Introduction
Tracheal, bronchus, and lung cancer (TBLC) remains a critical global public health challenge, ranking first in both incidence and mortality among all cancers. 1 According to the 2022 Global Cancer Statistics (GLOBOCAN), approximately 2.48 million new cases and 1.82 million deaths were reported worldwide, accounting for 12.4% of all new cancer cases and 18.7% of cancer-related deaths. 2 The burden of TBLC is particularly pronounced in low- and middle-income countries (LMICs), where low rates of early diagnosis, limited treatment availability, and inadequate healthcare infrastructure significantly impact outcomes. 3 Additionally, regional disparities in epidemiological patterns and risk factors further contribute to global health inequities. 4 Addressing these challenges requires a comprehensive understanding of the disease’s epidemiological characteristics and cross-national differences in outcomes and health-system capacity.
Quality of care (QoC) refers to the extent to which timely, effective, and patient-centered healthcare services are delivered to achieve optimal health outcomes. 5 In recent years, significant advancements have been made in QoC research across various disease areas. For example, studies on hematologic malignancies using Global Burden of Disease (GBD) data highlighted substantial disparities in QoC across regions and socioeconomic contexts. 6 , 7 Similarly, in ischemic heart disease (IHD) and infective endocarditis (IE), metrics such as the mortality-to-incidence ratio (MIR) and QoC index (QCI) provided critical insights for comparative assessment. 8 , 9 However, research on such comparative proxy indicators in TBLC remains underdeveloped. Existing studies are largely confined to specific regions and lack a comprehensive global assessment, which obscures the extent of global inequities in QoC for TBLC. 10 Furthermore, the absence of standardized metrics for evaluating the QoC for TBLC limits cross-regional and cross-population comparisons. 11 These gaps significantly hinder the ability of policymakers and clinicians to fully understand and improve TBLC-related outcomes across settings. The GBD study, a leading authority in global health, provides comprehensive, systematic, and comparable epidemiological data, including disease incidence, mortality, risk factors, and health outcomes. 12 , 13 , 14 Leveraging its extensive coverage and standardized methodology, GBD data offer a robust framework for analyzing QCI for TBLC, particularly in resource-limited settings, thereby addressing critical gaps in current research. This study aims to construct a QCI by using GBD data to evaluate trends and disparities in this composite proxy indicator for TBLC across 204 countries from 1990 to 2021. By examining factors such as age, sex, and regional economic context, we seek to identify inequities and inform evidence-based interventions, ultimately advancing global health equity and improving patient outcomes.
Results
Global TBLC burden remained high in 2021 despite declines in age-standardized incidence, mortality, and DALYs
Global TBLC burden declined in age-standardized terms but remained substantial in 2021. Global TBLC burden declined in age-standardized terms but remained substantial in 2021. In 2021, there were an estimated 2,280,688.22 incident cases (95% UI, 2,063,251.87–2,509,739.73), 2,016,547.44 deaths (1,820,497.67–2,218,371.89), and 46,536,272.12 DALYs (41,903,412.29–51,205,051.41) globally. The age-standardized incidence rate (ASIR) for TBLC globally was 26.43 (23.9, 29.07) in 2021, with an estimated annual percentage change (EAPC) of −0.24 (−0.31, −0.17) compared with the values in 1990. The age-standardized death rate (ASDR) was 23.5 (21.22, 25.85), with an EAPC of −0.54 (−0.6, −0.48), and the age-standardized DALYs rate was 533 (480.13, 586.36), with an EAPC of −0.87 (−0.93, −0.81) ( Table S1 ). Globally, the region with the highest ASIR and ASDR, with values being 43.41 (35.14, 52.35) and 38.53 (31.22, 46.22), respectively, was East Asia. At the national level, Monaco had the highest ASIR and ASDR, at 76.86 (62.11, 94.88) and 64.05 (52.15, 78.6), respectively. The region with the highest age-standardized DALYs rate was Central Europe, at 896.35 (830.52, 955.3), while the nation with the highest rate was Greenland, at 1,525.23 (1,236.45, 1,849.94) ( Tables S1 and S2 ). In 2021, the ASIR and ASDR for females globally were 16.8 (14.9, 18.6) and 14.51 (12.83, 16.1), respectively, both values lower than those for males. The EAPCs for females were 0.51 (0.42, 0.6) and 0.17 (0.09, 0.25). Globally, the highest number of cases and deaths occurred in the 70–74 year age group, whereas the highest incidence and mortality rates were observed in the 90–94 year age group. ( Table S1 ; Figure 1 A). In 2021, the high-middle socio-demographic index (SDI) region had the highest ASIR, ASDR, and age-standardized DALYs rate, at 36.41 (31.79, 41.67), 32.38 (28.46, 36.78), and 762.67 (667.05, 870.01), respectively. From 1990 to 2021, the high SDI region exhibited the lowest EAPCs for ASIR, ASDR, and age-standardized DALYs rate, at −0.72 (−0.86, −0.58), −1.14 (−1.24, −1.04), and −1.52 (−1.62, −1.41), respectively ( Figure 1 B). Figure 1 Global TBLC burden remained high in 2021 despite declines in age-standardized incidence, mortality, and DALY rates (A) Global incidence, deaths, and DALYs of TBLC by age group and sex in 2021. (B) Trends in the ASIR, ASDR, and age-standardized DALY rate of TBLC globally and across five SDI regions from 1990 to 2021. ASIR, age-standardized incidence rate; ASDR, age-standardized death rate; DALYs, disability-adjusted life-years; SDI, socio-demographic index; TBLC, tracheal, bronchus, and lung cancer. Error bars for numbers and shaded bands for rates indicate 95% uncertainty intervals.
