Disparities in Treatment and Expenditures among Lung Cancer Patients under Tiered Social Health Insurance: A Population-Based Study in China

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The degree of cancer care inequalities under tiered SHI in China and other low- and middle-income countries (LMICs) remain poorly understood. Methods Out of 319,677 patients diagnosed with lung cancer between 2017 and 2021 in Shandong, we established propensity score-matched samples under the Urban and Rural Resident Basic Medical Insurance (URRBMI) and those under the Urban Employee Basic Medical Insurance (UEBMI). We ran multivariable regressions to assess the effects of SHI schemes on cancer treatment and expenditures. Subgroup analyses of cancer treatment were conducted based on whether the cancer had metastasized. Results In the matched samples, utilization of cancer care increased under both schemes from 2017 to 2021. Higher proportions of cancer care use were seen in those under UEBMI compared those under URRBMI consistently with statistical significance. UEBMI was associated with a higher probability of receiving surgery in patients without metastasis, and higher probabilities of receiving radiotherapy or chemotherapy, targeted therapy, and immunotherapy in patients with metastasis. Patients under UEBMI were also less likely to be discharged against medical advice than those under URRBMI. Furthermore, UEBMI beneficiaries had 13.3% higher total expenditures but 19.1% lower out-of-pocket expenditures. Conclusions Significant gaps remained in access to and financial protection for lung cancer, particularly in surgery for non-metastatic cancer. Targeted harmonization of benefit packages is needed to address pressing disparities in cancer care in LMICs with tiered SHI. Lung cancer disparity treatment expenditures social health insurance Figures Figure 1 Figure 2 1. Introduction Risk pooling for health care, frequently established through social health insurance (SHI), is expected to enable patients to access essential health services when needed without financial hardship 1 , 2 . For diseases associated with high expenditures like cancer, unequal SHI coverage could make substantial differences in both access to quality services and financial vulnerability 3 , 4 . In health systems with tiered pools of SHI, which are common in low- and middle-income countries (LMICs) 5 , 6 , disparity of benefit package or copayment rates means potentially significant disparities of cancer care and financial protection 7 – 11 . In China, approximately 95% of the population was covered by two main types of SHI in 2023 12 . The Urban Employee Basic Medical Insurance (UEBMI) funded through premiums contributed by employers and individuals covers employees and retirees in the formal sector, who constitute about 28% of all SHI beneficiaries 13 . The other main scheme was the Urban and Rural Resident Basic Medical Insurance (URRBMI) 13 . UEBMI and URRBMI share the same benefit package. However, the former is more generous than the latter in reimbursement, though the gap in-between has been narrowing. In 2023, the average reimbursement rates for inpatient expenses under UEBMI and URRBMI were 84.6% and 68.1%, respectively 12 . Understanding the disparities in cancer care between schemes may provide important insights to narrow the benefit gaps in the context of tiered SHI 5 , 14 . A literature review from the United States found that cancer patients with no or inferior insurance coverage had lower utilization of high-cost treatments and systemic treatments, and higher chances of treatment delays 15 . In the case of lung cancer, despite recent advancement in treatment technology for patients with this condition, international studies consistently demonstrated significant disparities in cancer care and economic burden across health insurance schemes 7 – 11 , 16 . While the majority of current studies on the disparity between private and public health insurance, few studied the effects of tiered SHI on disparity in cancer care in China and other LMICs. Lung cancer, which has the highest incidence and mortality rates among all malignant tumors in China 17 , causes a substantial medical and economic burden on both patients and society 18 , 19 . However, little is known about the inequalities regarding lung cancer care and expenditure caused by the tiered SHI in China. To fill this knowledge gap, we sought investigating the disparities of lung cancer treatment and related expenditures between URRBMI and UEBMI beneficiaries in Shandong, one of the most populous provinces in China. 