institutions, using calibrated equipment and strictly adhering to the standardized procedures outlined in the GOLD guidelines to minimize measurement bias. Data collection Demographic and sociological characteristics included age, sex, body mass index (BMI), residence(urban/rural), birth place, smoking index, alcohol intake, education level, marital status, employment status, annual personal and family income per capita, accumulated dust exposure time, work-related injury insurance, region of China, season of survey, and source of cases. BMI was calculated as weight (kg) divided by the square of height (m 2 ). The smoking index was determined by multiplying the number of cigarettes smoked per day by the number of years of smoking [ 14 ]. Smoking index was categorized into four groups: <100, 100–199, 200–399, and ≥ 400. Comorbidities included COPD, pulmonary tuberculosis, pulmonary bullae or pneumothorax, cardiovascular, diabetes, and hypertension. Clinical characteristics encompassed type of pneumoconiosis, stage of pneumoconiosis, self-rated health, history of using immune-enhancing drugs, and history of hormone therapy for pneumoconiosis. Pneumoconiosis types were classified into three groups: silicosis, coal workers’ pneumoconiosis (CWP), and other types. Pneumoconiosis staging was divided into four categories: clinical cases, stage I, stage II, and stage III. Clinical cases referred to patients clinically diagnosed with pneumoconiosis who did not possess an official occupational disease certificate, primarily comprising migrant workers. Self-rated health status was evaluated using the European Quality of Life Five-Dimension Scale (EQ-5D) [ 15 , 16 ], which employs a 20 cm vertical visual analog scale (VAS) ranging from 0 (worst health) to 100 (best health). Statistical analysis Categorical data were presented as counts and percentages (or proportions) and analyzed using the chi-square test or Fisher’s exact test, as appropriate. To assess the association between age and COPD risk, univariate and multivariate logistic regression models were constructed, with odds ratios (ORs) and 95% confidence intervals (95% CIs) reported. Multicollinearity was evaluated by calculating the variance inflation factor (VIF) and tolerance via a multivariate linear regression model; variables with tolerance > 0.1 and VIF < 10 were retained [ 17 ]. Additionally, fully adjusted restricted cubic spline (RCS) logistic regression analyses with four knots were performed to examine the non-linear and dose-response relationships between age and COPD risk. The knot numbers were selected based on methodological conventions (3–5 knots as standard for RCS) to balance model parsimony (3 knots) and flexibility (5 knots), while the knot positions followed quantile-based placement to ensure representative coverage of age distribution. Subgroup and interaction analyses were conducted to explore potential effect modification. In sensitivity analyses, we repeated the RCS analyses in the fully adjusted model using three and five knots, respectively, to evaluate the stability of identified risk inflection points and observed non-linear trends. We conducted 1:1 propensity score matching (PSM) via nearest-neighbor matching without replacement and a caliper width of 0.2. An acceptable balance between groups was defined as a standardized mean difference (SMD) < 0.10. Based on the matched dataset, stratified RCS analyses with four knots were performed to further validate the stability of age-related risk inflection points within these key subgroups. All statistical analyses were performed using R software (version 4.3.3), and a two-sided p-value < 0.05 was considered significant. Results Baseline characteristics of participants A total of 9,964 participants were included in this study, with an overall COPD prevalence of 24.1%. The mean age was 57.9 ± 11.7 years for the total population and 56.3 ± 10.9 years for those without COPD. Among COPD cases, mean age varied by stage: 67.9 ± 12.0 years for stage I, 63.3 ± 12.6 years for stage II, 56.1 ± 10.9 years for stage III, and 57.7 ± 10.4 years for clinical cases. Significant differences were observed between groups with and without COPD in terms of age, sex, residence (urban/rural), birth place, education level, marital status, body mass index (BMI), smoking index, alcohol intake, region of China, work-related injury insurance (WRII), self-rated health (SRH), comorbid pulmonary bullae or pneumothorax, and type of pneumoconiosis (all P < 0.001; supplementary table 1 ). Prevalence and risk factors of COPD in patients with pneumoconiosis The prevalence of COPD was 19.8% among rural pneumoconiosis patients and 29.7% among urban patients. Univariate analysis indicated that several factors were associated with an increased risk of COPD in these patients, including demographic and socioeconomic characteristics (age, residence, BMI, smoking index, alcohol intake, employment status, annual personal and family income per capita, and WRII), comorbidities (pulmonary bullae or pneumothorax, pulmonary tuberculosis, cardiovascular, diabetes, and hypertension), clinical characteristics (type and stage of pneumoconiosis, self-rated health), as well as history of using immune-enhancing drugs, and history of hormone therapy for pneumoconiosis. Specifically, smoking index > 399 (OR = 1.54), BMI < 18.5 kg/m 2 (OR = 2.48), stage III pneumoconiosis (OR = 1.52), and the presence of pulmonary bullae or pneumothorax (OR = 3.75) were all significantly associated with a higher risk of COPD (all P < 0.05; Table 1 and supplementary table 2). Table 1 Prevalence and ORs of COPD Associated with Risk Factors and Social Determinants among Pneumoconiosis Patients in 27 Provinces of China, Stratified by Urban and Rural Residence Items Prevalence (%) in patients (95%CI) Living in rural areas Living in urban areas Total OR 95%CI P Smoking index < 100 15.1 (13.8–16.6) 28.3 (26.5–30.2) 21.3 (20.2–22.5) 1.00 Reference 100–199 21.4 (18.2–25.0) 21.0 (17.3–25.3) 21.3 (18.8–24.0) 1.00 0.84–1.18 0.967 200–399 23.6 (21.0-26.4) 29.2 (25.7–32.9) 25.8 (23.7–28.0) 1.29 1.13–1.47 399 24.7 (22.7–27.0) 36.4 (33.5–39.4) 29.4 (27.7–31.2) 1.54 1.38–1.72 < 0.001 P for trend < 0.001 < 0.001 < 0.001 BMI < 18.5kg/m 2 35.7 (31.8–39.8) 39.8 (33.4–46.6) 36.8 (33.4–40.4) 2.48 2.00-3.07 < 0.001 18.5–23.9kg/m 2 19.8 (18.5–21.2) 29.1 (27.1–31.1) 23.3 (22.2–24.4) 1.00 Reference 24.0–27.9kg/m 2 15.7 (13.9–17.6) 29.5 (27.4–31.7) 23.2 (21.7–24.7) 0.55 0.45–0.66 < 0.001 ≥ 28.0kg/m 2 12.1 (8.9–16.1) 28.4 (24.4–32.8) 21.5 (18.7–24.6) 0.63 0.45–0.88 0.007 P for trend < 0.001 0.113 60 10.7 (9.4–12.2) 19.3 (17.6–21.0) 15.2 (14.1–16.4) 0.43 0.39–0.48 < 0.001 P for difference < 0.001 < 0.001 < 0.001 WRII No 17.4 (16.2–18.6) 17.3 (15.2–19.5) 17.4 (16.3–18.4) 1.00 Reference Yes 25.0 (23.1–27.1) 34.4 (32.8–36.1) 31.0 (29.7–32.3) 2.14 1.94–2.35 < 0.001 P for difference < 0.001 < 0.001 < 0.001 Type of pneumoconiosis Others 8.1 (5.6–11.5) 25.6 (22.0-29.6) 18.6 (16.1–21.4) 1.00 Reference Silicosis 18.6 (17.3–20.0) 32.1 (29.9–34.4) 23.2 (22.0-24.4) 1.32 1.09–1.59 0.004 CWP 23.5 (21.7–25.3) 28.9 (27.0-30.8) 26.2 (24.9–27.5) 1.55 1.29–1.87 < 0.001 P for difference < 0.001 0.001 < 0.001 Stages of pneumoconiosis Stage I 11.5 (10.1–13.1) 28.4 (26.7–30.1) 21.7 (20.5–22.9) 1.00 Reference Stage II 19.7 (17.7–21.7) 34.6 (31.7–37.7) 25.5 (23.8–27.3) 1.24 1.10–1.39 < 0.001 Stage III 28.8 (26.7–31.0) 32.5 (28.4–37.0) 29.6 (27.7–31.6) 1.52 1.35–1.71 < 0.001 Clinical cases (no classified stage) 20.1 (17.2–23.2) 18.5 (14.1–24.0) 19.7 (17.2–22.4) 0.88 0.74–1.06 0.172 P for trend < 0.001 0.344 0.054 Pulmonary bullae or pneumothorax No 16.1 (15.1–17.2) 27.6 (26.2–29.0) 21.3 (20.5–22.2) 1.00 Reference Yes 46.5 (42.8–50.2) 59.6 (53.9–65.2) 50.4 (47.2–53.5) 3.75 3.27–4.29 < 0.001 P for difference < 0.001 < 0.001 < 0.001 Data are expressed as number(%); COPD: Chronic obstructive pulmonary disease; OR: Odds ratio; CI: Confidence interval; BMI: Body mass index; WRII: Work-related injury insurance; SRH: Self-Rated Health; CWP: Coal workers’ pneumoconiosis. Multivariate analysis of risk factors for COPD in patients with pneumoconiosis Prior to regression modeling, diagnostic checks confirmed that all variables had a tolerance > 0.1 and a variance inflation factor (VIF) < 10, indicating no concern for multicollinearity(supplementary table 3). In the model including all enrolled patients, significant risk factors for COPD development among individuals with pneumoconiosis were identified as: increasing age, smoking index ≥ 200, BMI < 18.5 kg/m 2 , residence in western or northeastern China, with WRII, comorbid cardiovascular diseases, coal workers’ pneumoconiosis (CWP), advanced pneumoconiosis stage, cumulative dust exposure of 20–24 years, history of immune-enhancing drug use, history of hormone therapy for pneumoconiosis, and comorbid pulmonary bullae or pneumothorax (all P < 0.05). Notably, the association between WRII and increased COPD risk may reflect detection bias, as insured individuals typically undergo more systematic health monitoring, leading to higher diagnostic identification of COPD. Stratified analyses by urban/rural residence showed that the markedly elevated COPD risk observed in the northeastern region was primarily confined to urban patients. The risk associated with CWP was higher among urban patients than rural counterparts. Furthermore, case source, survey season, history of immune-enhancing drug use, and history of hormone therapy for pneumoconiosis were also significantly associated with COPD risk, with most of these associations differing between urban and rural populations (Table 2 and supplementary table 4). Table 2 Multivariable-adjusted odds ratios (ORs) for COPD-related risk factors among pneumoconiosis patients in 27 provinces of China Items All pneumoconiosis cases OR (95%CI) P Living in rural areas OR (95%CI) P Living in urban areas OR (95%CI) P Age 1.03 (1.03–1.04) <0.001 1.03 (1.02–1.04) < 0.001 1.05 (1.04–1.06) < 0.001 Smoking index < 100 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 100–199 1.08 (0.88–1.32) 0.449 1.27 (0.97–1.66) 0.080 0.88 (0.63–1.21) 0.430 200–399 1.60 (1.37–1.89) <0.001 1.61 (1.30-2.00) < 0.001 1.67 (1.29–2.15) 399 1.77 (1.54–2.03) <0.001 1.63 (1.35–1.97) < 0.001 1.88 (1.52–2.32) < 0.001 Alcohol intake Non-drinkers 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Current