TBLC QCI improved unevenly across countries, with Japan ranking highest and the Republic of Korea showing the fastest gains
Country-level QCI improved substantially but remained highly unequal worldwide. We conducted principal-component analysis (PCA) on the four secondary indicators (years of life lost [YLLs] to years lived with disability [YLDs] ratio [YLR], DALYs to prevalence ratio [DPR], mortality to incidence ratio [MIR], and prevalence to incidence ratio [PIR]) and constructed the principal-component equations based on PCA weights: PC1 = −0.49 YLR + −0.51 DPR + −0.5 MIR +0.5 PIR; PC2 = 0.77 YLR +0.12 DPR + −0.5 MIR +0.37 PIR。Here, PC1 explained 94.52% of the variance, while PC2 accounted for only 3.73%. Correlation analysis revealed that PIR was negatively correlated with the other three secondary indicators (YLR, DPR, and MIR) ( p < 0.01). The Spearman correlation coefficient with the healthcare access and quality index (HAQI) was 0.89 ( p < 0.01). Significant differences in the clustering characteristics of QCI across countries at different SDI levels were observed: the Euclidean distance between groups in Low-SDI countries was 0.47 (0.46, 0.48), whereas in high-SDI countries, it was 2.037 (1.92, 2.16), indicating greater heterogeneity among high-SDI countries in principal components ( Figures S1 A–S1C). In 2021, among the 204 countries globally, the highest QCI for TBLC was observed in Japan (99.3), Singapore (91.4), and the Republic of Korea (89.9), while the lowest QCI was found in Lesotho (8.09), Kiribati (8.86), and the Marshall Islands (9.63) ( Figure 2 A; Table S3 ). From 1990 to 2021, the countries with the highest EAPC in QCI were the Republic of Korea, 4.66 (4.11, 5.20); China, 4.13 (3.99, 4.27); and France, 3.62 (3.35, 3.89). Conversely, the countries with the lowest EAPC were Egypt, −0.39 (−0.54, −0.23); Eswatini, −0.50 (−1.10, 0.10); and Lesotho, −1.29 (−1.70, −0.88) ( Figure 2 B ; Table S5 ). The QCI ranks for 1990, 2000, 2010, and 2021 showed that Japan consistently ranked first, with QCI values of 69.4, 79.97, 98.2, and 99.32, respectively. The United States dropped from 2nd place (58.36) in 1990 to 11th place (75.33) in 2021, while the Republic of Korea significantly improved from 39th place (24.61) in 1990 to 3rd place (89.92) in 2021 ( Figure 2 C). Figure 2 TBLC QCI improved unevenly across countries from 1990 to 2021 (A) Geographic distribution of QCI for TBLC across 204 countries in 2021. (B) Geographic distribution of the EAPC in QCI for TBLC across 204 countries from 1990 to 2021. (C) Rankings of the top 10 countries by QCI for TBLC in 1990, 2000, 2010, and 2021. QCI, quality-of-care index; EAPC, estimated annual percentage change; TBLC, tracheal, bronchus, and lung cancer.