2. Materials and methods 2.1 Data source and study population Shandong is a coastal province in eastern China. It had a population of approximately 101.2 million with level of economic development equivalent to a upper-middle income country 20 . We used standardized hospital discharge data, formally known as hospitalization record front pages (HRFPs) from all secondary and tertiary hospitals in Shandong stored in the Cheeloo Lifespan Electronic Health Research Data-library (Cheeloo LEAD) (see Methodological Appendix Part 1 for details). HRFPs cover basic socio-demographics of patients, detailed information about disease diagnosis, treatment, and expenditure. Data on the platform were deidentified, with data for an individual linked via a unique encrypted identity number. We selected patients who were diagnosed with lung cancer (ICD-10: C34) and under URRBMI or UEBMI from January 1, 2017 to December 31, 2021. We set a window of at least four years to exclude patients with whom lung cancer had been previously diagnosed, by washing out repeated diagnoses emerging in the following years. The four-year wash-out period was adopted as the number of new cancer cases in 2021 remained stable when the time window was reset from four years (2017–2021) to eight years (2013–2021). Only cancer cases in individuals who received no cancer-specific diagnosis or treatment during the washout period were considered index cases 21 – 23 and thus included in our analysis (see Methodological Appendix Part 2 for details). We further excluded patients aged 100 years at diagnosis. Figure 1 summarizes how the sample was derived. We followed up all patients for lung cancer-specific hospitalizations until one year after the incidence hospitalization (see Supplementary Table S1 in details). As this study used pre-existing secondary data for analysis, informed consent was waived. This study was approved by the Ethics Committee for Public Health of Shandong University (LL20241105). We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines to ensure the reporting of this observational study 24 . [Please insert Fig. 1 here.] 2.2 Health insurance status and covariates Health insurance status was categorized as URRBMI and UEBMI, based on the insurance type recorded in HRFP. For the fewer than 10% of individuals who showed changes in scheme enrolment, we used the one that covered the bigger share of their hospitalization counts. Building on previous literature 25 , we included patient socio-demographics, clinical and healthcare provider characteristics as covariates. Socio-demographic characteristics included sex, age, marital status, ethnicity and occupation at diagnosis. Clinical characteristics included the histologic type of lung cancer, whether the cancer has metastasized, and non-cancer comorbidities. Patient comorbidities were assessed using the Charlson Comorbidity Index (CCI), as described by Deyo and colleagues (see Supplementary Table S2 for the score for each diagnostic code) 26 . We categorized patients into three groups based on non-cancer CCI score: 0, 1, and 2 or more. We specifically focused on non-cancer comorbidities to examine their impacts on the lung cancer care 27 . Meanwhile, we obtained information about the level of hospital (tertiary, secondary, and unclassified or other) from the official database of China’s National Health Commission ( https://zgcx.nhc.gov.cn ). The sites of hospitals were categorized into four regions based on proximity in geography and economic development. 2.3 Outcomes Our main outcome variables include cancer care and expenditures within one year after the index hospitalization, because the intensity of medical treatment and expenditures for cancer patients are significantly higher in the first year compared to the years afterwards 28 , 29 , and that in the first year after diagnosis has a substantial impact on the prognosis of lung cancer 30 , 31 . We examined a range of cancer care variables, including surgery, radiation therapy or chemotherapy, targeted therapy, and immunotherapy identified via a series of ICD-10 and ICD-9-CM3 codes 32 (see Supplementary Table S3), as well as discharge against medical advice (DAMA). DAMA refers to the practice where a patient chooses to leave the hospital against medical assessment based on the patient’s conditions 33 . It can lead to increased chances of morbidity and mortality 34 , so we included it as a key indicator of disparity in cancer care. Besides, we assessed expenditures, including total, out-of-pocket, surgical, drug, and diagnostic expenditures. The expenditures were adjusted to 2021 Chinese yuan . 2.4 Statistical analysis Categorical variables are reported as frequencies (%) while continuous variables are summarized as means with standard deviation (SD). Pearson's Chi-square test was used for assessing differences of categorical variables. To control for differences between beneficiaries of the two main categories of SHI, we established propensity score-matched samples (see Methodological Appendix Part 4). One-to-one propensity score matching (PSM) was performed using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score 35 . Logistic regression was used to calculate a propensity score, which evaluates confounding by indication and/or baseline covariates between two insurance groups. The matching variables used in the PSM models were year of diagnosis, age group, gender, race, marital status, and occupation. The primary analytical approach was multivariable regression analysis of the propensity score-matched sample (see Methodological Appendix Part 3 for details). We utilized multivariable logistic regressions to measure associations between health insurance status and the receipt of treatments, and conducted subgroup analyses based on whether the cancer had metastasized. We reported marginal effects, which are interpreted as average differences in the probability of receiving any type of treatment had a beneficiary of URRBMI been covered by UEBMI 36 . Then, we applied a generalized linear model (GLM) with a gamma distribution and log link function to estimate the difference in expenditure attributed to health insurance status. All models were adjusted for covariates mentioned above. We also incorporated the timing of cancer diagnosis by considering fixed effects of the year of diagnosis. Additionally, expenditures were log transformed after adding 1 to all values to allow for zeros. P values were 2-sided with P < 0.05 considered indicative of statistical significance. All statistical analyses were performed using R version 4.3.1. 