drinkers 0.79 (0.67–0.92) 0.002 0.64 (0.51–0.81) < 0.001 0.97 (0.78–1.21) 0.806 Former drinkers 1.03 (0.90–1.18) 0.655 1.26 (1.06–1.50) 0.009 0.94 (0.75–1.18) 0.610 BMI < 18.5kg/m 2 1.40 (1.14–1.70) 0.001 1.48 (1.17–1.88) 0.001 1.28 (0.86–1.89) 0.222 18.5–23.9kg/m 2 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 24.0–27.9kg/m 2 0.90 (0.79–1.02) 0.101 0.86 (0.71–1.04) 0.114 0.90 (0.75–1.09) 0.285 ≥ 28.0kg/m 2 0.84 (0.67–1.04) 0.112 0.62 (0.42–0.92) 0.017 0.92 (0.69–1.22) 0.559 Region of China Eastern 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Central 0.79 (0.66–0.94) 0.009 1.97 (1.51–2.57) < 0.001 0.28 (0.20–0.37) < 0.001 Western 2.20 (1.88–2.58) <0.001 3.15 (2.50–3.97) < 0.001 1.29 (0.99–1.67) 0.058 Northeastern 8.81 (7.20-10.79) <0.001 1.88 (1.09–3.26) 0.024 9.70 (7.45–12.62) < 0.001 With WRII 1.37 (1.19–1.56) <0.001 1.43 (1.20–1.71) 60 0.65 (0.57–0.74) <0.001 0.61 (0.51–0.74) < 0.001 0.76 (0.63–0.91) 0.003 Type of pneumoconiosis Others 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Silicosis 1.24 (0.98–1.58) 0.073 1.53 (0.96–2.42) 0.072 1.27 (0.94–1.72) 0.125 CWP 2.18 (1.71–2.77) <0.001 2.07 (1.31–3.28) 0.002 3.34 (2.43–4.60) < 0.001 Stages of pneumoconiosis Stage I 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Stage II 1.37 (1.19–1.58) <0.001 1.45 (1.16–1.80) < 0.001 1.53 (1.24–1.88) < 0.001 Stage III 1.68 (1.43–1.98) <0.001 1.98 (1.59–2.47) < 0.001 1.40 (1.04–1.89) 0.028 Clinical cases (no classified stage) 1.59 (1.26-2.00) <0.001 1.65 (1.24–2.21) < 0.001 1.49 (0.94–2.37) 0.092 Pulmonary bullae or pneumothorax 2.80 (2.36–3.33) <0.001 2.56 (2.08–3.14) < 0.001 3.49 (2.50–4.86) < 0.001 Data are expressed as number(%); COPD: Chronic obstructive pulmonary disease; OR: Odds ratio; CI: Confidence interval; BMI: Body mass index; WRII: Work-related injury insurance; SRH: Self-Rated Health; CWP: Coal workers’ pneumoconiosis. The nonlinear association between age and the risk of COPD The fully adjusted restricted cubic spline (RCS) regression model revealed a significant non-linear positive association between age and the risk of comorbid COPD among patients with pneumoconiosis (P for overall < 0.001; P for nonlinear = 0.017). With 55 years as the reference point (OR = 1), the risk of COPD increased continuously with advancing age, showing a pronounced accelerating trend (Fig. 1 ). Subgroup analysis To further investigate the relationship between age and the risk of COPD, we divided patients into two subgroups using 55 years as the cutoff. As shown in Fig. 2 and supplementary Fig. 1, among all patients, age ≥ 55 years was identified as a significant risk factor for COPD comorbidity in individuals with pneumoconiosis (OR = 1.32, 95%CI: 1.16–1.50). Interaction analysis indicated that residence, BMI, region of China, WRII, SRH, pneumoconiosis stage, and type of pneumoconiosis significantly modified the association between age and COPD risk (P for interaction < 0.05). Stratified RCS Analysis of the Association Between Age and COPD To investigate heterogeneity in the association between age and COPD, we conducted stratified RCS analyses based on factors that showed significant interactions in subgroup analyses. The results revealed substantial differences in the age-related effect across patient characteristics, primarily manifesting as either a risk amplification or a risk moderation pattern. Stratified analyses revealed pronounced heterogeneity in age-related risk patterns across subgroups. A steeper increase in risk beyond a specific age threshold was observed in multiple subgroups, including rural residents, individuals without WRII, those with Stage I pneumoconiosis, and eastern residents (Figs. 3 a, 3 c; supplementary Figs. 2a, 3c, 4d, 5a, 6a). In contrast, a more gradual rise in risk was identified in subgroups such as urban residents, individuals with WRII, and those with Stage II pneumoconiosis (Figs. 3 b, 3 d; supplementary Figs. 2b, 3b). This marked divergence highlights substantial heterogeneity in how age modifies COPD risk among different patient profiles. The age at which COPD risk began to increase (OR > 1) varied substantially across subgroups, defining a disparity of nearly a decade. The earliest thresholds (52–53 years) were observed among rural residents, individuals without WRII, and western residents. While the latest thresholds (59–61 years) were seen in urban residents, individuals with WRII, and eastern or northeastern residents. Sensitivity analyses To validate the robustness of our primary findings, we conducted comprehensive sensitivity analyses that consistently supported the initial conclusions. First, to evaluate potential model dependency in the nonlinear association between age and COPD risk, we performed restricted cubic spline (RCS) analyses using both three-knot and five-knot specifications. Both configurations demonstrated a significant nonlinear positive association (both P < 0.05; supplementary Fig. 7), with the risk inflection point consistently observed at 55 years without notable variation. Second, we established a balanced cohort through 1:1 nearest-neighbor propensity score matching, generating 2,267 matched pairs of COPD and non-COPD patients with pneumoconiosis. All covariates exhibited standardized mean differences below 0.1 (supplementary table 5). RCS analysis within this matched cohort revealed a COPD risk inflection point at 54 years, further corroborating the robustness of our primary results. Finally, stratified RCS analyses based on residence and WRII in the matched dataset revealed pronounced disparities: rural residents showed an inflection point at 54 years compared to 64 years for urban residents-a 10-year gap. Similarly, individuals without WRII exhibited an inflection point at 54 years versus 61 years among those with WRII, representing a 7-year difference (supplementary Fig. 8). These stratified findings closely aligned with trends observed in the overall sample, confirming that the heterogeneity in age-related COPD risk trajectories across subgroups defined by social determinants persists after accounting for potential baseline confounding. Discussion In this nationwide study of 9,964 patients with pneumoconiosis, we delineate for the first time a nonlinear relationship between age and COPD risk that is substantially modified by sociodemographic factors, notably residence and work-related injury insurance status. These findings refine the understanding of COPD development in this high-risk occupational population and provide critical evidence for risk stratification, which is of significant value for guiding public health planning, targeted population monitoring, and early intervention strategies in the public health domain. Although pneumoconiosis and COPD have traditionally been regarded as distinct clinical entities, accumulating evidence highlights considerable pathophysiological overlap. Both conditions involve chronic pulmonary inflammation, protease-antiprotease imbalance, and oxidative stress, which are core processes that drive progressive and irreversible airflow limitation [ 18 , 19 ]. A meta-analysis reported a detection rate of 26.4% for comorbid COPD in patients with pneumoconiosis [ 20 ]. In our study, the prevalence of COPD among pneumoconiosis patients was 24.1%, which is consistent with this pooled estimate and significantly higher than the 13.6% reported in the general Chinese population aged 40 years and above [ 21 ]. This finding supports the notion that long-term occupational dust exposure represents an independent and potent risk factor for COPD [ 22 ]. We further identified a graded association between pneumoconiosis severity and COPD risk [ 7 ], reinforcing the concept that dust-induced structural lung damage and sustained inflammatory responses synergistically promote the development and progression of COPD [ 23 ]. This study confirms that age is an independent risk factor for COPD in patients with pneumoconiosis, consistent with findings from most epidemiological studies [ 6 , 9 , 24 ]. With advancing age, physiological changes including reduced pulmonary elasticity and impaired reparative capacity, combined with the inherent pulmonary fibrosis and inflammatory damage of pneumoconiosis, exert a synergistic effect that accelerates the development and progression of COPD [ 25 , 26 ]. Notably, restricted cubic spline (RCS) analysis identified a non-linear relationship wherein the risk of COPD among patients with pneumoconiosis exhibits an inflection point and rises sharply at 55 years of age. This core finding was further validated through sensitivity analyses: altering the number of knots in the RCS model did not compromise the significance of the non-linear relationship, and the risk inflection point remained consistent at 55 years. This inflection point may represent a critical threshold at which pulmonary compensatory mechanisms become exhausted, leading to a marked increase in COPD risk. Notably, this 55-year inflection point in Chinese pneumoconiosis patients is substantially earlier than the age at which COPD incidence begins to show exponential growth in the general population of high sociodemographic index (SDI) countries (around 60 years) [ 12 , 27 , 31 ]. This discrepancy may be attributed to the synergistic effects of long-term occupational dust exposure and age-related physiological decline. Critically, we found that the effect of age is not uniform but is substantially modified by sociodemographic and clinical characteristics. A striking eight-year disparity in risk-onset age was observed between rural and urban residents, with inflection points at 53 and 61 years, respectively. In the sensitivity analysis, after controlling for multiple baseline confounders through propensity score matching (PSM), we not only replicated a similar risk inflection point at 54 years of age within the matched cohort, but also confirmed that the COPD risk inflection point for rural patients occurred 10 years earlier than that for urban patients. This finding suggests that rural patients experience not only occupational lung damage but also cumulative health disadvantages stemming from limited healthcare access and lower health literacy [ 28 ]. A more complex pattern emerged regarding work-related injury insurance (WRII) status. Although insured status was associated with a higher