QCI was higher in females and high-SDI settings but declined steeply with age
Female sex and high SDI were associated with higher QCI, whereas older age was associated with lower QCI. From 1990 to 2021, significant statistical differences in QCI values were observed by gender ( p < 0.01). In 1990, the average QCI for females across 204 countries globally was 19.8 (18.23, 21.37), higher than that for males at 17.67 (16.26, 19.08). By 2021, the QCI for females increased to 29.82 (26.84, 32.81), still significantly higher than that for males at 26.8 (24.11, 29.48) ( Figure 3 A). Differences in QCI were also observed across SDI groups. In 1990, the QCI for low SDI regions was 12.21 (11.8, 12.61), lower than that for high SDI regions at 44.11 (39.12, 49.1). By 2021, the average QCI for low SDI regions increased to 14.05 (13.33, 14.78), while high SDI regions showed a significant increase to 68.61 (62.23, 74.99) ( Figure 3 B ) . From 1990 to 2021, QCI values in high and high-middle SDI regions were consistently higher than those in low and low-middle SDI regions ( Figure 3 C ) . The 25–29 year age group consistently showed the highest QCI values (94.5 in 1990, 99.9 in 2021), while the 95+ group had the lowest (1.39 in 1990, 3.90 in 2021). In 2021, females aged 40–94 years had higher QCI values than males, whereas females aged 15–24 years had lower values, indicating a potential sex-age interaction in QCI ( Figure 3 D). Spearman correlation analysis showed a strong negative association between QCI and age (coefficient < −0.97, p < 0.01) from 1990 to 2021. The 75–79 year age group exhibited the largest QCI increase (9.13) during this period ( Figure 3 E). Figure 3 QCI was higher in females and high-SDI settings but declined markedly with age (A) Trends in QCI for TBLC in females and males across 204 countries from 1990 to 2021. (B) Trends in QCI for TBLC across SDI regions from 1990 to 2021 by sex. (C) QCI for TBLC across SDI subgroups in 1990 and 2021. (D) QCI for TBLC by age group globally in 1990 and 2021, shown for both sexes combined, females, and males. (E) Trends in QCI for TBLC by age group globally from 1990 to 2021. In (A) and (C), boxplots show medians, interquartile ranges, 1.5× interquartile-range whiskers, and outliers. In (B), lines represent mean QCI values, and shaded bands indicate SEM. Data in (B) are represented as mean ± SEM. In (A), female and male QCI values were compared across 204 countries for each year using two-sided Wilcoxon rank-sum tests. In (C), QCI values across SDI subgroups were compared using pairwise two-sided Wilcoxon rank-sum tests. ns, p ≥ 0.05; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001. QCI, quality-of-care index; SDI, socio-demographic index; TBLC, tracheal, bronchus, and lung cancer; SEM, standard error of the mean.
Higher sociodemographic development predicted higher QCI, whereas older age predicted lower QCI
Linear mixed modeling identified SDI as a positive predictor and older age as a negative predictor of QCI. QCI, derived from PCA of the four secondary indicators and subsequently rescaled to a 0–100 range by linear transformation was used as the dependent variable in the linear mixed model (LMM). The model included age group, sex, and SDI as fixed effects, with year group as a random effect ( Table 1 ). The model fit indices were AIC = 206,759.8, BIC = 206,975.4, and ICC = 0.001, indicating minimal between-group variance attributable to year group. Sex had a negative effect on QCI (β = −3.53e−02, p < 1.10e−94), indicating slightly lower QCI values in males compared with females ( Figure S2 A). SDI showed a positive association with QCI (β = 1.92, p < 0.001), suggesting that higher SDI levels correlate with higher QCI values ( Figure S2 B). The influence of age on QCI was non-linear, with significant differences across age groups. The 30–34 year age group exhibited the strongest positive effect (β = 1.14, p < 0.01), while older age groups (e.g., 70–74 years, β = −0.54, p < 0.001; 95+ years, β = −2.03, p < 0.001) showed negative effects ( Figure S2 C). Random effects analysis revealed minimal influence of year group on QCI, with effect sizes of −1.18e−02, 1.28e−02, and -9.65e−04 for 1990–2000, 2001–2010, and 2011–2021, respectively. Table 1 Regression coefficients, standard errors, and statistical inference values of each variable in the linear mixed model
Table 1
Regression coefficients, standard errors, and statistical inference values of each variable in the linear mixed model
Older adults had lower TBLC QCI than the overall population in nearly all countries
Aging-related QCI (AQCI) indicated a less favourable age-relative QCI pattern in older adults across almost all countries. Based on the results of the LMM analysis, we found that age ≥65 years was associated with lower QCI, whereas SDI was positively associated with QCI (β = 1.92). To further characterize age-related differences in QCI, we constructed the AQCI to evaluate the heterogeneity and geospatial distribution of QCI among elderly populations across distinct regions. In 2021, AQCI values were lower than 1 in 201 out of 204 (98.53%) countries, indicating lower QCI for populations aged 65 years and older than in the overall analyzed population. ( Figure 4 A). Analysis of AQCI change ratios (1990–2021) revealed that the low SDI group had a ratio of 0.22 (0.15, 0.28), significantly lower than the high-middle SDI group 0.96 (−0.25, 2.18), p < 0.01. The high SDI group showed a ratio of −0.93 (−2.96, 1.1), with no statistical difference from the low SDI group ( p = 0.66) but higher variability (SD: 1.04 vs. 0.03), indicating greater heterogeneity in AQCI changes. Additionally, countries showing the highest AQCI change ratios included New Zealand (6.42), Czechia (5.49), and Croatia (5.08), while the lowest were Republic of Korea (−12.75), Japan (−10.32), and France (−9.31), all six countries from high and high-middle SDI groups ( Figure 4 B). Figure 4 Older adults had lower TBLC QCI than the overall population in nearly all countries (A) Distribution of AQCI for TBLC across 204 countries in 1990 and 2021. (B) Changes in AQCI for TBLC across 204 countries from 1990 to 2021, stratified by SDI subgroup. Boxplots show medians, interquartile ranges, 1.5× interquartile-range whiskers, and outliers. Brackets indicate pairwise comparisons between SDI subgroups, with values showing p values. AQCI, aging quality-of-care index; SDI, socio-demographic index; TBLC, tracheal, bronchus, and lung cancer.
