Urban–Rural and Socioeconomic Disparities in Lymphoma Outcomes: Evidence from a Population-Based Study in Xiamen, China

Urban–Rural and Socioeconomic Disparities in Lymphoma Outcomes: Evidence from a Population-Based Study in Xiamen, China | 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 Urban–Rural and Socioeconomic Disparities in Lymphoma Outcomes: Evidence from a Population-Based Study in Xiamen, China Wenting Luo, Yilan Lin, Xingyu Li, Jiahuang Chi, Yanxin Luo, Xingou Zhuang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7966695/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Regional data on lymphoma in China remain limited. We analyzed incidence, mortality, and survival trends in Xiamen from 2011–2020 to identify demographic and socioeconomic factors associated with outcomes. Methods All newly diagnosed lymphoma cases (ICD-10 C81–C86, C96) were retrieved from the Xiamen Cancer Registry. Age-standardized incidence and mortality rates (ASIR, ASMR) were calculated using Segi’s world standard population. Survival was assessed using observed survival, relative survival, and age-standardized relative survival. Cox regression identified independent prognostic factors. Results Between 2011 and 2020, 1,436 lymphoma cases were recorded. The ASIR was higher in males than females (6.44 vs. 4.34 per 100,000) and in urban versus rural residents (5.99 vs. 4.05 per 100,000). Mortality was also elevated in males (ASMR 3.57 vs. 2.10 per 100,000) and urban residents (3.14 vs. 2.10 per 100,000). Five-year age-standardized relative survival was 48.98% overall, higher in females than males (52.40% vs. 47.09%) and in urban than rural residents (50.64% vs. 41.44%). Multivariable Cox regression identified older age, rural residence, marital status, education, lymphoma subtype, and earlier diagnosis period (2011–2015) as independent predictors of poorer prognosis. Conclusion Lymphoma incidence and mortality in Xiamen reflect gender and regional disparities, while survival outcomes are strongly influenced by demographic and socioeconomic factors. These findings underscore the need for targeted cancer control strategies addressing urban–rural inequities and socioeconomic barriers. lymphoma incidence mortality survival epidemiology prognostic factors Figures Figure 1 Figure 2 Introduction Lymphoma is a heterogeneous group of hematological malignancies with substantial variation in incidence, survival, and mortality across regions and populations 1 . According to GLOBOCAN 2020, the age-standardized incidence rate (ASIR) of non-Hodgkin lymphoma (NHL) in China was 6.7 per 100,000, with an estimated 92,834 new cases and 54,351 deaths 2 . Both Hodgkin lymphoma (HL) and NHL contribute significantly to the global cancer burden, though their patterns differ by geography, age, and socioeconomic context 2 . In China, the incidence of lymphoma has been increasing steadily over the past two decades 3,4 . National cancer registry reports have documented rising trends and changing age distributions, with a growing proportion of cases diagnosed at younger ages 3 . Despite these increases, survival outcomes remain poorer than those observed in high-income countries 2 . For example, 5-year relative survival for NHL in China is below 50%, compared with 60–70% in Europe and North America 5 . These gaps highlight differences in early diagnosis, access to care, and health system capacity. Most prior Chinese studies have described national or provincial patterns 6–8 . However, regional evidence remains limited, and important questions persist regarding how socioeconomic status, education, and marital status influence survival. Urban–rural disparities are of particular concern, as prior registry studies suggest that rural residents may face delayed diagnosis, limited access to specialized care, and poorer outcomes 9 . Yet systematic analyses incorporating these factors are still scarce. Xiamen, a rapidly developing coastal city in southeastern China, provides a unique context for studying these disparities. The city is characterized by distinct urban–rural contrasts and a high-quality cancer registry with internationally acceptable data quality indicators (MV% >98%, DCO% ~1%). This setting enables a robust evaluation of lymphoma burden and outcomes while also reflecting the broader challenges of balancing cancer control across regions undergoing rapid socioeconomic transition. Therefore, using data from the Xiamen Cancer Registry between 2011 and 2020, we aimed to: (i) describe incidence and mortality patterns of lymphoma; (ii) evaluate survival outcomes by demographic and socioeconomic subgroups; and (iii) identify independent prognostic factors using multivariable models. By situating regional findings within the national and international context, this study provides evidence on health inequities that can inform cancer control strategies in China and contribute to the global discussion on disparities in hematologic malignancies. Materials and Methods Data source and case definition Data were obtained from the Xiamen Cancer Registry, a population-based registry that covers the entire permanent resident population of Xiamen (approximately 4.3 million). All incident cases of lymphoma diagnosed between January 1, 2011, and December 31, 2020 were included. Cases were identified according to the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) and were grouped into Hodgkin lymphoma (HL, C81) and non-Hodgkin lymphoma (NHL, C82–C85, C96), with multiple myeloma (C90) excluded from the present analysis. Only first primary cases were included. Data quality control The registry follows the standards of the Chinese National Central Cancer Registry (NCCR) and the International Agency for Research on Cancer (IARC). Quality indicators were assessed annually, including the proportion of morphologically verified cases (MV%), the proportion of cases identified through death certificate only (DCO%), and the mortality-to-incidence ratio (M/I). For the study period, MV% exceeded 98%, DCO% was approximately 1%, and M/I ranged from 0.45 to 0.55, indicating high-quality and reliable registry data. Incidence and mortality calculation Crude incidence and mortality rates were calculated per 100,000 person-years. Age-standardized rates were computed using the direct method, with the Segi world standard population as the reference. Rates were reported overall and stratified by sex and urban–rural residence. Survival analysis Patient survival was assessed using both observed survival (OS) and relative survival (RS), with the latter defined as the ratio of observed to expected survival. Expected survival was derived using the Ederer II method with national life tables. Age-standardized relative survival (ARS) was calculated using the International Cancer Survival Standard (ICSS) weights to facilitate comparability across populations. Kaplan–Meier curves were used to visualize survival differences across subgroups, and differences were tested with the log-rank method. Prognostic factor analysis Cox proportional hazards models were fitted to identify independent prognostic factors, including sex, age group, residence (urban vs. rural), education level, marital status, histological subtype, and diagnosis period (2011–2015 vs. 2016–2020). Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. The proportional hazards assumption was assessed using Schoenfeld residuals and found to be satisfied. Trend analysis Time trends in incidence, mortality, and survival were evaluated using Joinpoint regression analysis (Joinpoint software, version 4.9.1.0, US National Cancer Institute). Annual percent change (APC) and average annual percent change (AAPC) with 95% CIs were estimated. Statistical significance of trends was assessed using the Monte Carlo permutation method. When Joinpoint analysis was not feasible due to limited data points, Cochran–Armitage tests for trend were applied. All statistical analyses were conducted using R software (version 4.2.2, R Foundation for Statistical Computing) and Joinpoint regression program (version 4.9.1.0). A two-sided p-value < 0.05 was considered statistically significant. Results Incidence trends Incidence trends by sex and region From 2011 to 2020, a total of 1,436 new cases of lymphoma were recorded in Xiamen. The crude incidence rate was 6.64 per 100,000, and the age-standardized incidence rate (ASIR) was 5.36 per 100,000 ( Table 1 ). Incidence was consistently higher in men than women (ASIR: 6.44 vs. 4.34 per 100,000) and in urban compared with rural residents (ASIR: 5.99 vs. 4.05 per 100,000). Trend analysis revealed that, overall, incidence rates remained relatively stable during the study period. However, in rural areas, both the crude incidence and ASIR showed significant upward trends. The crude rate increased with an average annual percent change (AAPC) of 5.58% (95% CI: 2.20–9.08, p = 0.005), and the ASIR increased at 4.72% annually (95% CI: 1.66–7.87, p = 0.007). By contrast, no significant temporal changes were detected among males, females, or urban residents. These findings highlight the growing burden of lymphoma in rural populations and are summarized in Table 1 and illustrated in Figure 1 . Mortality trends by sex and region By September 30, 2023, a total of 762 lymphoma patients had died, including 646 deaths directly attributed to lymphoma and 116 from other causes. The crude mortality rate was 3.53 per 100,000, and the age-standardized mortality rate (ASMR) was 2.80 per 100,000 ( Table 1 ). Mortality was higher in men than women (ASMR: 3.57 vs. 2.10 per 100,000) and in urban compared with rural residents (ASMR: 3.14 vs. 2.10 per 100,000). Mortality trends displayed a biphasic pattern. From 2011 to 2014, crude mortality rose significantly (APC = 16.33%, 95% CI: 4.78–29.16), and ASMR showed a parallel increase (APC = 15.33%, 95% CI: 2.48–29.80). However, from 2014 to 2020, both crude mortality and ASMR declined significantly (crude APC = –4.80%, 95% CI: –8.11 to –1.38; ASMR APC = –6.68%, 95% CI: –10.34 to –2.88). Among men, mortality decreased steadily after 2014, with ASMR falling by 10.74% annually (95% CI: –16.86 to –4.17). In urban areas, crude mortality first increased (APC = 15.91%, 95% CI: 0.83–33.24, 2011–2014) and then decreased significantly thereafter (APC = –5.89%, 95% CI: –10.22 to –1.35, 2014–2020). No statistically significant temporal change was observed in rural residents. Mortality patterns are detailed in Table 1 and depicted in Figure 1 . Survival outcomes Overall, the 5-year observed survival (OS), relative survival (RS), and age-standardized relative survival (ARS) were 49.38% (95% CI: 46.28–52.40), 52.94% (95% CI: 49.62–56.18), and 48.98% (95% CI: 45.35–52.91), respectively ( Table 1 ). Females had higher survival than males (5-year ARS: 52.40% vs. 47.09%), and urban residents had better outcomes than rural residents (50.64% vs. 41.44%). When stratified by subtype, Hodgkin lymphoma patients had the most favorable prognosis (5-year ARS: 82.26%, 95% CI: 76.09–88.94), while outcomes were poorer for non-Hodgkin lymphoma (48.26%), multiple myeloma (37.96%), and immunoproliferative disorders (29.49%) ( Table 2 ). Survival also varied by sociodemographic characteristics. Separated, divorced, or widowed individuals had the highest survival (63.92%) compared with married (47.96%) and single patients (42.30%). Educational attainment was strongly associated with outcome: patients with at least high school or technical education had markedly better survival (63.92%) than those with junior high school or less (42.44%). Age was a critical determinant of prognosis. Patients aged 0–44 years had a 5-year ARS of 73.98%, compared with only 28.69% in those aged ≥75 years ( Table 2 ). Female patients maintained a survival advantage across nearly all age groups, except in the 45–54 age category. Kaplan–Meier survival curves ( Figure 2 ) demonstrate these disparities by sex and residence. Temporal analysis demonstrated clear improvements in survival over the study period ( Table 3 ). The overall 5-year ARS increased from 42.6% in 2011–2015 to 52.6% in 2016–2020. Male patients showed significant improvements, with an AAPC of 9.99% (95% CI: 4.74–15.51, p = 0.003). Urban residents also experienced gains between 2015 and 2020 (APC = 9.83%, 95% CI: 1.15–14.99, p = 0.033). However, no significant temporal improvements were detected among rural residents. Detailed results for 1-, 3-, and 5-year ARS by sex and residence are presented in Table 3 . Prognostic factors (Cox regression analysis) A total of 1,358 patients were included in the multivariable Cox regression analysis after excluding 15 death certificate–only cases and 64 with multiple primaries. Six independent prognostic factors were identified: age, residence, marital status, education, lymphoma subtype, and diagnosis period ( Table 4 ). Older age was strongly associated with increased mortality. Patients ≥75 years had more than a five-fold higher risk of death compared with those aged 0–44 years (HR = 5.06, 95% CI: 3.51–7.28, p < 0.001). Rural residence conferred a survival disadvantage relative to urban residence (HR = 1.25, 95% CI: 1.02–1.53, p = 0.028). Education level demonstrated a U-shaped association: both ≤junior high (HR = 1.52, 95% CI: 1.22–1.91, p = 0.0003) and ≥college (HR = 1.35, 95% CI: 1.02–1.79, p = 0.037) were associated with poorer survival compared with the reference group of high school/technical education. Significant improvements over time were confirmed: patients diagnosed between 2011–2015 had a higher risk of death than those diagnosed in 2016–2020 (HR = 1.44, 95% CI: 1.24–1.68, p < 0.001). Lymphoma subtype was also predictive: compared with Hodgkin lymphoma, non-Hodgkin lymphoma (HR = 1.98, 95% CI: 1.20–3.27, p = 0.010) and multiple myeloma (HR = 2.23, 95% CI: 1.33–3.75, p < 0.001) were associated with substantially higher mortality ( Table 4 ). Discussion This population-based study provides a comprehensive overview of lymphoma epidemiology in Xiamen, southeastern China, over a ten-year period. By integrating high-quality registry data with sociodemographic information, we identified clear disparities in incidence, mortality, and survival across sex, residence, and socioeconomic factors. Several important findings emerge from this analysis. Incidence and mortality trends Consistent with national registry reports 10–12 and international observations 13–15 , the incidence and mortality of lymphoma were higher in men than in women. Biological differences, such as sex-specific immune responses and hormonal influences, have been proposed as possible explanations for these patterns 16,17 . In addition, differences in health-seeking behavior and comorbidity burden may contribute to poorer outcomes among men 18 . Urban–rural disparities were also evident. Incidence rates were higher in urban areas, likely reflecting both greater diagnostic capacity and lifestyle-related risk factors associated with urbanization. Conversely, mortality declined more markedly in urban than rural populations, underscoring the persistent inequities in healthcare access. Similar patterns have been reported in other Chinese provinces 11,19 and in global cancer surveillance studies 9,15 . Rural patients may experience delayed diagnosis and reduced access to hematology specialists, advanced therapies, or standardized treatment protocols, leading to poorer survival outcomes. Survival outcomes The 5-year ARS of 49% observed in this study is broadly consistent with other Chinese registries 6 but lower than survival reported in high-income countries, where NHL survival often exceeds 60–70% 5 . EUROCARE studies in Europe and SEER data from the United States have consistently documented higher survival, particularly for HL, which now approaches 80–85% at 5 years in many Western countries 5 . The survival gap highlights ongoing challenges in China, including limitations in early detection, treatment access, and follow-up care. Our findings also reaffirm the modest but consistent female survival advantage, a pattern seen internationally 18 , possibly related to differences in tumor biology, comorbidities, or treatment adherence. Socioeconomic disparities Education level and marital status emerged as significant predictors of survival. Patients with lower education experienced poorer outcomes, which may reflect reduced health literacy, financial constraints, and barriers to accessing timely care. Similar associations between education and lymphoma survival have been observed in both Asian and Western settings 20–22 . Interestingly, widowed or separated patients showed better survival in our analysis. This result contradicts prior