COPD prevalence (31.0% vs 17.4%) and emerged as an independent risk factor (OR = 1.37) in multivariable analysis, this likely reflects detection bias due to more systematic health surveillance. Stratified RCS analysis further revealed that insured patients experienced a six-year delay in the age of steep risk escalation (59 years) compared to uninsured individuals (53 years). Notably, in the sensitivity analysis, the inflection point for COPD risk occurred seven years earlier among patients without WRII compared to those with WRII. This indicates that the stable employment and protective working conditions linked to WRII confer a delay in disease manifestation, though they cannot ultimately offset age-related physiological decline [ 29 , 30 ]. This study demonstrates a significant nonlinear association between age and COPD risk among patients with pneumoconiosis, which is substantially modified by sociodemographic factors. The primary risk inflection point occurred at 55 years of age. Crucially, residence and WRII status emerged as key modifiers of this risk pattern, underscoring the role of health inequities. Further variation in the inflection point was observed according to pneumoconiosis stage, self-rated health, and geographic region, as detailed in the Supplementary Material. Collectively, patients aged 55 years or older, particularly those who live in rural areas, without WRII, or clinical cases, constitute the highest-risk subgroup. This group should be prioritized for intensified pulmonary function monitoring and early intervention. Addressing these sociodemographically driven disparities is essential to mitigate the disproportionate burden of COPD in this occupational population. We also acknowledge important limitations. The cross-sectional design precludes causal inference, and some variables (such as self-rated health and accumulated dust exposure time) are prone to potential recall bias due to their self-reported nature. Importantly, while dust exposure duration was captured via questionnaire, data on exposure intensity or cumulative exposure dose were unavailable, which may limit the precision of exposure-response analyses. Our identification of an earlier COPD risk inflection point in socioeconomically disadvantaged subgroups aligns with global observations that social determinants exacerbate occupational lung disease burden in low- and middle-income countries [ 31 ]. Future prospective cohort studies with objective measurements including dust exposure intensity and longitudinal lung function monitoring in multinational populations are necessary to validate these associations and clarify potential causal mechanisms. Declarations Competing interests The authors report no conflicts of interest. Ethics approval and consent to participate This study was approved by the Ethics Committee of the National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention (Approval No. 201720). Written informed consent was obtained from all participants prior to data collection. Funding National Major Science and Technology Project of China (No. 2024ZD0528901); National Natural Science Foundation of China (No. 82574060). Author Contribution Conception and design: H W, K L, Y R. Analysis and interpretation of data: K L, L Z, X L. Drafting the article or revising it critically for important intellectual content: K L, H W. Final approval of the version to be submitted for revision: H W, K L, Y R. All authors had full access to all the study data and accepted responsibility for submitting this work. Acknowledgement The authors extend their gratitude to all study participants, the survey teams across the 27 provinces, and the project development and management teams for their invaluable contributions. Data Availability The original individual participant data cannot be shared publicly due to containing sensitive personal information. De-identified summary data are available from the corresponding author upon reasonable request and with ethics committee approval. References Cullinan P, Muñoz X, Suojalehto H, et al. Occupational lung diseases: from old and novel exposures to effective preventive strategies. Lancet Respir Med. 2017;5(5):445–55. Huang X, Liu W, Yao Y, et al. 30-Year Trends in the Disease Burden, Incidence, and Prevention of Pneumoconiosis. China CDC Wkly. 2023;5(38):856–60. Li J, Yin P, Wang H, et al. The burden of pneumoconiosis in China: an analysis from the Global Burden of Disease Study 2019. BMC Public Health. 2022;22(1):1114. Wang H, Lyu X, Luo D, et al. Prevalence and Types of Comorbidities in Pneumoconiosis - China, 2018–2021. China CDC Wkly. 2023;5(38):837–43. Wang C, Xu J, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet. 2018;391(10131):1706–17. Peng Y, Li X, Cai S, et al. Prevalence and characteristics of COPD among pneumoconiosis patients at an occupational disease prevention institute: a cross-sectional study. BMC Pulm Med. 2018;18(1):22. Fan Y, Xu W, Wang Y, et al. Association of occupational dust exposure with combined chronic obstructive pulmonary disease and pneumoconiosis: a cross-sectional study in China. BMJ Open. 2020;10(9):e038874. Wang Z, Lin J, Liang L, et al. Global, regional, and national burden of chronic obstructive pulmonary disease and its attributable risk factors from 1990 to 2021: an analysis for the Global Burden of Disease Study 2021. Respir Res. 2025;26(1):2. Yang H, Yang Y, Wang F, et al. Clinical and Prognostic Differences in Mild to Moderate COPD With and Without Emphysema. Chest. 2025;167(3):724–35. Yüksel Yavuz M, Coşkun Beyan A, Ayik Türk M, et al. Survival analysis of patients with pneumoconiosis followed in occupational medicine clinics: 10 years experience. BMC Pulm Med. 2025;25(1):236. Wang Y, Han R, Ding X, et al. Chronic obstructive pulmonary disease across three decades: trends, inequalities, and projections from the Global Burden of Disease Study 2021. Front Med (Lausanne). 2025;12:1564878. Fragoso CA. Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) in Aging Populations. COPD. 2016;13(2):125–9. Wang H, Dai H, He J, et al. Epidemiological characteristics of pulmonary tuberculosis in patients with pneumoconiosis based on its social determinants and risk factors in China: a cross-sectional study from 27 provinces. Chin Med J (Engl). 2022;135(24):2984–97. Sulsky SI, Fuller WG, Van Landingham C, et al. Evaluating the association between menthol cigarette use and the likelihood of being a former versus current smoker. Regul Toxicol Pharmacol. 2014;70(1):231–41. Made AD, Peters RW, Verheul C, et al. Proximal hamstring tendon avulsions: comparable clinical outcomes of operative and non-operative treatment at 1-year follow-up using a shared decision-making model. Br J Sports Med. 2022;56(6):340–8. Zhuo L, Xu L, Ye J, et al. Time Trade-Off Value Set for EQ-5D-3L Based on a Nationally Representative Chinese Population Survey. Value Health. 2018;21(11):1330–7. Cui Y, Zhang J, Wang Y, et al. Multivariate predictive model of the therapeutic effects of metoprolol in paediatric vasovagal syncope: a multi-centre study. EBioMedicine. 2025;113:105595. Kurt OK, Ergun D, Anlar HG, et al. Evaluation of Oxidative Stress Parameters and Genotoxic Effects in Patients With Work-Related Asthma and Silicosis. J Occup Environ Med. 2023;65(2):146–51. Rahman I, Adcock IM. Oxidative stress and redox regulation of lung inflammation in COPD. Eur Respir J. 2006;28(1):219–42. Deng L, Wang Z. Meta-analysis of the prevalence of common complications among pneumoconiosis patients. J Chin Med Libr Assoc. 2024;33:45–52. (In Chinese with English abstract). Fang L, Gao P, Bao H, et al. Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir Med. 2018;6(6):421–30. Alif SM, Dharmage SC, Bowatte G, et al. Occupational exposure and risk of chronic obstructive pulmonary disease: a systematic review and meta-analysis. Expert Rev Respir Med. 2016;10(8):861–72. Eisner MD, Anthonisen N, Coultas D, et al. An official American Thoracic Society public policy statement: Novel risk factors and the global burden of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2010;182(5):693–718. Ding F, Liu W, Hu X, et al. Factors related to the progression of chronic obstructive pulmonary disease: a retrospective case-control study. BMC Pulm Med. 2025;25(1):5. Qi XM, Luo Y, Song MY, et al. Pneumoconiosis: current status and future prospects. Chin Med J (Engl). 2021;134(8):898–907. Torrance BL, Haynes L. Cellular senescence is a key mediator of lung aging and susceptibility to infection. Front Immunol. 2022;13:1006710. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Soller B, Myers O, Sood A. Transfer of Knowledge on Pneumoconiosis Care Among Rural-Based Members of a Digital Community of Practice: Cross-Sectional Study. JMIR Form Res. 2024;8:e52414. Thygesen LC, Hvidtfeldt UA, Mikkelsen S, et al. Quantification of the healthy worker effect: a nationwide cohort study among electricians in Denmark. BMC Public Health. 2011;11:571. Salvi SS, Barnes PJ. Chronic obstructive pulmonary disease in non-smokers. Lancet. 2009;374(9691):733–43. Adeloye D, Song P, Zhu Y, et al. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med. 2022;10(5):447–58. Additional Declarations No competing interests reported. 