Discussion
This study highlights that the incidence and mortality of TBLC exceeded 2 million in 2021. Despite this substantial burden, ASIR and ASDR have shown a progressive decline since 1990, reflecting an overall improvement in TBLC outcomes. These findings align with global lung cancer trends reported in the GLOBOCAN database. 15 Smoking remains the predominant risk factor for TBLC, and the implementation of the WHO Framework Convention on Tobacco Control (FCTC) has significantly contributed to the reduction in smoking prevalence and the associated disease burden. 16 For instance, without tobacco control measures, US per capita cigarette consumption in 2011 would have been five times higher. 17 By 2022, tobacco control efforts had averted over 3.85 million lung cancer deaths globally, 18 thus underscoring the pivotal role of such policies in mitigating the TBLC burden.
From a gender perspective, in 2021, the ASIR and ASDR of TBLC in males were 2.25 and 2.36 times higher than in females, respectively, highlighting a significantly greater TBLC burden among males. However, the EAPCs in ASIR and ASDR for females from 1990 to 2021 were 0.51 (0.42, 0.6) and 0.17 (0.09, 0.25), respectively, indicating less pronounced improvements in TBLC outcomes among females compared with males. This gender disparity aligns with the findings from lung cancer studies in Europe and North America, 19 reinforcing the global trend of a higher TBLC burden in males. Smoking and tobacco control policies are key contributors to this disparity. 20 Notably, significant gender differences in TBLC burden are absent among non-smokers, 21 underscoring the role of higher smoking prevalence among males. Tobacco control measures have effectively reduced male smoking rates, leading to declines in TBLC incidence and mortality. Additionally, gender differences in TBLC pathological subtypes may play a role. Females exhibit stronger immune responses to lung adenocarcinoma than males, 22 and in 2022, adenocarcinoma accounted for 59.7% of cases in females compared with 45.6% in males, 23 potentially contributing to the higher TBLC burden in males.
Regionally, the ASIR (36.41) and ASDR (32.38) of TBLC in high-middle SDI regions were significantly higher than those in low SDI regions (5.5 and 5.91, respectively), consistent with previous findings. 24 In high-income countries where smoking rates have peaked, lung cancer incidence and mortality, while initially high, have now reached a peak and are declining. 25 This study confirmed declining incidence and mortality in high SDI regions, with EAPCs for ASIR, ASDR, and age-standardized DALYs rate at −0.72, −1.14, and −1.52, respectively. While tobacco control had led to reduced TBLC incidence and mortality, regional environmental factors may drive high TBLC prevalence in some areas. For example, East Asia had the highest TBLC ASDR (38.53) in this study, corresponding to findings that over 50% of PM2.5-related lung cancer deaths occur in this region. 26 These findings suggest that the TBLC burden in low SDI regions may increase with ongoing economic development, potentially driven by rising smoking rates and environmental exposures.
This study is the first global systematic analysis to use QCI to characterize cross-national disparities in a composite proxy indicator for TBLC. Supplementary analyses based on GBD 2023 data showed that the PCA-derived QCI structure remained stable, the SDI gradient persisted, and the main associations of QCI with sex, SDI, and age were unchanged, although some country rankings and the age-specific peak were modestly updated ( Figures S3–S5 ; Table S4 , and Data S1–S4 ). Universal health coverage remains a critical goal worldwide, and the quantitative nature of QCI provides a robust tool for policymakers to allocate health resources more effectively. Previous global QCI studies across multiple malignancies—including hematologic malignancies, 6 leukemia, 7 liver cancer, 27 lip and oral cavity cancer, 28 cervical and ovarian cancer, 29 skin malignant melanoma, 30 multiple myeloma, 31 thyroid cancer, 32 and gallbladder and biliary tract cancer 33 —have consistently shown that QCI tends to be more favorable in higher-SDI settings and remains unequally distributed across age and sex groups. Although no pre-2021 global TBLC-specific QCI study was identified, our findings can still be interpreted in the context of two related bodies of literature. Previous GBD-based analyses of TBLC showed that the global age-standardized burden declined overall, but important disparities by sex and SDI persisted, with less favorable trends in females and in lower-SDI settings. This is consistent with our findings that show slower improvement in females and persistently less favorable QCI patterns in low- and low-middle-SDI regions. 34 In addition, previous global QCI studies in other malignancies, including thyroid cancer and gallbladder and biliary tract cancer, 32 , 33 similarly found that QCI was more favorable in higher-SDI settings and remained unequally distributed across age and sex groups. In our study, QCI was strongly correlated with HAQI, remained consistently higher in high- and high-middle-SDI regions, and was positively associated with SDI in the LMM, whereas older age was associated with lower QCI, and AQCI was below 1 in most countries. Compared with these earlier studies, our analysis further highlights marked heterogeneity within high-SDI settings and extends the QCI framework by introducing AQCI to capture age-related disparity in QCI. These differences may reflect TBLC-specific factors such as smoking patterns, environmental exposure, screening uptake, access to newer therapies, and population aging.