evidence suggesting worse outcomes in unmarried individuals 19 . The discrepancy likely reflects residual confounding or misclassification within registry data and thus should be interpreted cautiously. Temporal improvements Encouragingly, survival improved over the study period, with 5-year ARS rising from 42.6% in 2011–2015 to 52.6% in 2016–2020. These gains likely reflect advances in diagnostic techniques, broader adoption of immunohistochemistry and molecular diagnostics, and improved availability of chemotherapy and immunotherapy agents. Policy initiatives aimed at strengthening cancer care capacity in China may also have contributed. Nonetheless, survival remains below that reported in developed countries, indicating that additional efforts are required to close the gap. Strengths and limitations The strengths of this study include use of a population-based registry with internationally acceptable data quality indicators (MV% >98%, DCO% ~1%), large sample size, and long-term follow-up. However, several limitations must be acknowledged. First, clinical information such as stage, treatment regimens, and comorbidities was unavailable, precluding adjustment for these important prognostic factors. Second, estimates for rare subtypes such as immunoproliferative diseases are unstable and should be interpreted with caution. Third, socioeconomic information was limited to education and marital status; more granular measures such as income or occupation were not available. Finally, as a single-city study, the findings may not fully represent other regions in China, although the patterns observed align with national trends. Implications Our results highlight the importance of addressing disparities in lymphoma outcomes from both national and international perspectives. Within China, targeted interventions are needed to improve access to early diagnosis and standardized treatment in rural populations, as well as to address socioeconomic barriers that affect patient outcomes. Globally, these findings contribute to the evidence base on health inequities in hematologic malignancies, aligning with World Health Organization (WHO) calls to integrate equity considerations into cancer control planning. Continued strengthening of cancer registries, combined with expanded clinical data linkages, will be essential to monitor progress and guide resource allocation. Conclusion In summary, this study demonstrates gender, urban–rural, and socioeconomic disparities in lymphoma incidence, mortality, and survival in Xiamen. Although survival has improved over time, it remains lower than in high-income countries. Future studies should incorporate detailed clinical and treatment information to clarify underlying mechanisms. Our findings are consistent with global trends but highlight unique disparities in China, underscoring the need for both national and international strategies to reduce inequalities in lymphoma outcomes. Declarations Disclosure The authors report no conflicts of interest in this work. Ethical approval and consent to participate The study was approved by the ethics committee of Xiamen City Center for Disease Control and Prevention (XJK/LLSC (2023)004).The need of informed consent was waived by the ethics committee of Xiamen City Center due to retrospective and anonymous study design, All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Funding This work is supported by the Xiamen Medical and Health Guidance Project (3502Z20224ZD1014), the Natural Scientific Foundation of Xiamen (No. 3502Z20227340), the Fujian Natural Science Foundation of China (No. 2022J011372) and the Fujian provincial health technology project (No.2022RBK016). Author Contribution The study concept and design were developed by YL and XZ. Data collection was carried out by JC and YL. WL and XZ conducted the statistical analysis and drafted the manuscript. XL participated in the discussions and revisions. All authors have read and approved the final manuscript. Acknowledgments We are grateful to all the subjects for their participation. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Thandra, K. C. et al. Epidemiology of Non-Hodgkin’s Lymphoma. Med. Sci. Basel Switz. 9 , 5 (2021). Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 74 , 229–263 (2024). Lin, K. et al. 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The incidence and mortality of lymphoma in Xiamen City from 2011 to 2020 Variable Group N Crude Rate Age Standardized Rate Rate (1/10 5 ) AAPC(95%CI） Rate (1/10 5 ) AAPC(95%CI） Incidence Gender: Male 826 7.71 1.07(-1.07, 3.27) 6.44 -0.27(-3.08, 2.62) Female 610 5.60 5.92(-1.64,14.07) 4.34 4.29(-4.00, 13.31) Suburb: Urban 1083 7.37 1.37(-1.26, 4.06) 5.99 -0.39(-3.41, 2.72) Rural 610 5.60 5.58 * (2.20, 9.08) 4.05 4.72 * (1.66, 7.87) Total 1436 6.64 2.59 * (0.43, 4.79) 5.36 1.14(-1.33, 3.67) Mortality Gender: Male 461 4.30 0.33 # (-4.26, 5.14) 3.57 -2.76 # (-7.07, 1.76) Female 301 2.76 3.63(-4.06, 1.94) 2.10 3.02(-3.57, 10.07) Suburb: Urban 579 3.94 0.88 # (-3.34, 5.28) 3.14 -1.35(-6.48, 4.06) Rural 183 2.65 2.20(-4.44, 9.30) 2.10 3.27(-10.16,18.70) Total 762 3.53 1.78 # (-1.44, 5.10) 2.80 0.15 # (-3.42, 3.84) Note: *P values 0.05. #P value of the annual percentage change (APC) <0.05: CR mortality in male APC=-6.52 (95%CI:[-11.22, -1.57]) from 2014 to 2020, in urban APC=15.91 (95%CI:[0.83, 33.24]) from 2011 to 2014 and APC=-5.89 (95%CI:[-10.22, -1.35]) from 2014 to 2020, in total APC=16.33 (95%CI:[4.78, 29.16]) from 2011 to 2014 and APC=-4.80 (95%CI:[-8.11, -1.38]) from 2014 to 2020; ASR mortality in male APC=-10.74 (95%CI:[-16.86, -4.17]) from 2015 to 2020, in total APC=15.33 (95%CI:[2.48, 29.80]) from 2011 to 2014 and APC=-6.68(95%CI:[-10.34, -2.88]) from 2014 to 2020. Table 2. The 5-year survival rate of lymphoma in Xiamen City from 2011 to 2020. Variable Observed Survival Rate (95%CI) Relative Survival Rate (95%CI) Age Standardized Rate Rate(95%CI) AAPC (95%CI) Gender: Male 47.10(43.02-51.06) 51.45(47.00-55.78) 47.09(42.50-52.16) 9.99 * (4.74, 15.51) Female 52.51(47.72-57.08) 54.95(49.93-59.72) 52.40(46.83-58.63) 1.12(-11.12, 15.04) Suburb: Urban 49.42(45.83-52.90) 53.18(49.31-56.92) 50.64(46.54-55.11) 1.47 # (-6.16, 9.73) Rural 49.30(43.08-55.21) 52.02(45.46-58.25) 41.44(34.30-50.06) 7.36(-5.86, 22.44) Total 49.38(46.28-52.4) 52.94(49.62-56.18) 48.98(45.35-52.91) 2.73(-4.00, 9.94) Abbreviations: AAPC, average annual percent change; CI, confidence interval. Note: * P values 0.05. #The APC (annual percentage change) from 2015 to 2020 was 9.83 (95%CI:1.15-14.99, P =0.033), there was no join point in the other groups. Table 3. The age-standardized relative survival rates of lymphoma in different year in Xiamen city from 2013 to 2020 survival year 2013 2014 2015 2016 2017 2018 2019 2020 AAPC * (95%CI) 1 86.52 67.60 73.97 78.64 80.42 80.26 87.24 79.88 1.18(-1.96, 4.41) 3 61.56 41.55 51.80 57.00 60.36 62.00 63.99 67.48 4.13(-0.76, 9.26) 5 61.65 34.97 45.02 53.57 49.06 52.04 57.31 56.98 2.73(-4.00, 9.94) Abbreviations: AAPC, average annual percent change; CI, confidence interval. Note: * P values >0.05. Table 4. Results of multivariate Cox regression analysis Variables Total (n=1358, %) 5-year ARS Hazard Ratio 95%CI of HR P -value Age (years old) 0-44 275(20.25) 73.98(67.78-79.20) 1.000 45-54 188(13.84) 58.24(49.87-65.76) 1.536 1.083-2.179 0.0161 55-64 326(24.01) 51.98(45.33-58.29) 1.702 1.212-2.390 0.0021 65-74 338(24.89) 45.55(38.52-52.43) 2.754 1.940-3.911 <0.0001 75+ 231(17.01) 28.69(20.11-38.36) 5.056 3.511-7.280 <0.0001 Sex Female 583(42.93) 52.40(46.83-58.63) 1.000 Male 775(57.07) 47.09(42.50-52.16) 1.087 0.933-1.268 0.2836 Suburb urban 1010(74.37) 50.64(46.54-55.11) 1.000 rural 348(25.63) 41.44(34.30-50.06) 1.251 1.024-1.529 0.0283 Marital status married 1013(74.59) 47.96(43.69-52.64) 1.000 single 82(6.04) 42.30(36.22-49.40) 0.929 0.566-1.527 0.7722 Separated/ divorced/widowed 203(14.95) 63.73(55.55-73.12) 0.627 0.500-0.787 <0.0001 unknown 60(4.42) 12.66(7.16-22.36) 1.580 1.157-2.157 0.0040 Education High school, higher vocational school, or technical school 260(19.15) 63.92(55.16-74.08) 1.000 Junior high school or below 854(62.89) 42.44(38.33-47.00) 1.524 1.216-1.909 0.0003 College or above 196(14.43) 51.66(41.59-64.18) 1.351 1.018-1.794 0.0372 unknown 48 (3.53) 32.25(24.84-41.87) 0.552 0.285-1.071 0.0789 Occupation Officer 204(15.02) 37.07(32.12-42.79) 1.000 farmer 442(32.55) 41.97(35.92-49.03) 1.892 1.297-2.761 0.0009 retiree 444(32.70) 42.38(36.64-49.03) 1.353 0.931-1.966 0.1130 worker 125(9.20) 49.12(40.30-59.87) 1.253 0.823-1.909 0.2931 other 143(10.53) 19.69(15.21-25.49) 2.125 1.380-3.273 0.0006 Type Hodgkin 55(4.05) 82.26(76.09-88.94) 1.000 Non-hodgkin 1043(76.80) 48.26(43.97-52.96) 1.983 1.201-3.274 0.0100 Immunoproliferative disease 8(0.59) 29.49(20.11-43.25) 1.621 0.535-4.906 0.3900 Multiple myeloma 252(18.56) 37.96(30.76-46.85) 2.233 1.329-3.752 <0.0001 Year of Diagnose 2016-2020 774(57.00) 52.63(48.08-57.61) 1.000 2011-2015 584(43.00) 42.59(36.62-49.54) 1.442 1.237-1.681 <0.0001 Abbreviations: ARS, age-standardized relative survival rate; CI, confidence interval. HR, hazard ratio. Note: *5-year relative survival time. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 28 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7966695","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549248034,"identity":"c7fdb302-e3aa-433e-99bb-dbe20f44e809","order_by":0,"name":"Wenting Luo","email":"","orcid":"","institution":"First Affiliated Hospital of Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Wenting","middleName":"","lastName":"Luo","suffix":""},{"id":549248035,"identity":"2e3b3bb3-4b93-45f7-9c04-7c3bab16e9f1","order_by":1,"name":"Yilan Lin","email":"","orcid":"","institution":"Xiamen Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yilan","middleName":"","lastName":"Lin","suffix":""},{"id":549248036,"identity":"4903bc7e-2429-4594-a51c-8db0091f436e","order_by":2,"name":"Xingyu Li","email":"","orcid":"","institution":"Zhongshan Hospital of Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Li","suffix":""},{"id":549248037,"identity":"d55c56c1-622a-4a02-9009-4e3100e1bcf9","order_by":3,"name":"Jiahuang Chi","email":"","orcid":"","institution":"Xiamen Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jiahuang","middleName":"","lastName":"Chi","suffix":""},{"id":549248038,"identity":"81b85a8a-fbf2-44df-82bb-bda3f86b9b1c","order_by":4,"name":"Yanxin Luo","email":"","orcid":"","institution":"First Affiliated Hospital of Xiamen 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1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized incidence (ASIR) and mortality (ASMR) trends of lymphoma in Xiamen, 2011–2020, stratified by sex (male vs. female) and residence (urban vs. rural). Rates are standardized to the Segi world standard population.\u003c/p\u003e","description":"","filename":"placeholderimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7966695/v1/7a522ddac9837feae2dc384b.png"},{"id":96787279,"identity":"fc94c4c7-7041-42bb-81a5-c9bd1ca9c2ba","added_by":"auto","created_at":"2025-11-26 06:24:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eFive-year age-standardized relative survival (ARS) of lymphoma patients in Xiamen, 2011–2020, stratified by sex (male vs. female) and residence (urban vs. rural). Survival was estimated using the Ederer II method, with log-rank tests comparing groups.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-7966695/v1/1f1a0b9a945728b8fec08a70.png"},{"id":96922510,"identity":"46a84871-8672-426f-8e9c-ae84d00467e5","added_by":"auto","created_at":"2025-11-27 14:19:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7966695/v1/b7d67bfd-3b40-4cd4-ab31-ff8bf7ecff06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban–Rural and Socioeconomic Disparities in Lymphoma Outcomes: Evidence from a Population-Based Study in Xiamen, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLymphoma is a heterogeneous group of hematological malignancies with substantial variation in incidence, survival, and mortality across regions and populations\u003csup\u003e1\u003c/sup\u003e. According to GLOBOCAN 2020, the age-standardized incidence rate (ASIR) of non-Hodgkin lymphoma (NHL) in China was 6.7 per 100,000, with an estimated 92,834 new cases and 54,351 deaths\u003csup\u003e2\u003c/sup\u003e. Both Hodgkin lymphoma (HL) and NHL contribute significantly to the global cancer burden, though their patterns differ by geography, age, and socioeconomic context\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn China, the incidence of lymphoma has been increasing steadily over the past two decades\u003csup\u003e3,4\u003c/sup\u003e. National cancer registry reports have documented rising trends and changing age distributions, with a growing proportion of cases diagnosed at younger ages\u003csup\u003e3\u003c/sup\u003e. Despite these increases, survival outcomes remain poorer than those observed in high-income countries\u003csup\u003e2\u003c/sup\u003e. For example, 5-year relative survival for NHL in China is below 50%, compared with 60\u0026ndash;70% in Europe and North America\u003csup\u003e5\u003c/sup\u003e. These gaps highlight differences in early diagnosis, access to care, and health system capacity.\u003c/p\u003e\u003cp\u003eMost prior Chinese studies have described national or provincial patterns\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. However, regional evidence remains limited, and important questions persist regarding how socioeconomic status, education, and marital status influence survival. Urban\u0026ndash;rural disparities are of particular concern, as prior registry studies suggest that rural residents may face delayed diagnosis, limited access to specialized care, and poorer outcomes\u003csup\u003e9\u003c/sup\u003e. Yet systematic analyses incorporating these factors are still scarce.\u003c/p\u003e\u003cp\u003eXiamen, a rapidly developing coastal city in southeastern China, provides a unique context for studying these disparities. The city is characterized by distinct urban\u0026ndash;rural contrasts and a high-quality cancer registry with internationally acceptable data quality indicators (MV% \u0026gt;98%, DCO% ~1%). This setting enables a robust evaluation of lymphoma burden and outcomes while also reflecting the broader challenges of balancing cancer control across regions undergoing rapid socioeconomic transition.\u003c/p\u003e\u003cp\u003eTherefore, using data from the Xiamen Cancer Registry between 2011 and 2020, we aimed to: (i) describe incidence and mortality patterns of lymphoma; (ii) evaluate survival outcomes by demographic and socioeconomic subgroups; and (iii) identify independent prognostic factors using multivariable models. By situating regional findings within the national and international context, this study provides evidence on health inequities that can inform cancer control strategies in China and contribute to the global discussion on disparities in hematologic malignancies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source and case definition\u003c/h2\u003e\u003cp\u003eData were obtained from the Xiamen Cancer Registry, a population-based registry that covers the entire permanent resident population of Xiamen (approximately 4.3\u0026nbsp;million). All incident cases of lymphoma diagnosed between January 1, 2011, and December 31, 2020 were included. Cases were identified according to the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) and were grouped into Hodgkin lymphoma (HL, C81) and non-Hodgkin lymphoma (NHL, C82\u0026ndash;C85, C96), with multiple myeloma (C90) excluded from the present analysis. Only first primary cases were included.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData quality control\u003c/h3\u003e\n\u003cp\u003eThe registry follows the standards of the Chinese National Central Cancer Registry (NCCR) and the International Agency for Research on Cancer (IARC). Quality indicators were assessed annually, including the proportion of morphologically verified cases (MV%), the proportion of cases identified through death certificate only (DCO%), and the mortality-to-incidence ratio (M/I). For the study period, MV% exceeded 98%, DCO% was approximately 1%, and M/I ranged from 0.45 to 0.55, indicating high-quality and reliable registry data.\u003c/p\u003e\n\u003ch3\u003eIncidence and mortality calculation\u003c/h3\u003e\n\u003cp\u003eCrude incidence and mortality rates were calculated per 100,000 person-years. Age-standardized rates were computed using the direct method, with the Segi world standard population as the reference. Rates were reported overall and stratified by sex and urban\u0026ndash;rural residence.