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OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9064711/v1/39123e286d5d6483202a7aea.png"},{"id":105259551,"identity":"52f1fd28-be65-48e3-863f-5918dbf243aa","added_by":"auto","created_at":"2026-03-24 05:51:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24328932,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analyses of the association between age (≥55 vs \u0026lt;55 years) and COPD risk in patients with pneumoconiosis (adjusted for other covariates). BMI: Body mass index; WRII: Work-related injury insurance; SRH: Self-Rated Health; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9064711/v1/ff47ebd5c641bddea802abf8.png"},{"id":105259550,"identity":"5a937b62-50b8-48db-8a98-87300b2c9594","added_by":"auto","created_at":"2026-03-24 05:51:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13811324,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of age-related COPD risk by residence and work-related injury insurance(WRII) status in patients with pneumoconiosis (fully adjusted model). Rural residence (a), urban residence (b), without WRII (c) and with WRII (d). OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9064711/v1/fc5293ebdf7a0ea120870d01.png"},{"id":105259553,"identity":"1b870654-619f-4107-a01f-fbc593d6b3b1","added_by":"auto","created_at":"2026-03-24 05:51:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5676256,"visible":true,"origin":"","legend":"\u003cp\u003ePropensity score matching (PSM) validation and age-related COPD risk after PSM in patients with pneumoconiosis (RCS model adjusted for covariates excluding PSM variables). (a) Propensity score density distribution before and after matching; (b) Non-linear association between age and COPD risk after PSM in patients with pneumoconiosis. OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9064711/v1/30f8e92b235512ad67d7686a.png"},{"id":105564169,"identity":"c8232235-9def-4d55-bbd1-a5744c0cf6cd","added_by":"auto","created_at":"2026-03-27 12:48:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":37481897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064711/v1/00ae5619-d9b8-4c55-94e9-820e91779d54.pdf"},{"id":105259548,"identity":"3e4a1f92-d56e-4904-8d29-99bd6d6b4545","added_by":"auto","created_at":"2026-03-24 05:51:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1442688,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064711/v1/2d881c207d6de4820c0e4862.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health inequities in the comorbid risk of pneumoconiosis and chronic obstructive pulmonary disease: a nationwide study of age-related risk heterogeneity","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePneumoconiosis remains a leading occupational respiratory disease globally, contributing substantially to annual morbidity and mortality rates, particularly in developing countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, it has long been the most prevalent occupational illness, pathologically defined as diffuse pulmonary fibrosis caused by occupational dust exposure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients with pneumoconiosis suffer progressive lung function decline and are at high risk of serious complications, notably chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The coexistence of these conditions accelerates clinical deterioration and multiplies the overall disease burden. COPD, characterized by persistent airflow limitation and chronic respiratory symptoms resulting from chronic airway and parenchymal inflammation, represents a major global public health challenge [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, important gaps persist in understanding COPD risk in pneumoconiosis patients, especially regarding how risk factors are heterogeneously distributed across different subpopulations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge is a well-established risk factor for both diseases [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Yet, existing studies are largely limited by the assumption of a homogeneous, linear age effect across the entire population [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This presumption overlooks marked inter-individual differences in socioeconomic (e.g., residence and work-related injury insurance) and clinical profiles, thereby masking the underlying social determinants of risk and impeding the development of targeted public health interventions.\u003c/p\u003e \u003cp\u003eTo address these limitations, we conducted a national cross-sectional study that extends beyond conventional linear analyses. We aimed to precisely characterize the nonlinear dose-response relationship between age and COPD risk among patients with pneumoconiosis, and to systematically quantify the heterogeneity of this relationship across key sociodemographic and clinical subgroups. We hypothesized that the age-related risk is not uniform but is significantly modified by multiple contextual factors. Our findings aim to provide actionable evidence for identifying high-risk subgroups and informing stratified prevention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eData for this study were derived from a nationwide cross-sectional survey conducted from December 2017 to June 2021. The survey covered 27 provinces (excluding Tianjin, Shanghai, Hainan, Tibet, Hong Kong, Macau, and Taiwan), yielding a total of 11,181 completed questionnaires [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Survey items included demographic and sociological characteristics, comorbidities, and clinical features. Data were collected via face-to-face interviews conducted by certified occupational disease physicians and nurses who had undergone standardized training. In some instances, patients completed questionnaires in different regions or hospitals, or at different time points within the same hospital, resulting in the exclusion of 378 duplicate questionnaires. Duplicate questionnaires were identified through logic-based verification using multiple identifiers, including ID number, name, and contact information. Additionally, 180 respondents were under medical observation: these individuals had evidence of pulmonary lesions, but the density of small nodules on chest radiographs did not meet the current criteria for stage I pneumoconiosis, requiring further medical follow-up. Furthermore, 659 additional questionnaires were excluded owing to missing key data that could not be supplemented during follow-up. After excluding these 1,217 questionnaires (378 duplicate responses, 180 participants under medical observation, and 659 with missing key data), a total of 9,964 valid questionnaires were included in the final analysis. Based on an expected COPD prevalence of 25%, with α\u0026thinsp;=\u0026thinsp;0.05, 90% power (β\u0026thinsp;=\u0026thinsp;0.10), and accounting for 10% loss to follow-up, the minimum required sample size was 8,900. This study enrolled 9,964 participants, exceeding this target and ensuring adequate statistical power. This study was approved by the ethics Committee. Written informed consent was obtained from all participants prior to data collection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of this study was COPD. Eligible participants with pneumoconiosis were required to either hold an official occupational disease diagnosis certificate or have received a clinical diagnosis from a physician qualified in pneumoconiosis assessment. In line with previous studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], comorbidity data were collected via questionnaire, specifically by querying participants about a history of relevant complications. COPD was diagnosed based on two complementary criteria: documentation of COPD in medical records and a post-bronchodilator forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) ratio\u0026thinsp;\u0026lt;\u0026thinsp;70% (consistent with the Global Initiative for Chronic Obstructive Lung Disease [GOLD] criteria). Lung function tests were performed by uniformly trained physicians from participating occupational disease prevention and control institutions, using calibrated equipment and strictly adhering to the standardized procedures outlined in the GOLD guidelines to minimize measurement bias.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eDemographic and sociological characteristics included age, sex, body mass index (BMI), residence(urban/rural), birth place, smoking index, alcohol intake, education level, marital status, employment status, annual personal and family income per capita, accumulated dust exposure time, work-related injury insurance, region of China, season of survey, and source of cases. BMI was calculated as weight (kg) divided by the square of height (m\u003csup\u003e2\u003c/sup\u003e). The smoking index was determined by multiplying the number of cigarettes smoked per day by the number of years of smoking [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Smoking index was categorized into four groups: \u0026lt;100, 100\u0026ndash;199, 200\u0026ndash;399, and \u0026ge;\u0026thinsp;400.\u003c/p\u003e \u003cp\u003eComorbidities included COPD, pulmonary tuberculosis, pulmonary bullae or pneumothorax, cardiovascular, diabetes, and hypertension.\u003c/p\u003e \u003cp\u003eClinical characteristics encompassed type of pneumoconiosis, stage of pneumoconiosis, self-rated health, history of using immune-enhancing drugs, and history of hormone therapy for pneumoconiosis. Pneumoconiosis types were classified into three groups: silicosis, coal workers\u0026rsquo; pneumoconiosis (CWP), and other types. Pneumoconiosis staging was divided into four categories: clinical cases, stage I, stage II, and stage III. Clinical cases referred to patients clinically diagnosed with pneumoconiosis who did not possess an official occupational disease certificate, primarily comprising migrant workers. Self-rated health status was evaluated using the European Quality of Life Five-Dimension Scale (EQ-5D) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which employs a 20 cm vertical visual analog scale (VAS) ranging from 0 (worst health) to 100 (best health).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical data were presented as counts and percentages (or proportions) and analyzed using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. To assess the association between age and COPD risk, univariate and multivariate logistic regression models were constructed, with odds ratios (ORs) and 95% confidence intervals (95% CIs) reported. Multicollinearity was evaluated by calculating the variance inflation factor (VIF) and tolerance via a multivariate linear regression model; variables with tolerance\u0026thinsp;\u0026gt;\u0026thinsp;0.1 and VIF\u0026thinsp;\u0026lt;\u0026thinsp;10 were retained [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, fully adjusted restricted cubic spline (RCS) logistic regression analyses with four knots were performed to examine the non-linear and dose-response relationships between age and COPD risk. The knot numbers were selected based on methodological conventions (3\u0026ndash;5 knots as standard for RCS) to balance model parsimony (3 knots) and flexibility (5 knots), while the knot positions followed quantile-based placement to ensure representative coverage of age distribution. Subgroup and interaction analyses were conducted to explore potential effect modification.