Methodologically, our study further extended this literature by constructing QCI that uses both PC1 and PC2 components, thereby effectively mitigating biases associated with single-component analysis. 7 This approach allows for precise evaluation of QCI across countries, geographic regions, and SDI levels, facilitating comparisons across age and gender groups. In 2021, Japan, Singapore, and South Korea had the highest global QCI scores, with Japan consistently ranking first from 1990 to 2021. This success may be related to Japan’s efficient care practices, low smoking rates, and investment in targeted therapies for advanced lung cancer. 35 For instance, Japan emphasizes continuous psychological care and support for patients with poor prognoses, 36 while certified cancer care specialists provide appropriate supportive care. 37 South Korea, another East Asian country, has shown one of the fastest QCI increases over the past 30 years, driven by its cancer screening programs 38 and culturally defined family roles in patient care. 39 Given the prominent role of the Republic of Korea in our findings, recent nationwide cohort studies from the Republic of Korea also highlight the strength of its healthcare data infrastructure. 40 , 41 Further investigation into the successful strategies of high-QCI countries, such as Japan and South Korea, is warranted to inform and improve cancer-service planning and related outcomes in low-QCI settings, including Lesotho, Kiribati, and the Marshall Islands.
In addition to country-specific policies and care resources, gender is a significant factor influencing QCI disparities of TBLC. This study found that from 1990 to 2021, females consistently exhibited higher QCI than males. As previously discussed, differences in histopathology and treatment responses between genders contribute to this disparity. 42 Additionally, gender-based variations in recommended surgical treatments for lung cancer may play a role. 43 From an SDI perspective, high SDI regions consistently demonstrated higher QCI than low SDI regions from 1990 to 2021, thus underscoring the impact of economic development on QCI. Higher economic levels provide advantages in early diagnosis, treatment capacity, and screening and prevention programs. 44 Conversely, low SDI regions often prioritize resources for chronic infectious diseases, which may compromise cancer-service capacity and related outcomes. 45 Notably, the Euclidean distance of QCI among high SDI countries (2.037) was significantly larger than that among low SDI countries (0.47), indicating greater heterogeneity in the proxy pattern captured by QCI among high SDI nations. This pattern suggests that high socioeconomic development does not necessarily translate into a uniform care-related outcome profile. Even across high-income settings, differences in cancer-service organization, screening intensity, access to newer therapies, and supportive-care pathways can produce substantial between-country variation, and higher national cancer spending alone does not necessarily correspond to lower cancer mortality. 46 These differences may also reflect variation in how health systems organize lung-cancer care pathways, centralize diagnostic and treatment services, and allocate resources across screening, active treatment, supportive care, and age-adapted long-term management, as well as differences in the design and implementation of national cancer control policies. 46 , 47
From an age perspective, QCI of TBLC showed a negative correlation with age from 1990 to 2021, indicating lower QCI values for older patients within this proxy framework. However, this age pattern should be interpreted cautiously. The very high QCI values observed in younger adults may reflect not only age-related differences in disease biology and survival structure but also instability of the ratio-based components under sparse-event conditions, because TBLC burden is extremely low at younger ages. These age-specific peaks, therefore, should not be interpreted as evidence of better care quality in younger adults. In LMM analysis, age groups exhibited a non-linear relationship with QCI, highlighting a less favorable QCI profile in older adults with TBLC. This aligns with QCI trends observed in other malignancies, 7 , 48 with key factors including higher comorbidity and treatment risks in elderly TBLC patients. For instance, elderly lung cancer patients faced increased cardiovascular risks post-chemotherapy (OR: 2.03), 49 and their tumor microenvironment differs significantly from younger patients, leading to poorer responses to therapies like PD-L1. 50
Given the opposing effects of SDI and age on QCI, this study introduced AQCI (the ratio of QCI in patients aged 65+ to the overall population QCI) to evaluate aging-related differences in QCI across countries. Globally, over 98% of countries had AQCI <1, indicating lower QCI values in populations aged 65 years and older than in the overall population. High SDI countries exhibited higher AQCI than low SDI countries, although this pattern should be interpreted cautiously because AQCI may also reflect population aging, detection patterns, case mix, and the proxy structure of QCI. 51 , 52 Notably, AQCI variability was greater among high SDI countries, with Japan, despite ranking first in QCI, showing a decline in AQCI from 1990 to 2021, suggesting widening age-related gaps in the QCI pattern captured by this proxy indicator. This pattern may be particularly relevant in rapidly aging settings such as Japan and the Republic of Korea, where high overall cancer-system performance can coexist with increasing challenges in age-adapted care. In Japan, geriatric oncology has been recognized as an important but still evolving field with limited oncology-geriatrics integration, 53 , 54 while recent recommendations continue to emphasize the importance of comprehensive geriatric assessment in treatment decision-making for older adults with cancer. 53 In the Repulic of Korea, cancer-control system has continued to expand screening, treatment, and data infrastructure under one of the world’s fastest aging transitions, 55 , 56 and national lung-cancer screening implementation studies suggest that screening expansion and policy transition may also reshape the age distribution and detection profile of lung cancer over time. 55 , 57 To address age-related challenges in high-QCI, rapidly aging settings such as Japan, strategies such as enhanced support for family caregivers, 58 improved end-of-life care, 59 and better integration of geriatric and oncology services may be helpful. 60 Accordingly, the decline in AQCI in these settings is more plausibly interpreted in the context of rapid population aging, changing cancer detection patterns, and increasing demands on age-adapted oncology care rather than as direct evidence of deteriorating elderly care.