3. Results 3.1 Patient characteristics and matching 319,677 patients were diagnosed with lung cancer between Jan 1, 2017, and Dec 31, 2021, of which 60.27% were insured by URRBMI and 30.54% were insured by UEBMI. The mean age of those covered by URRBMI was 65.5 (SD, 10.1) years, with males accounting for 58.7%. The mean age of UEBMI beneficiaries was 63.5 (SD, 11.6) years, with males accounting for 62.9%. Table 1 shows descriptive statistics for the unmatched and matched samples. Socio-demographic characteristics across the 2 matched groups were well balanced, with all standardized mean differences smaller than 0.1. After matching, 68.7% of UEBMI patients were diagnosed with NSCLC, and 12.3% had CCI ≥ 2, both higher than the 63.9% and 11.5% observed in URRBMI patients. Furthermore, UEBMI patients were more likely to receive treatment at tertiary hospitals (83.0% vs. 76.8%) and at hospitals located in the Central Region (40.3% vs. 36.4%). Notably, 22.1% of URRBMI patients had tumor metastasis, which was higher than the 18.2% occurred among UEBMI patients. [Please insert Table 1 here.] 3.2 Cancer treatment disparity after PSM Figure 2 demonstrates trends in cancer treatment among URRBMI and UEBMI beneficiaries diagnosed with lung cancer. From 2017 to 2021, there were increasing proportions of matched samples receiving surgery (UEBMI: 29.7% in 2017 to 54% in 2021; URRBMI: 24.6% in 2017 to 46.7% in 2021), targeted therapy (UEBMI: 4.7% in 2017 to 20.2% in 2021; URRBMI: 2.2% in 2017 to 19.5% in 2021), and immunotherapy (UEBMI: 2.9% in 2017 to 13.2% in 2021; URRBMI: 1.1% in 2017 to 12.8% in 2021), with the gradually decreasing difference between the two schemes. Meanwhile, the proportions of DAMA were decreasing among matched samples in both groups (UEBMI: 13.0% in 2017 to 7.8% in 2021; URRBMI: 15% in 2017 to 10.4% in 2021). [Please insert Figure 2 here.] Table 2 displays cancer-directed treatment and the average marginal effects of UEBMI beneficiaries over URRBMI beneficiaries on the likelihood of receiving cancer therapy for the matched sample. UEBMI beneficiaries were more likely, compared with URRBMI beneficiaries, to receive surgery (46% vs 34.7%), targeted therapy (11.5% vs 9.6%), and immunotherapy (6.4% vs 5.4%). According to the results of the multivariable logistic regression, UEBMI was associated with increased probabilities of receiving surgery (average marginal difference [AME]: 6.76%; 95% CI, 6.31% to 7.20%), targeted therapy (AME, 2.39%; 95% CI, 2.05% to 2.73%) and immunotherapy (AME, 1.26%; 95% CI, 1% to 1.53%). In contrast, UEBMI was associated with lower probabilities to experience chemotherapy or radiotherapy (AME, -0.55%; 95% CI, -1.09 to -0.02) and DAMA (AME, -1.56%; 95% CI, -1.92 to -1.20). [Please insert Table 2 here.] 3.3 Subgroups analysis of cancer treatment disparity Table 3 presents the cancer-directed treatment and the AMEs of UEBMI over URRBMI on the likelihood of receiving cancer therapy for matched subgroups, categorized by the presence or absence of metastasis. Distribution of the propensity scores for the unmatched and matched subgroups is provided in Supplementary Tables S4-5. In patients without metastasis, UEBMI were associated with a higher rate of surgery (AME, 8.04%; 95% CI, 7.52% to 8.57%) but a lower rate of chemotherapy or radiotherapy alone (AME, -1.40%; 95% CI, -1.98% to -0.82%). Among patients with metastasis, the AME of UEBMI was 1.13% (95% CI, 0.52% to 1.74%) for surgery, and 3.49% (95% CI, 2.27% to 4.71%) for chemotherapy or radiotherapy. Additionally, in both subgroups, UEBMI beneficiaries were consistently associated with higher likelihoods of receiving both targeted therapy and immunotherapy (particularly among the group with metastatic cancer), and a lower likelihood of DAMA (non-metastatic group: AME, -1.79%; 95% CI, -2.16% to -1.41%; metastatic group: AME, -1.15%; 95% CI, -2.14% to -0.16%). [Please insert Table 3 here.] 3.4 Disparity in expenditures after PSM Table 4 presents differences in expenditures across SHI schemes in the matched sample (see Supplementary Table S6 for further descriptive statistics about the unmatched and matched samples) and the additional expenditures as a proportion of expenditures under URRBMI associated with URRBMI after controlling for covariates. Patients under UEBMI had 13.34% higher total expenditures compared with those under URRBMI (95% CI, 13.08% to 15.47%). Specifically, surgical expenditures were 18.57% higher for UEBMI patients (95% CI, 17.68% to 23.19%), drug expenditures were 8.21% higher (95% CI, 6.87% to 10.26%), and diagnostic expenditures were 8.21% higher (95% CI, 6.90% to 10.25%). However, out-of-pocket expenditures for UEBMI patients was 19.09% lower than for URRBMI patients (95% CI, -18.39% to -16.36%). [Please insert Table 4 here.] 4. Discussion Taking advantage of population-wide discharge data from one of the most populous provinces in China, we analyzed the inequalities between URRBMI and UEBMI beneficiaries in lung