\u003c/p\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003ePatient survival was assessed using both observed survival (OS) and relative survival (RS), with the latter defined as the ratio of observed to expected survival. Expected survival was derived using the Ederer II method with national life tables. Age-standardized relative survival (ARS) was calculated using the International Cancer Survival Standard (ICSS) weights to facilitate comparability across populations. Kaplan\u0026ndash;Meier curves were used to visualize survival differences across subgroups, and differences were tested with the log-rank method.\u003c/p\u003e\n\u003ch3\u003ePrognostic factor analysis\u003c/h3\u003e\n\u003cp\u003eCox proportional hazards models were fitted to identify independent prognostic factors, including sex, age group, residence (urban vs. rural), education level, marital status, histological subtype, and diagnosis period (2011\u0026ndash;2015 vs. 2016\u0026ndash;2020). Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. The proportional hazards assumption was assessed using Schoenfeld residuals and found to be satisfied.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eTrend analysis\u003c/h2\u003e\u003cp\u003eTime trends in incidence, mortality, and survival were evaluated using Joinpoint regression analysis (Joinpoint software, version 4.9.1.0, US National Cancer Institute). Annual percent change (APC) and average annual percent change (AAPC) with 95% CIs were estimated. Statistical significance of trends was assessed using the Monte Carlo permutation method. When Joinpoint analysis was not feasible due to limited data points, Cochran\u0026ndash;Armitage tests for trend were applied.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R software (version 4.2.2, R Foundation for Statistical Computing) and Joinpoint regression program (version 4.9.1.0). A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIncidence trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncidence trends by sex and region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2011 to 2020, a total of 1,436 new cases of lymphoma were recorded in Xiamen. The crude incidence rate was 6.64 per 100,000, and the age-standardized incidence rate (ASIR) was 5.36 per 100,000 (\u003cstrong\u003eTable 1\u003c/strong\u003e). Incidence was consistently higher in men than women (ASIR: 6.44 vs. 4.34 per 100,000) and in urban compared with rural residents (ASIR: 5.99 vs. 4.05 per 100,000).\u003c/p\u003e\n\u003cp\u003eTrend analysis revealed that, overall, incidence rates remained relatively stable during the study period. However, in rural areas, both the crude incidence and ASIR showed significant upward trends. The crude rate increased with an average annual percent change (AAPC) of 5.58% (95% CI: 2.20\u0026ndash;9.08, p = 0.005), and the ASIR increased at 4.72% annually (95% CI: 1.66\u0026ndash;7.87, p = 0.007). By contrast, no significant temporal changes were detected among males, females, or urban residents. These findings highlight the growing burden of lymphoma in rural populations and are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e and illustrated in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality trends by sex and region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy September 30, 2023, a total of 762 lymphoma patients had died, including 646 deaths directly attributed to lymphoma and 116 from other causes. The crude mortality rate was 3.53 per 100,000, and the age-standardized mortality rate (ASMR) was 2.80 per 100,000 (\u003cstrong\u003eTable 1\u003c/strong\u003e). Mortality was higher in men than women (ASMR: 3.57 vs. 2.10 per 100,000) and in urban compared with rural residents (ASMR: 3.14 vs. 2.10 per 100,000).\u003c/p\u003e\n\u003cp\u003eMortality trends displayed a biphasic pattern. From 2011 to 2014, crude mortality rose significantly (APC = 16.33%, 95% CI: 4.78\u0026ndash;29.16), and ASMR showed a parallel increase (APC = 15.33%, 95% CI: 2.48\u0026ndash;29.80). However, from 2014 to 2020, both crude mortality and ASMR declined significantly (crude APC = \u0026ndash;4.80%, 95% CI: \u0026ndash;8.11 to \u0026ndash;1.38; ASMR APC = \u0026ndash;6.68%, 95% CI: \u0026ndash;10.34 to \u0026ndash;2.88). Among men, mortality decreased steadily after 2014, with ASMR falling by 10.74% annually (95% CI: \u0026ndash;16.86 to \u0026ndash;4.17). In urban areas, crude mortality first increased (APC = 15.91%, 95% CI: 0.83\u0026ndash;33.24, 2011\u0026ndash;2014) and then decreased significantly thereafter (APC = \u0026ndash;5.89%, 95% CI: \u0026ndash;10.22 to \u0026ndash;1.35, 2014\u0026ndash;2020). No statistically significant temporal change was observed in rural residents. Mortality patterns are detailed in \u003cstrong\u003eTable 1\u003c/strong\u003e and depicted in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the 5-year observed survival (OS), relative survival (RS), and age-standardized relative survival (ARS) were 49.38% (95% CI: 46.28\u0026ndash;52.40), 52.94% (95% CI: 49.62\u0026ndash;56.18), and 48.98% (95% CI: 45.35\u0026ndash;52.91), respectively (\u003cstrong\u003eTable 1\u003c/strong\u003e). Females had higher survival than males (5-year ARS: 52.40% vs. 47.09%), and urban residents had better outcomes than rural residents (50.64% vs. 41.44%).\u003c/p\u003e\n\u003cp\u003eWhen stratified by subtype, Hodgkin lymphoma patients had the most favorable prognosis (5-year ARS: 82.26%, 95% CI: 76.09\u0026ndash;88.94), while outcomes were poorer for non-Hodgkin lymphoma (48.26%), multiple myeloma (37.96%), and immunoproliferative disorders (29.49%) (\u003cstrong\u003eTable 2\u003c/strong\u003e). Survival also varied by sociodemographic characteristics. Separated, divorced, or widowed individuals had the highest survival (63.92%) compared with married (47.96%) and single patients (42.30%). Educational attainment was strongly associated with outcome: patients with at least high school or technical education had markedly better survival (63.92%) than those with junior high school or less (42.44%).\u003c/p\u003e\n\u003cp\u003eAge was a critical determinant of prognosis. Patients aged 0\u0026ndash;44 years had a 5-year ARS of 73.98%, compared with only 28.69% in those aged\u0026nbsp;\u0026ge;75 years (\u003cstrong\u003eTable 2\u003c/strong\u003e). Female patients maintained a survival advantage across nearly all age groups, except in the 45\u0026ndash;54 age category. Kaplan\u0026ndash;Meier survival curves (\u003cstrong\u003eFigure 2\u003c/strong\u003e) demonstrate these disparities by sex and residence.\u003c/p\u003e\n\u003cp\u003eTemporal analysis demonstrated clear improvements in survival over the study period (\u003cstrong\u003eTable 3\u003c/strong\u003e). The overall 5-year ARS increased from 42.6% in 2011\u0026ndash;2015 to 52.6% in 2016\u0026ndash;2020. Male patients showed significant improvements, with an AAPC of 9.99% (95% CI: 4.74\u0026ndash;15.51, p = 0.003). Urban residents also experienced gains between 2015 and 2020 (APC = 9.83%, 95% CI: 1.15\u0026ndash;14.99, p = 0.033). However, no significant temporal improvements were detected among rural residents. Detailed results for 1-, 3-, and 5-year ARS by sex and residence are presented in \u003cstrong\u003eTable 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic factors (Cox regression analysis)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,358 patients were included in the multivariable Cox regression analysis after excluding 15 death certificate\u0026ndash;only cases and 64 with multiple primaries. Six independent prognostic factors were identified: age, residence, marital status, education, lymphoma subtype, and diagnosis period (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOlder age was strongly associated with increased mortality. Patients\u0026nbsp;\u0026ge;75 years had more than a five-fold higher risk of death compared with those aged 0\u0026ndash;44 years (HR = 5.06, 95% CI: 3.51\u0026ndash;7.28, p \u0026lt; 0.001). Rural residence conferred a survival disadvantage relative to urban residence (HR = 1.25, 95% CI: 1.02\u0026ndash;1.53, p = 0.028). Education level demonstrated a U-shaped association: both\u0026nbsp;\u0026le;junior high (HR = 1.52, 95% CI: 1.22\u0026ndash;1.91, p = 0.0003) and\u0026nbsp;\u0026ge;college (HR = 1.35, 95% CI: 1.02\u0026ndash;1.79, p = 0.037) were associated with poorer survival compared with the reference group of high school/technical education.