Overall, by integrating both PC1 and PC2 in the construction of the QCI, this study reduced the potential bias inherent in single-component approaches. The strong correlation with the HAQI further supports the validity of this framework for assessing global disparities and improvement potential in QCI for TBLC across socioeconomic and demographic strata. Policy priorities should, therefore, be tailored to SDI level. In low-SDI regions, the priority is to strengthen basic cancer-service capacity through tobacco control, earlier diagnosis, referral networks, pathology support, and essential treatment services. In middle-SDI regions, efforts should focus on improving standardized diagnostic and treatment pathways, multidisciplinary care, and access to oncology care. In high-SDI regions, where AQCI declines despite relatively high QCI, priority should be given to geriatric assessment and age-adapted oncology care, and better integration of supportive and survivorship services. In addition, high-performing settings should pay closer attention to age-specific equity within national cancer control programs, including how resources are distributed across screening, treatment, supportive care, and geriatric-oncology integration. Overall, SDI-stratified and age-sensitive strategies are needed to reduce inequities and improve TBLC outcomes worldwide, but these findings should not be interpreted as indicating uniform deterioration in elderly care across aging populations.
Limitations of the study
However, limitations also exist. First, while PC1 and PC2 reflect different aspects of the composite proxy structure, the use of endogenous weights rather than empirical weights may introduce endogeneity. Future studies should validate and refine these findings using larger datasets. Second, because the GBD database provides harmonized population-level epidemiological estimates rather than patient-reported outcomes or clinical process indicators, factors such as patient satisfaction, staff responsiveness, treatment reliability, and treatment effectiveness could not be directly incorporated. Third, QCI did not directly measure healthcare accessibility, but inferred it indirectly from epidemiological ratios. Future studies should link GBD-based comparative analyses with population-based cancer registries, hospital-based clinical databases, administrative claims, and patient-reported datasets to construct more direct accessibility dimensions, such as stage at diagnosis, treatment delay, and geographic access to oncology services. Moreover, because QCI is derived from GBD-based epidemiological ratios, it may be influenced not only by health-care delivery but also by disease biology, risk-factor structure, diagnostic capacity, case ascertainment, cancer registry completeness, mortality data quality, and coding practices. Additionally, AQCI should be interpreted cautiously. Because TBLC is rare in young adults, QCI in these groups may be unstable and should not be interpreted as indicating better care quality. Therefore, age-related differences in QCI, including AQCI, may reflect not only health-care delivery, but also disease biology and the behavior of ratio-based indicators in low-incidence age groups. 61 In addition, the decline in AQCI observed in high-SDI countries should be interpreted cautiously, as it may reflect rapid demographic shifts and changes in cancer detection patterns rather than solely a meaningful deterioration in elderly QoC. 62 Future research should incorporate more detailed age-stratified data to strengthen conclusions. Despite these limitations, this study provides a robust framework for global TBLC QCI-based comparative evaluation and lays a scientific foundation for further research and policy development.
Resource availability
Lead contact
Further information and requests for resources should be directed to the lead contact, Chen Xu ( 377197448@qq.com ).
Materials availability
This study did not generate new unique reagents, materials, or experimental models.
Data and code availability
• The data used for these analyses are all publicly available at http://ghdx.healthdata.org/gbd-2021 . • Processed data and analytical code are available from the lead contact upon reasonable request.
Acknowledgments
This study was supported by Hospital Level Scientific Research Fund Program of Shanghai Sixth People’s Hospital (grant nos. X-4379-LG , X-4286 , ynqn202211 , and hlyjkt202325 ), 10.13039/501100012226 Fundamental Research Funds for the Central Universities (grant nos. YG2025QNA44 and YG2025QNB51 ), and Shanghai Oriental Talent Program Youth Project (grant no. QNZH2024002 ). We sincerely appreciate the exceptional cooperation of our study team members and gratefully acknowledge the significant contributions of the Global Burden of Disease study.
Author contributions
Conceptualization, C.X., J.Y., and J.W.; methodology, Y.W., E.Z., Z.S., and T.Z.; software, T.Z.; validation, Y.W., E.Z., and Z.S.; formal analysis, Y.W., E.Z., Z.S., and T.Z.; data curation, W.L. and W.H.; visualization, Z.S.; writing – original draft, Y.W., E.Z., and Z.S.; writing – review and editing, W.H., T.Z., C.X., J.Y., J.W., S.J., F.F., W.L., and H.Y.; supervision, C.X., J.Y., J.W., S.J., and F.F.; project administration, H.Y. All authors made a significant contribution to the work reported, approved the final version of the manuscript, and agreed to its submission.