\u003c/p\u003e \u003cp\u003eIn sensitivity analyses, we repeated the RCS analyses in the fully adjusted model using three and five knots, respectively, to evaluate the stability of identified risk inflection points and observed non-linear trends. We conducted 1:1 propensity score matching (PSM) via nearest-neighbor matching without replacement and a caliper width of 0.2. An acceptable balance between groups was defined as a standardized mean difference (SMD)\u0026thinsp;\u0026lt;\u0026thinsp;0.10. Based on the matched dataset, stratified RCS analyses with four knots were performed to further validate the stability of age-related risk inflection points within these key subgroups. All statistical analyses were performed using R software (version 4.3.3), and a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003eA total of 9,964 participants were included in this study, with an overall COPD prevalence of 24.1%. The mean age was 57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 years for the total population and 56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years for those without COPD. Among COPD cases, mean age varied by stage: 67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 years for stage I, 63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 years for stage II, 56.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years for stage III, and 57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4 years for clinical cases. Significant differences were observed between groups with and without COPD in terms of age, sex, residence (urban/rural), birth place, education level, marital status, body mass index (BMI), smoking index, alcohol intake, region of China, work-related injury insurance (WRII), self-rated health (SRH), comorbid pulmonary bullae or pneumothorax, and type of pneumoconiosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence and risk factors of COPD in patients with pneumoconiosis\u003c/h3\u003e\n\u003cp\u003eThe prevalence of COPD was 19.8% among rural pneumoconiosis patients and 29.7% among urban patients. Univariate analysis indicated that several factors were associated with an increased risk of COPD in these patients, including demographic and socioeconomic characteristics (age, residence, BMI, smoking index, alcohol intake, employment status, annual personal and family income per capita, and WRII), comorbidities (pulmonary bullae or pneumothorax, pulmonary tuberculosis, cardiovascular, diabetes, and hypertension), clinical characteristics (type and stage of pneumoconiosis, self-rated health), as well as history of using immune-enhancing drugs, and history of hormone therapy for pneumoconiosis. Specifically, smoking index\u0026thinsp;\u0026gt;\u0026thinsp;399 (OR\u0026thinsp;=\u0026thinsp;1.54), BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e(OR\u0026thinsp;=\u0026thinsp;2.48), stage III pneumoconiosis (OR\u0026thinsp;=\u0026thinsp;1.52), and the presence of pulmonary bullae or pneumothorax (OR\u0026thinsp;=\u0026thinsp;3.75) were all significantly associated with a higher risk of COPD (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and supplementary table 2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence and ORs of COPD Associated with Risk Factors and Social Determinants among Pneumoconiosis Patients in 27 Provinces of China, Stratified by Urban and Rural Residence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrevalence (%) in patients (95%CI)\u003c/p\u003e \u003cp\u003eLiving in rural areas Living in urban areas Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1 (13.8\u0026ndash;16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.3 (26.5\u0026ndash;30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3 (20.2\u0026ndash;22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026ndash;199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.4 (18.2\u0026ndash;25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 (17.3\u0026ndash;25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3 (18.8\u0026ndash;24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u0026ndash;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.6 (21.0-26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.2 (25.7\u0026ndash;32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.8 (23.7\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.7 (22.7\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.4 (33.5\u0026ndash;39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.4 (27.7\u0026ndash;31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38\u0026ndash;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.7 (31.8\u0026ndash;39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.8 (33.4\u0026ndash;46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.8 (33.4\u0026ndash;40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00-3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.8 (18.5\u0026ndash;21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1 (27.1\u0026ndash;31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3 (22.2\u0026ndash;24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.0\u0026ndash;27.9kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.7 (13.9\u0026ndash;17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.5 (27.4\u0026ndash;31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.2 (21.7\u0026ndash;24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u0026ndash;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28.0kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.1 (8.9\u0026ndash;16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.4 (24.4\u0026ndash;32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5 (18.7\u0026ndash;24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.1 (22.7\u0026ndash;25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.4 (36.5\u0026ndash;40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.5 (28.4\u0026ndash;30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.7 (9.4\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.3 (17.6\u0026ndash;21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.2 (14.1\u0026ndash;16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u0026ndash;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWRII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.4 (16.2\u0026ndash;18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.3 (15.2\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4 (16.3\u0026ndash;18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.0 (23.1\u0026ndash;27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.4 (32.8\u0026ndash;36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.0 (29.7\u0026ndash;32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.94\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of pneumoconiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1 (5.6\u0026ndash;11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6 (22.0-29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.6 (16.1\u0026ndash;21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilicosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.6 (17.3\u0026ndash;20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.1 (29.9\u0026ndash;34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.2 (22.0-24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09\u0026ndash;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5 (21.7\u0026ndash;25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.9 (27.0-30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.2 (24.9\u0026ndash;27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29\u0026ndash;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStages of pneumoconiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.5 (10.1\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.4 (26.7\u0026ndash;30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7 (20.5\u0026ndash;22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.7 (17.7\u0026ndash;21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.6 (31.7\u0026ndash;37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5 (23.8\u0026ndash;27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.8 (26.7\u0026ndash;31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.5 (28.4\u0026ndash;37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.6 (27.7\u0026ndash;31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35\u0026ndash;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical cases (no classified stage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.1 (17.2\u0026ndash;23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.5 (14.1\u0026ndash;24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.7 (17.2\u0026ndash;22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary bullae or pneumothorax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.1 (15.1\u0026ndash;17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.6 (26.2\u0026ndash;29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3 (20.5\u0026ndash;22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.5 (42.8\u0026ndash;50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.6 (53.9\u0026ndash;65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.4 (47.2\u0026ndash;53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.27\u0026ndash;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are expressed as number(%); COPD: Chronic obstructive pulmonary disease; OR: Odds ratio; CI: Confidence interval; BMI: Body mass index; WRII: Work-related injury insurance; SRH: Self-Rated Health; CWP: Coal workers\u0026rsquo; pneumoconiosis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariate analysis of risk factors for COPD in patients with pneumoconiosis\u003c/h3\u003e\n\u003cp\u003ePrior to regression modeling, diagnostic checks confirmed that all variables had a tolerance\u0026thinsp;\u0026gt;\u0026thinsp;0.1 and a variance inflation factor (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;10, indicating no concern for multicollinearity(supplementary table 3). In the model including all enrolled patients, significant risk factors for COPD development among individuals with pneumoconiosis were identified as: increasing age, smoking index\u0026thinsp;\u0026ge;\u0026thinsp;200, BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, residence