\u003c/p\u003e\n\u003cp\u003eSignificant improvements over time were confirmed: patients diagnosed between 2011\u0026ndash;2015 had a higher risk of death than those diagnosed in 2016\u0026ndash;2020 (HR = 1.44, 95% CI: 1.24\u0026ndash;1.68, p \u0026lt; 0.001). Lymphoma subtype was also predictive: compared with Hodgkin lymphoma, non-Hodgkin lymphoma (HR = 1.98, 95% CI: 1.20\u0026ndash;3.27, p = 0.010) and multiple myeloma (HR = 2.23, 95% CI: 1.33\u0026ndash;3.75, p \u0026lt; 0.001) were associated with substantially higher mortality (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis population-based study provides a comprehensive overview of lymphoma epidemiology in Xiamen, southeastern China, over a ten-year period. By integrating high-quality registry data with sociodemographic information, we identified clear disparities in incidence, mortality, and survival across sex, residence, and socioeconomic factors. Several important findings emerge from this analysis.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIncidence and mortality trends\u003c/h2\u003e\u003cp\u003eConsistent with national registry reports\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e and international observations\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e, the incidence and mortality of lymphoma were higher in men than in women. Biological differences, such as sex-specific immune responses and hormonal influences, have been proposed as possible explanations for these patterns\u003csup\u003e16,17\u003c/sup\u003e. In addition, differences in health-seeking behavior and comorbidity burden may contribute to poorer outcomes among men\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUrban\u0026ndash;rural disparities were also evident. Incidence rates were higher in urban areas, likely reflecting both greater diagnostic capacity and lifestyle-related risk factors associated with urbanization. Conversely, mortality declined more markedly in urban than rural populations, underscoring the persistent inequities in healthcare access. Similar patterns have been reported in other Chinese provinces\u003csup\u003e11,19\u003c/sup\u003e and in global cancer surveillance studies\u003csup\u003e9,15\u003c/sup\u003e. Rural patients may experience delayed diagnosis and reduced access to hematology specialists, advanced therapies, or standardized treatment protocols, leading to poorer survival outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSurvival outcomes\u003c/h2\u003e\u003cp\u003eThe 5-year ARS of 49% observed in this study is broadly consistent with other Chinese registries\u003csup\u003e6\u003c/sup\u003e but lower than survival reported in high-income countries, where NHL survival often exceeds 60\u0026ndash;70%\u003csup\u003e5\u003c/sup\u003e. EUROCARE studies in Europe and SEER data from the United States have consistently documented higher survival, particularly for HL, which now approaches 80\u0026ndash;85% at 5 years in many Western countries\u003csup\u003e5\u003c/sup\u003e. The survival gap highlights ongoing challenges in China, including limitations in early detection, treatment access, and follow-up care. Our findings also reaffirm the modest but consistent female survival advantage, a pattern seen internationally\u003csup\u003e18\u003c/sup\u003e, possibly related to differences in tumor biology, comorbidities, or treatment adherence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSocioeconomic disparities\u003c/h2\u003e\u003cp\u003eEducation level and marital status emerged as significant predictors of survival. Patients with lower education experienced poorer outcomes, which may reflect reduced health literacy, financial constraints, and barriers to accessing timely care. Similar associations between education and lymphoma survival have been observed in both Asian and Western settings\u003csup\u003e20\u0026ndash;22\u003c/sup\u003e. Interestingly, widowed or separated patients showed better survival in our analysis. This result contradicts prior evidence suggesting worse outcomes in unmarried individuals\u003csup\u003e19\u003c/sup\u003e. The discrepancy likely reflects residual confounding or misclassification within registry data and thus should be interpreted cautiously.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTemporal improvements\u003c/h2\u003e\u003cp\u003eEncouragingly, survival improved over the study period, with 5-year ARS rising from 42.6% in 2011\u0026ndash;2015 to 52.6% in 2016\u0026ndash;2020. These gains likely reflect advances in diagnostic techniques, broader adoption of immunohistochemistry and molecular diagnostics, and improved availability of chemotherapy and immunotherapy agents. Policy initiatives aimed at strengthening cancer care capacity in China may also have contributed. Nonetheless, survival remains below that reported in developed countries, indicating that additional efforts are required to close the gap.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe strengths of this study include use of a population-based registry with internationally acceptable data quality indicators (MV% \u0026gt;98%, DCO% ~1%), large sample size, and long-term follow-up. However, several limitations must be acknowledged. First, clinical information such as stage, treatment regimens, and comorbidities was unavailable, precluding adjustment for these important prognostic factors. Second, estimates for rare subtypes such as immunoproliferative diseases are unstable and should be interpreted with caution. Third, socioeconomic information was limited to education and marital status; more granular measures such as income or occupation were not available. Finally, as a single-city study, the findings may not fully represent other regions in China, although the patterns observed align with national trends.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eOur results highlight the importance of addressing disparities in lymphoma outcomes from both national and international perspectives. Within China, targeted interventions are needed to improve access to early diagnosis and standardized treatment in rural populations, as well as to address socioeconomic barriers that affect patient outcomes. Globally, these findings contribute to the evidence base on health inequities in hematologic malignancies, aligning with World Health Organization (WHO) calls to integrate equity considerations into cancer control planning. Continued strengthening of cancer registries, combined with expanded clinical data linkages, will be essential to monitor progress and guide resource allocation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study demonstrates gender, urban\u0026ndash;rural, and socioeconomic disparities in lymphoma incidence, mortality, and survival in Xiamen. Although survival has improved over time, it remains lower than in high-income countries. Future studies should incorporate detailed clinical and treatment information to clarify underlying mechanisms. Our findings are consistent with global trends but highlight unique disparities in China, underscoring the need for both national and international strategies to reduce inequalities in lymphoma outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDisclosure\u003c/h2\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003ch2\u003eEthical approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Xiamen City Center for Disease Control and Prevention (XJK/LLSC (2023)004).The need of informed consent was waived by the ethics committee of Xiamen City Center due to retrospective and anonymous study design, All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work is supported by the Xiamen Medical and Health Guidance Project (3502Z20224ZD1014), the Natural Scientific Foundation of Xiamen (No. 3502Z20227340), the Fujian Natural Science Foundation of China (No. 2022J011372) and the Fujian provincial health technology project (No.2022RBK016).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eThe study concept and design were developed by YL and XZ. Data collection was carried out by JC and YL. WL and XZ conducted the statistical analysis and drafted the manuscript. XL participated in the discussions and revisions. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe are grateful to all the subjects for their participation.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThandra, K. C. \u003cem\u003eet al.\u003c/em\u003e Epidemiology of Non-Hodgkin\u0026rsquo;s Lymphoma. \u003cem\u003eMed. Sci. 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Lymphoma\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 1656\u0026ndash;1667 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. The incidence and mortality of lymphoma in Xiamen City from 2011 to \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCrude Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003eAge Standardized Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eRate\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAAPC(95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eRate\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eAAPC(95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.07(-1.07, 3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.27(-3.08, 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5.92(-1.64,14.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.29(-4.00, 13.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuburb:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.37(-1.26, 4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.39(-3.41, 2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5.58\u003csup\u003e*\u003c/sup\u003e(2.20, 9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.72\u003csup\u003e*\u003c/sup\u003e(1.66, 7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.59\u003csup\u003e*\u003c/sup\u003e(0.43, 4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.14(-1.33, 3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.33\u003csup\u003e#\u003c/sup\u003e(-4.26, 5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e-2.76\u003csup\u003e#\u003c/sup\u003e(-7.07, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e3.63(-4.06, 1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.02(-3.57, 10.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuburb:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.88\u003csup\u003e#\u003c/sup\u003e(-3.34, 5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e-1.35(-6.48, 4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.20(-4.44, 9.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.27(-10.16,18.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.78\u003csup\u003e#\u003c/sup\u003e(-1.44, 5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.15\u003csup\u003e#\u003c/sup\u003e(-3.42, 3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eNote: *P values \u0026lt;0.05, the others P values of AAPC\u0026gt;0.05. \u0026nbsp;#P value of the annual percentage change (APC) \u0026lt;0.05: CR mortality in male APC=-6.52 (95%CI:[-11.22, -1.57]) from 2014 to 2020, \u0026nbsp;in urban APC=15.91 (95%CI:[0.83, 33.24]) from 2011 to 2014 and APC=-5.89 (95%CI:[-10.22, -1.35]) from 2014 to 2020, \u0026nbsp;in total APC=16.33 (95%CI:[4.78, 29.16]) from 2011 to 2014 and APC=-4.80 (95%CI:[-8.11, -1.38]) from 2014 to 2020; ASR mortality in male APC=-10.74 (95%CI:[-16.86, -4.17]) from 2015 to 2020, in total APC=15.33 (95%CI:[2.48, 29.80]) from 2011 to 2014 and APC=-6.68(95%CI:[-10.34, -2.88]) from 2014 to 2020.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The 5-year survival rate of lymphoma in Xiamen City from 2011 to 2020.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eObserved Survival Rate (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eRelative Survival Rate (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003eAge Standardized Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eRate(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAAPC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e47.10(43.02-51.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e51.45(47.00-55.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e47.09(42.50-52.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e9.99\u003csup\u003e*\u003c/sup\u003e(4.74, 15.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e52.51(47.72-57.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e54.95(49.93-59.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e52.40(46.83-58.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.12(-11.12, 15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuburb:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e49.42(45.83-52.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e53.18(49.31-56.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e50.64(46.54-55.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.47\u003csup\u003e#\u003c/sup\u003e(-6.16, 9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e49.30(43.08-55.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e52.02(45.46-58.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e41.44(34.30-50.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.36(-5.86, 22.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e49.38(46.28-52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e52.94(49.62-56.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e48.98(45.35-52.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.73(-4.00, 9.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AAPC, average annual percent change; CI, confidence interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNote: *\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalues \u0026lt;0.05, the others \u003cem\u003eP\u0026nbsp;\u003c/em\u003evalues of AAPC\u0026gt;0.05. \u0026nbsp;#The APC (annual percentage change) from 2015 to 2020 was 9.83 (95%CI:1.15-14.99, \u003cem\u003eP\u003c/em\u003e=0.033), there was no join point in the other groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. The age-standardized relative survival rates of lymphoma in different year in Xiamen city from 2013 to 2020\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003esurvival year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAAPC\u003csup\u003e*\u003c/sup\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e86.52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e67.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e73.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e78.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e80.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e80.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e87.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e79.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.18(-1.96, 4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e61.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e41.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e51.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e57.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e60.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e62.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e63.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e67.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4.13(-0.76, 9.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e61.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e34.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e45.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e53.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e49.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e52.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e57.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e56.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.73(-4.00, 9.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AAPC, average annual percent change; CI, confidence interval. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNote: * P values \u0026gt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003eResults of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emultivariate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cox regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eTotal (n=1358, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5-year ARS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e95%CI of HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years old)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e0-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e275(20.