Declaration of interests
The authors declare no competing interests.
STAR★Methods
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data Global Burden of Disease Study 2021 estimates Institute for Health Metrics and Evaluation (IHME), Global Health Data Exchange/GBD Results Tool GBD 2021; https://ghdx.healthdata.org/gbd-2021 ; https://vizhub.healthdata.org/gbd-results/ Software and algorithms R R Foundation, Vienna, Austria version 4.4.3; https://www.r-project.org Adobe Illustrator Adobe Inc., San Jose, CA, USA version 2023; https://www.adobe.com/products/illustrator.html
| REAGENT or RESOURCE | SOURCE | IDENTIFIER |
| Deposited data | ||
| Global Burden of Disease Study 2021 estimates | Institute for Health Metrics and Evaluation (IHME), Global Health Data Exchange/GBD Results Tool | GBD 2021; https://ghdx.healthdata.org/gbd-2021 ; https://vizhub.healthdata.org/gbd-results/ |
| Software and algorithms | ||
| R | R Foundation, Vienna, Austria | version 4.4.3; https://www.r-project.org |
| Adobe Illustrator | Adobe Inc., San Jose, CA, USA | version 2023; https://www.adobe.com/products/illustrator.html |
Method details
Data source and study design
The GBD provides comprehensive estimates of disease incidence and mortality across countries and regions by integrating multiple data sources. Data for this study were obtained from the GBD Results Tool available on the GBD database website ( https://ghdx.healthdata.org/gbd-2021 ), with detailed methodologies referenced from previous studies. 63 We extracted data on deaths, incidence, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), and prevalence of TBLC globally from 1990 to 2021, stratified by sex, age, and region. The socio-demographic index (SDI), which reflects a country’s overall development level and its association with health outcomes, was downloaded from GBD compare for the period 1990–2021. 64 World-standardized population data were derived from the GBD 2021 study. 65 Additionally, the healthcare access and quality index (HAQI), developed by the GBD researchers, was utilized to evaluate healthcare quality across countries and regions. 66
Definitions of sex, age, SDI, and disease classification
Gender stratification included three categories: both sexes, females, and males. Due to the lack of mortality data for TBLC in individuals under 15 years, age groups were divided into 5-year intervals (15–19, 20–24, …, 90–94, 95+), all ages, and age-standardized ages. Countries and regions were classified into five SDI quintiles: high, high-middle, middle, low-middle, and low SDI. 67 TBLC classification followed ICD-10 codes, including C33 (malignant neoplasms of the trachea) and C34-C34.92 (malignant neoplasms of the bronchi and lungs).
Estimation of temporal trends using EAPC
To assess the burden of TBLC across regions and over time, age-standardized rates (ASRs) from the GBD database were utilized. The estimated annual percentage change (EAPC), a measure of ASR trends, was calculated using a linear regression model. The natural logarithm of ASR was the dependent variable ( y ), and the year was the independent variable ( x ). The regression coefficient ( β ) was used to derive the EAPC, expressed by the formula: y = α + β x + ε E A P C = 100 × ( exp ( β ) − 1 ) where β represents the slope of the regression line, indicating the rate of change in the natural logarithm of ASR over time.
Construction of the quality of care index
In this study, QCI was used as an indirect composite proxy indicator related to population-level outcomes and medical service context, rather than as a direct clinically grounded measure of QoC. Six primary indicators were used to derive four secondary metrics: the YLLs to YLDs ratio (YLR), DALYs to prevalence ratio (DPR), mortality to incidence ratio (MIR), and prevalence to incidence ratio (PIR). The YLR assesses premature mortality by standardizing quality of life levels, 27 while the DPR evaluates mortality relative to existing morbidity. The MIR measures mortality against incident cases, and the PIR compares the existing disease burden to new incidence levels. 28 Principal component analysis (PCA) was applied to integrate the YLR, DPR, MIR, and PIR into a composite score. Before PCA, YLR, DPR, MIR, and PIR were standardized using the scale = TRUE option in the prcomp function, corresponding to Z score standardization. Sampling adequacy was additionally assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity. The KMO value was 0.779, and Bartlett’s test of sphericity was significant (χ 2 = 48,557.82, df = 6, p < 0.001), supporting the suitability of the data for PCA. Traditionally, the first principal component (PC1) represents the QCI, as it explains the maximum variance in the data and captures the primary trend. However, in certain scenarios (e.g., global multiple myeloma analysis), the PIR contributes minimally to PC1 and primarily influences the second principal component (PC2), potentially resulting in the loss of critical information. 