in western or northeastern China, with WRII, comorbid cardiovascular diseases, coal workers\u0026rsquo; pneumoconiosis (CWP), advanced pneumoconiosis stage, cumulative dust exposure of 20\u0026ndash;24 years, history of immune-enhancing drug use, history of hormone therapy for pneumoconiosis, and comorbid pulmonary bullae or pneumothorax (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the association between WRII and increased COPD risk may reflect detection bias, as insured individuals typically undergo more systematic health monitoring, leading to higher diagnostic identification of COPD. Stratified analyses by urban/rural residence showed that the markedly elevated COPD risk observed in the northeastern region was primarily confined to urban patients. The risk associated with CWP was higher among urban patients than rural counterparts. Furthermore, case source, survey season, history of immune-enhancing drug use, and history of hormone therapy for pneumoconiosis were also significantly associated with COPD risk, with most of these associations differing between urban and rural populations (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and supplementary table 4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable-adjusted odds ratios (ORs) for COPD-related risk factors among pneumoconiosis patients in 27 provinces of China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAll pneumoconiosis cases\u003c/p\u003e \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLiving in rural areas\u003c/p\u003e \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLiving in urban areas\u003c/p\u003e \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (1.03\u0026ndash;1.04) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e100\u0026ndash;199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.88\u0026ndash;1.32) 0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.27 (0.97\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.63\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e200\u0026ndash;399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60 (1.37\u0026ndash;1.89) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.61 (1.30-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.67 (1.29\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77 (1.54\u0026ndash;2.03) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.63 (1.35\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.88 (1.52\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon-drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCurrent drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.67\u0026ndash;0.92) 0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.64 (0.51\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.78\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFormer drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.90\u0026ndash;1.18) 0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.26 (1.06\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94 (0.75\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.14\u0026ndash;1.70) 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.48 (1.17\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 (0.86\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e24.0\u0026ndash;27.9kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.79\u0026ndash;1.02) 0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.86 (0.71\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90 (0.75\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28.0kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.67\u0026ndash;1.04) 0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.62 (0.42\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.69\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRegion of China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.66\u0026ndash;0.94) 0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.97 (1.51\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28 (0.20\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20 (1.88\u0026ndash;2.58) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.15 (2.50\u0026ndash;3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29 (0.99\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNortheastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.81 (7.20-10.79) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.88 (1.09\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.70 (7.45\u0026ndash;12.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWith WRII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.19\u0026ndash;1.56) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.43 (1.20\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35 (1.07\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSRH\u0026gt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.57\u0026ndash;0.74) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.61 (0.51\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.63\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eType of pneumoconiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSilicosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (0.98\u0026ndash;1.58) 0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.53 (0.96\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27 (0.94\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.71\u0026ndash;2.77) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.07 (1.31\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.34 (2.43\u0026ndash;4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStages of pneumoconiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.19\u0026ndash;1.58) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.45 (1.16\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53 (1.24\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 (1.43\u0026ndash;1.98) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.98 (1.59\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.40 (1.04\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClinical cases (no classified stage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (1.26-2.00) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.65 (1.24\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49 (0.94\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePulmonary bullae or pneumothorax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80 (2.36\u0026ndash;3.33) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.56 (2.08\u0026ndash;3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.49 (2.50\u0026ndash;4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are expressed as number(%); COPD: Chronic obstructive pulmonary disease; OR: Odds ratio; CI: Confidence interval; BMI: Body mass index; WRII: Work-related injury insurance; SRH: Self-Rated Health; CWP: Coal workers\u0026rsquo; pneumoconiosis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe nonlinear association between age and the risk of COPD\u003c/h2\u003e \u003cp\u003eThe fully adjusted restricted cubic spline (RCS) regression model revealed a significant non-linear positive association between age and the risk of comorbid COPD among patients with pneumoconiosis (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinear\u0026thinsp;=\u0026thinsp;0.017). With 55 years as the reference point (OR\u0026thinsp;=\u0026thinsp;1), the risk of COPD increased continuously with advancing age, showing a pronounced accelerating trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eTo further investigate the relationship between age and the risk of COPD, we divided patients into two subgroups using 55 years as the cutoff. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and supplementary Fig.\u0026nbsp;1, among all patients, age\u0026thinsp;\u0026ge;\u0026thinsp;55 years was identified as a significant risk factor for COPD comorbidity in individuals with pneumoconiosis (OR\u0026thinsp;=\u0026thinsp;1.32, 95%CI: 1.16\u0026ndash;1.50). Interaction analysis indicated that residence, BMI, region of China, WRII, SRH, pneumoconiosis stage, and type of pneumoconiosis significantly modified the association between age and COPD risk (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStratified RCS Analysis of the Association Between Age and COPD\u003c/h2\u003e \u003cp\u003eTo investigate heterogeneity in the association between age and COPD, we conducted stratified RCS analyses based on factors that showed significant interactions in subgroup analyses. The results revealed substantial differences in the age-related effect across patient characteristics, primarily manifesting as either a risk amplification or a risk moderation pattern.\u003c/p\u003e \u003cp\u003eStratified analyses revealed pronounced heterogeneity in age-related risk patterns across subgroups. A steeper increase in risk beyond a specific age threshold was observed in multiple subgroups, including rural residents, individuals without WRII, those with Stage I pneumoconiosis, and eastern residents (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec; supplementary Figs.\u0026nbsp;2a, 3c, 4d, 5a, 6a). In contrast, a more gradual rise in risk was identified in subgroups such as urban residents, individuals with WRII, and those with Stage II pneumoconiosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed; supplementary Figs.\u0026nbsp;2b, 3b). This marked divergence highlights substantial heterogeneity in how age modifies COPD risk among different patient profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe age at which COPD risk began to increase (OR\u0026thinsp;\u0026gt;\u0026thinsp;1) varied substantially across subgroups, defining a disparity of nearly a decade. The earliest thresholds (52\u0026ndash;53 years) were observed among rural residents, individuals without WRII, and western residents. While the latest thresholds (59\u0026ndash;61 years) were seen in urban residents, individuals with WRII, and eastern or northeastern residents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eTo validate the robustness of our primary findings, we conducted comprehensive sensitivity analyses that consistently supported the initial conclusions.