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e73.98(67.78-79.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e188(13.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e58.24(49.87-65.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.083-2.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0161\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e326(24.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e51.98(45.33-58.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n 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15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e583(42.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e52.40(46.83-58.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e775(57.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e47.09(42.50-52.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.933-1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.2836\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuburb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e41.44(34.30-50.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.024-1.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0283\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1013(74.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e47.96(43.69-52.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e82(6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e42.30(36.22-49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.566-1.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.7722\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSeparated/ divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e203(14.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e63.73(55.55-73.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.500-0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e60(4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e12.66(7.16-22.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.157-2.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0040\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHigh school, higher vocational school, or technical school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e260(19.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e63.92(55.16-74.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e854(62.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e42.44(38.33-47.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.216-1.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e196(14.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e51.66(41.59-64.18)\u003c/p\u003e\n \u003c/td\u003e\n 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14px;\"\u003e\n \u003cp\u003e444(32.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e42.38(36.64-49.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.931-1.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.1130\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eworker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e125(9.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e49.12(40.30-59.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.823-1.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.2931\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e143(10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e19.69(15.21-25.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.380-3.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHodgkin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e55(4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e82.26(76.09-88.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eNon-hodgkin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1043(76.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e48.26(43.97-52.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.201-3.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.0100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eImmunoproliferative disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8(0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e29.49(20.11-43.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.535-4.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.3900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eMultiple myeloma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e252(18.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e37.96(30.76-46.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.329-3.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of Diagnose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e2016-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e774(57.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e52.63(48.08-57.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e2011-2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e584(43.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e42.59(36.62-49.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.237-1.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Abbreviations: ARS, age-standardized relative survival rate; CI, confidence interval. HR, hazard ratio. Note: *5-year relative survival time.\u003c/p\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":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"lymphoma, incidence, mortality, survival, epidemiology, prognostic factors","lastPublishedDoi":"10.21203/rs.3.rs-7966695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7966695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRegional data on lymphoma in China remain limited. We analyzed incidence, mortality, and survival trends in Xiamen from 2011\u0026ndash;2020 to identify demographic and socioeconomic factors associated with outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAll newly diagnosed lymphoma cases (ICD-10 C81\u0026ndash;C86, C96) were retrieved from the Xiamen Cancer Registry. Age-standardized incidence and mortality rates (ASIR, ASMR) were calculated using Segi\u0026rsquo;s world standard population. Survival was assessed using observed survival, relative survival, and age-standardized relative survival. Cox regression identified independent prognostic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBetween 2011 and 2020, 1,436 lymphoma cases were recorded. The ASIR was higher in males than females (6.44 vs. 4.34 per 100,000) and in urban versus rural residents (5.99 vs. 4.05 per 100,000). Mortality was also elevated in males (ASMR 3.57 vs. 2.10 per 100,000) and urban residents (3.14 vs. 2.10 per 100,000). Five-year age-standardized relative survival was 48.98% overall, higher in females than males (52.40% vs. 47.09%) and in urban than rural residents (50.64% vs. 41.44%). Multivariable Cox regression identified older age, rural residence, marital status, education, lymphoma subtype, and earlier diagnosis period (2011\u0026ndash;2015) as independent predictors of poorer prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLymphoma incidence and mortality in Xiamen reflect gender and regional disparities, while survival outcomes are strongly influenced by demographic and socioeconomic factors. These findings underscore the need for targeted cancer control strategies addressing urban\u0026ndash;rural inequities and socioeconomic barriers.\u003c/p\u003e","manuscriptTitle":"Urban–Rural and Socioeconomic Disparities in Lymphoma Outcomes: Evidence from a Population-Based Study in Xiamen, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:24:53","doi":"10.21203/rs.3.rs-7966695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-27T17:35:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219819225878767440362392840770836254553","date":"2025-12-17T19:45:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T21:08:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T11:33:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T11:30:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2025-10-28T07:45:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"59a45312-7bac-44a5-8550-c16f5420dca7","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T06:24:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:24:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7966695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7966695","identity":"rs-7966695","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}