31 To enhance the accuracy and comprehensiveness of this composite proxy indicator, this study utilized an improved method, 7 integrating PC1 and PC2 weighted by their respective variance contributions. In the present analysis, PC1 and PC2 explained 94.52% and 3.73% of the total variance, respectively, and were combined using these variance contributions as weights to generate the modified QCI. The QCI was scaled to a range of 0–100 via linear transformation, with higher values indicating a more favorable value of this composite proxy indicator. The formula is detailed below: Y L R = Y L L s Y L D s D P R = D A L Y s P r e v a l e n c e M I R = M o r t a l i t y I n c i d e n c e P I R = P r e v a l e n c e I n c i d e n c e P C 1 = ω 1 · Y L R + ω 2 · D P R + ω 3 · M I R + ω 4 · P I R P C 2 = ω 5 · Y L R + ω 6 · D P R + ω 7 · M I R + ω 8 · P I R where ω 1 , ω 2 , … …,ω 8 represent the weights determined by PCA, reflecting the contribution of each ratio to the principal components. PC1 captures the primary variation in the data, while PC2 supplements the additional variability not explained by PC1. P C A s c o r e ( x ) = ( V a r ( P C 1 ) V a r ( P C 1 ) + V a r ( P C 2 ) ) × P C 1 + ( V a r ( P C 2 ) V a r ( P C 1 ) + V a r ( P C 2 ) ) × P C 2 where Var(PC1) represents the variance explained by the first principal component, and Var(PC2) represents the variance explained by the second principal component. Q C I ( x ) = P C A s c o r e ( x ) − min P C A s c o r e ( x ) max P C A s c o r e ( x ) − min P C A s c o r e ( x ) × 100 % In this formula, QCI ( x )represents the QCI for the observation x . By subtracting the minimum PCA score from QCI ( x ) and dividing by the range of PCA scores, the index is standardized to a specified interval. This standardization process ensures that the QCI reflects the relative position of this composite proxy indicator, with higher scores indicating more favorable values of this composite proxy indicator.
Assessment of aging-related disparities in QCI
To evaluate the level of aging-related QCI across different countries, we defined an indicator termed AQCI (Aging-related QCI). AQCI was designed as a disease-specific, age-relative indicator for TBLC, complementing existing healthy-ageing metrics by capturing age-related disparity in QCI within the same setting. 68 , 69 The formula is expressed as: A Q C I x , y = mean ( Q C I x , y , a , a ∈ { 65 − 69 , … , 95 + } ) mean ( Q C I x , y , a , a ∈ { 15 − 19 , … , 95 + } ) Where QCI x , y , a represents the QCI value for age group a in region x during year y. A value of 1 indicated parity between older adults and the overall analyzed TBLC population aged 15 years and older, values below 1 indicated a less favourable pattern in older adults, and values above 1 indicated a more favourable pattern.
Linear mixed-effects modeling of QCI
A linear mixed model (LMM) was used to evaluate the influence of age, sex, and sociodemographic index (SDI) on QCI while accounting for broad temporal clustering in the dataset. Given the hierarchical structure of the data, age group, sex, and SDI were included as fixed effects, and year group was included as a random intercept. To capture broad temporal heterogeneity while avoiding excessive fragmentation of the random-effect structure, the study period was divided into three approximately balanced intervals: 1990–2000, 2001–2010, and 2011–2021. Model fit was assessed using the Akaike information criterion (AIC), Bayesian information criterion (BIC), and the intraclass correlation coefficient (ICC), with ICC used to quantify the proportion of variance attributable to clustering by year group.The formula is defined as: Q C I i j k = β 0 + β 1 S e x i + β 2 A g e j + β 3 S D I k + b l + ϵ i j k l Where β 1 Sex i represents the fixed effect of gender grouping, β 2 Age j represents the fixed effect of age grouping, β 3 SDI k represents the fixed effect of SDI level, b l represents the random effect of year grouping, and ϵ ijkl is the residual error term, capturing unexplained random variation in the model.
Quantification and statistical analysis
Descriptive analyses were performed to assess global incidence, mortality, and DALYs of TBLC from 1990 to 2021. Temporal trends were quantified using EAPC derived from linear regression models. The QCI for countries and regions was computed via PCA. The correlation between QCI and HAQI was assessed based on data distribution. Normality was evaluated using the Kolmogorov-Smirnov (K-S) test. The Pearson correlation coefficient was applied if both variables followed a normal distribution; otherwise, the Spearman correlation coefficient was used. Subgroup analyses of QCI values across 204 countries were conducted by sex, SDI, and age groups, with intergroup differences assessed using ANOVA and Wilcoxon rank-sum tests. To evaluate the effects of age, sex, SDI, and year on QCI, LMM was employed, addressing potential multicollinearity through VIF tests. Fixed effects were analyzed using ANOVA test. Key indicators were reported with 95% uncertainty intervals (UI), and statistical significance was set at a two-sided p -value <0.05.
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Supplemental information
Document S1. Figures S1–S5, Tables S1–S4, and Data S1–S4 Table S5. EAPC and 95% CI of the QCI for TBLC across 204 countries from 1990 to 2021
Supplemental information can be found online at https://doi.org/10.1016/j.isci.2026.116604 .