\u003c/p\u003e \u003cp\u003eFirst, to evaluate potential model dependency in the nonlinear association between age and COPD risk, we performed restricted cubic spline (RCS) analyses using both three-knot and five-knot specifications. Both configurations demonstrated a significant nonlinear positive association (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; supplementary Fig.\u0026nbsp;7), with the risk inflection point consistently observed at 55 years without notable variation.\u003c/p\u003e \u003cp\u003eSecond, we established a balanced cohort through 1:1 nearest-neighbor propensity score matching, generating 2,267 matched pairs of COPD and non-COPD patients with pneumoconiosis. All covariates exhibited standardized mean differences below 0.1 (supplementary table 5). RCS analysis within this matched cohort revealed a COPD risk inflection point at 54 years, further corroborating the robustness of our primary results.\u003c/p\u003e \u003cp\u003eFinally, stratified RCS analyses based on residence and WRII in the matched dataset revealed pronounced disparities: rural residents showed an inflection point at 54 years compared to 64 years for urban residents-a 10-year gap. Similarly, individuals without WRII exhibited an inflection point at 54 years versus 61 years among those with WRII, representing a 7-year difference (supplementary Fig.\u0026nbsp;8). These stratified findings closely aligned with trends observed in the overall sample, confirming that the heterogeneity in age-related COPD risk trajectories across subgroups defined by social determinants persists after accounting for potential baseline confounding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide study of 9,964 patients with pneumoconiosis, we delineate for the first time a nonlinear relationship between age and COPD risk that is substantially modified by sociodemographic factors, notably residence and work-related injury insurance status. These findings refine the understanding of COPD development in this high-risk occupational population and provide critical evidence for risk stratification, which is of significant value for guiding public health planning, targeted population monitoring, and early intervention strategies in the public health domain.\u003c/p\u003e \u003cp\u003eAlthough pneumoconiosis and COPD have traditionally been regarded as distinct clinical entities, accumulating evidence highlights considerable pathophysiological overlap. Both conditions involve chronic pulmonary inflammation, protease-antiprotease imbalance, and oxidative stress, which are core processes that drive progressive and irreversible airflow limitation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A meta-analysis reported a detection rate of 26.4% for comorbid COPD in patients with pneumoconiosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our study, the prevalence of COPD among pneumoconiosis patients was 24.1%, which is consistent with this pooled estimate and significantly higher than the 13.6% reported in the general Chinese population aged 40 years and above [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This finding supports the notion that long-term occupational dust exposure represents an independent and potent risk factor for COPD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We further identified a graded association between pneumoconiosis severity and COPD risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], reinforcing the concept that dust-induced structural lung damage and sustained inflammatory responses synergistically promote the development and progression of COPD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study confirms that age is an independent risk factor for COPD in patients with pneumoconiosis, consistent with findings from most epidemiological studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. With advancing age, physiological changes including reduced pulmonary elasticity and impaired reparative capacity, combined with the inherent pulmonary fibrosis and inflammatory damage of pneumoconiosis, exert a synergistic effect that accelerates the development and progression of COPD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Notably, restricted cubic spline (RCS) analysis identified a non-linear relationship wherein the risk of COPD among patients with pneumoconiosis exhibits an inflection point and rises sharply at 55 years of age. This core finding was further validated through sensitivity analyses: altering the number of knots in the RCS model did not compromise the significance of the non-linear relationship, and the risk inflection point remained consistent at 55 years. This inflection point may represent a critical threshold at which pulmonary compensatory mechanisms become exhausted, leading to a marked increase in COPD risk. Notably, this 55-year inflection point in Chinese pneumoconiosis patients is substantially earlier than the age at which COPD incidence begins to show exponential growth in the general population of high sociodemographic index (SDI) countries (around 60 years) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This discrepancy may be attributed to the synergistic effects of long-term occupational dust exposure and age-related physiological decline.\u003c/p\u003e \u003cp\u003eCritically, we found that the effect of age is not uniform but is substantially modified by sociodemographic and clinical characteristics. A striking eight-year disparity in risk-onset age was observed between rural and urban residents, with inflection points at 53 and 61 years, respectively. In the sensitivity analysis, after controlling for multiple baseline confounders through propensity score matching (PSM), we not only replicated a similar risk inflection point at 54 years of age within the matched cohort, but also confirmed that the COPD risk inflection point for rural patients occurred 10 years earlier than that for urban patients. This finding suggests that rural patients experience not only occupational lung damage but also cumulative health disadvantages stemming from limited healthcare access and lower health literacy [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A more complex pattern emerged regarding work-related injury insurance (WRII) status. Although insured status was associated with a higher COPD prevalence (31.0% vs 17.4%) and emerged as an independent risk factor (OR\u0026thinsp;=\u0026thinsp;1.37) in multivariable analysis, this likely reflects detection bias due to more systematic health surveillance. Stratified RCS analysis further revealed that insured patients experienced a six-year delay in the age of steep risk escalation (59 years) compared to uninsured individuals (53 years). Notably, in the sensitivity analysis, the inflection point for COPD risk occurred seven years earlier among patients without WRII compared to those with WRII. This indicates that the stable employment and protective working conditions linked to WRII confer a delay in disease manifestation, though they cannot ultimately offset age-related physiological decline [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study demonstrates a significant nonlinear association between age and COPD risk among patients with pneumoconiosis, which is substantially modified by sociodemographic factors. The primary risk inflection point occurred at 55 years of age. Crucially, residence and WRII status emerged as key modifiers of this risk pattern, underscoring the role of health inequities. Further variation in the inflection point was observed according to pneumoconiosis stage, self-rated health, and geographic region, as detailed in the Supplementary Material. Collectively, patients aged 55 years or older, particularly those who live in rural areas, without WRII, or clinical cases, constitute the highest-risk subgroup. This group should be prioritized for intensified pulmonary function monitoring and early intervention. Addressing these sociodemographically driven disparities is essential to mitigate the disproportionate burden of COPD in this occupational population.\u003c/p\u003e \u003cp\u003eWe also acknowledge important limitations. The cross-sectional design precludes causal inference, and some variables (such as self-rated health and accumulated dust exposure time) are prone to potential recall bias due to their self-reported nature. Importantly, while dust exposure duration was captured via questionnaire, data on exposure intensity or cumulative exposure dose were unavailable, which may limit the precision of exposure-response analyses. Our identification of an earlier COPD risk inflection point in socioeconomically disadvantaged subgroups aligns with global observations that social determinants exacerbate occupational lung disease burden in low- and middle-income countries [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Future prospective cohort studies with objective measurements including dust exposure intensity and longitudinal lung function monitoring in multinational populations are necessary to validate these associations and clarify potential causal mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors report no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThis study was approved by the Ethics Committee of the National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention (Approval No. 201720). Written informed consent was obtained from all participants prior to data collection.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNational Major Science and Technology Project of China (No. 2024ZD0528901); National Natural Science Foundation of China (No. 82574060).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: H W, K L, Y R. Analysis and interpretation of data: K L, L Z, X L. Drafting the article or revising it critically for important intellectual content: K L, H W. Final approval of the version to be submitted for revision: H W, K L, Y R. All authors had full access to all the study data and accepted responsibility for submitting this work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their gratitude to all study participants, the survey teams across the 27 provinces, and the project development and management teams for their invaluable contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original individual participant data cannot be shared publicly due to containing sensitive personal information. De-identified summary data are available from the corresponding author upon reasonable request and with ethics committee approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCullinan P, Mu\u0026ntilde;oz X, Suojalehto H, et al. Occupational lung diseases: from old and novel exposures to effective preventive strategies. Lancet Respir Med. 2017;5(5):445\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Liu W, Yao Y, et al. 30-Year Trends in the Disease Burden, Incidence, and Prevention of Pneumoconiosis. China CDC Wkly. 2023;5(38):856\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Yin P, Wang H, et al. The burden of pneumoconiosis in China: an analysis from the Global Burden of Disease Study 2019. BMC Public Health. 2022;22(1):1114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Lyu X, Luo D, et al. Prevalence and Types of Comorbidities in Pneumoconiosis - China, 2018\u0026ndash;2021. China CDC Wkly. 2023;5(38):837\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Xu J, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet. 2018;391(10131):1706\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Y, Li X, Cai S, et al. Prevalence and characteristics of COPD among pneumoconiosis patients at an occupational disease prevention institute: a cross-sectional study. BMC Pulm Med. 2018;18(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Y, Xu W, Wang Y, et al. Association of occupational dust exposure with combined chronic obstructive pulmonary disease and pneumoconiosis: a cross-sectional study in China. BMJ Open. 2020;10(9):e038874.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Lin J, Liang L, et al. Global, regional, and national burden of chronic obstructive pulmonary disease and its attributable risk factors from 1990 to 2021: an analysis for the Global Burden of Disease Study 2021. Respir Res. 2025;26(1):2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Yang Y, Wang F, et al. Clinical and Prognostic Differences in Mild to Moderate COPD With and Without Emphysema. Chest. 2025;167(3):724\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY\u0026uuml;ksel Yavuz M, Coşkun Beyan A, Ayik T\u0026uuml;rk M, et al. Survival analysis of patients with pneumoconiosis followed in occupational medicine clinics: 10 years experience. BMC Pulm Med. 2025;25(1):236.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Han R, Ding X, et al. Chronic obstructive pulmonary disease across three decades: trends, inequalities, and projections from the Global Burden of Disease Study 2021. Front Med (Lausanne). 2025;12:1564878.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFragoso CA. Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) in Aging Populations. COPD. 2016;13(2):125\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Dai H, He J, et al. Epidemiological characteristics of pulmonary tuberculosis in patients with pneumoconiosis based on its social determinants and risk factors in China: a cross-sectional study from 27 provinces. Chin Med J (Engl). 2022;135(24):2984\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSulsky SI, Fuller WG, Van Landingham C, et al. Evaluating the association between menthol cigarette use and the likelihood of being a former versus current smoker. Regul Toxicol Pharmacol. 2014;70(1):231\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMade AD, Peters RW, Verheul C, et al. Proximal hamstring tendon avulsions: comparable clinical outcomes of operative and non-operative treatment at 1-year follow-up using a shared decision-making model. Br J Sports Med. 2022;56(6):340\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuo L, Xu L, Ye J, et al. Time Trade-Off Value Set for EQ-5D-3L Based on a Nationally Representative Chinese Population Survey. Value Health. 2018;21(11):1330\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui Y, Zhang J, Wang Y, et al. Multivariate predictive model of the therapeutic effects of metoprolol in paediatric vasovagal syncope: a multi-centre study. EBioMedicine. 2025;113:105595.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurt OK, Ergun D, Anlar HG, et al. Evaluation of Oxidative Stress Parameters and Genotoxic Effects in Patients With Work-Related Asthma and Silicosis. J Occup Environ Med. 2023;65(2):146\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman I, Adcock IM. Oxidative stress and redox regulation of lung inflammation in COPD. Eur Respir J. 2006;28(1):219\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng L, Wang Z. Meta-analysis of the prevalence of common complications among pneumoconiosis patients. J Chin Med Libr Assoc. 2024;33:45\u0026ndash;52. (In Chinese with English abstract).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang L, Gao P, Bao H, et al. Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir Med. 2018;6(6):421\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlif SM, Dharmage SC, Bowatte G, et al. Occupational exposure and risk of chronic obstructive pulmonary disease: a systematic review and meta-analysis. Expert Rev Respir Med. 2016;10(8):861\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisner MD, Anthonisen N, Coultas D, et al. An official American Thoracic Society public policy statement: Novel risk factors and the global burden of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2010;182(5):693\u0026ndash;718.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing F, Liu W, Hu X, et al. Factors related to the progression of chronic obstructive pulmonary disease: a retrospective case-control study. BMC Pulm Med. 2025;25(1):5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi XM, Luo Y, Song MY, et al. Pneumoconiosis: current status and future prospects. Chin Med J (Engl). 2021;134(8):898\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorrance BL, Haynes L. Cellular senescence is a key mediator of lung aging and susceptibility to infection. Front Immunol. 2022;13:1006710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoller B, Myers O, Sood A. Transfer of Knowledge on Pneumoconiosis Care Among Rural-Based Members of a Digital Community of Practice: Cross-Sectional Study. JMIR Form Res. 2024;8:e52414.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThygesen LC, Hvidtfeldt UA, Mikkelsen S, et al. Quantification of the healthy worker effect: a nationwide cohort study among electricians in Denmark. BMC Public Health. 2011;11:571.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalvi SS, Barnes PJ. Chronic obstructive pulmonary disease in non-smokers. Lancet. 2009;374(9691):733\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeloye D, Song P, Zhu Y, et al. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med. 2022;10(5):447\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Occupational Health, Pneumoconiosis, COPD","lastPublishedDoi":"10.21203/rs.3.rs-9064711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9064711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePneumoconiosis patients are at heightened risk of developing chronic obstructive pulmonary disease (COPD). While age is a known risk factor, its impact across different populations is unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe included 9,964 pneumoconiosis patients from 27 Chinese provinces. Factors were identified using multivariable logistic regression, and the non-linear age\u0026ndash;COPD risk association was examined with restricted cubic spline (RCS) modelling. Stratified analysis and interaction tests assessed sociodemographic heterogeneity. Sensitivity analyses comprised RCS models with varying knots and propensity score matching.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of COPD among patients with pneumoconiosis was 24.1%. Each additional year of age was associated with a significant increase in COPD risk (adjusted odds ratio [aOR]\u0026thinsp;=\u0026thinsp;1.03, 95% confidence interval [CI]: 1.03\u0026ndash;1.04). RCS analysis revealed a significant nonlinear relationship, with risk increasing steeply beyond age 55 years (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinear\u0026thinsp;=\u0026thinsp;0.017). we identified subgroups with distinct risk patterns. Rural residents and those without work‑related injury insurance (WRII) demonstrated an earlier inflection point (53 years) followed by a steep increase in risk. In contrast, urban residents and individuals with WRII exhibited a later inflection point (at 61 and 59 years, respectively) and a more gradual rise in risk. The inflection point differed by 8 years between urban and rural residents and by 6 years between individuals with and without WRII. Sensitivity analyses verified the robustness of these findings.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nonlinear relationship between age and COPD risk in pneumoconiosis is significantly modified by sociodemographic factors.\u003c/p\u003e","manuscriptTitle":"Health inequities in the comorbid risk of pneumoconiosis and chronic obstructive pulmonary disease: a nationwide study of age-related risk heterogeneity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 05:51:24","doi":"10.21203/rs.3.rs-9064711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T11:54:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T11:30:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T03:53:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100127402956507195949895662751608690705","date":"2026-04-05T19:42:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T19:24:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200337795513514911022684785059024427089","date":"2026-04-04T14:59:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248047086465094261690065961127696764103","date":"2026-03-30T03:46:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T13:58:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-13T17:56:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T01:39:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2026-03-08T13:52:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"191040a2-0a36-475d-b9e1-8f8eaf2d9eb6","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T12:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 05:51:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9064711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9064711","identity":"rs-9064711","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}