cancer treatment and related expenditures. Using the PSM method to adjust for different patient characteristics across the two schemes, we observed an increase in utilization of treatment services for lung cancer patients in Shandong, China. However, significant inequalities remained in both cancer treatment and financial protection between URRBMI and UEBMI beneficiaries in China, with a notable disparity in surgical treatment, particularly among those without metastatic cancer. Our findings about the treatment inequalities between cancer patients under different SHI schemes in China, particularly in terms of surgery, are consistent with findings from multiple existing studies showing more generous insurance to be associated with higher rates of receiving cancer treatment (especially curative surgery) 37 – 40 . Crucially, we found the inequalities in surgery to be more pronounced among patients with non-metastatic lung cancer. Stokes et al 41 and Wakeam et al 42 also demonstrated that uninsured and Medicaid patients were less likely to receive surgery among patients with early-stage lung cancer compared with Medicare patients in the United States. Similar studies also identified discrepancies in delays of care and receipt of resection related to insurance within pancreatic, colorectal and hepatocellular cancers 43 – 45 . As early surgical intervention is associated with significantly longer survival for lung cancer 46 , the treatment inequalities in early-stage cancer likely translate into inequalities in survival. Meanwhile, the rapid rise of incidence cases and rates of surgery (nearly doubled during the study period) may also reflect potential over diagnosis of lung cancer and overuse of surgery. However, further analysis on this issue is beyond the scope of this study. The centralized procurement of innovative anticancer drugs by China’s National Healthcare Security Administration since 2018 led to an increasing number of receiving SHI reimbursement, which likely contributed to the rising proportion of people receiving targeted therapy or immunotherapy observed in our study. Particularly among patients with metastatic lung cancer, the main care advantage associated with UEBMI in comparison to URRBMI shifted to the utilization of radiotherapy, chemotherapy, targeted therapy and immunotherapy. Moreover, our study also found that URRBMI (compared with UEBMI) was associated with a higher rate of DAMA, which might result in rapid deterioration of the disease and shortened survival time 47 . Previous studies also found the type of health insurance might affect patients' treatment choices and the continuity of care they receive 48 . In stage IV non-small-cell lung cancer patients, Duma et al 49 observed Medicare and uninsured patients were more likely to refuse treatment compared to patients with private insurance. In our study, the persisted disparities in lung cancer care across SHI schemes after controlling for potential confounders, reveal potentially substantial unwarranted variations in cancer that could not be explained by illness severity or patient preference 14 , 50 . Besides factors on the demand-side, potential explanation from the provider perspectives is that physicians adjust their clinical management in response to patients’ insurance schemes 51 that provide differed financial incentives and constraints. In other words, the more generous reimbursement rates of UEBMI may encourage physicians to take more aggressive cancer care. In terms of expenditures, patients under UEBMI experienced better financial protection than those under URRBMI, which is consistent with previous research 52 , 53 . This economic advantage appears to translate into disparities in cancer healthcare, where UEBMI patients demonstrated greater access to cancer care than their URRBMI patients 5 . The heightened financial burden faced by URRBMI beneficiaries carries particular implications for vulnerable populations. As Mao et al. 54 demonstrated in their study, cancer patients over 60 years old faced a heavier financial burden, with high hospitalization costs potentially becoming a barrier for the elderly. Implications for policy and practice Several implications can be drawn for this study. First, the double inequalities in treatment and financial protection for lung cancer patients imply that inadequacy in cancer care for the URRBMI beneficiaries. Particularly for non-metastatic or early-stage lung cancer patients, it is important to consider narrowing the inequalities in reimbursement rates for surgery between URRBMI and UEBMI to enable URRBMI beneficiaries to afford necessary surgery at the right stage and to protect them from catastrophic expenditure. Indeed, policies on benefit packages and reimbursement rates may be further coordinated, so that incremental harmonization of SHI schemes prioritize raising reimbursement rates for good value cancer care. Second, the disparity in radiotherapy/chemotherapy, targeted therapy and immunotherapy in (particularly metastatic) lung cancer patients across SHI schemes in China should also raise discussions about standardization of cancer care and “value for money”. It is possible that some expenditures of UEBMI in the late-stage cancer care could be made to much better use in URRBMI for patients with an earlier stage cancer. Third, the fact that a much larger proportion of URRBMI patients receive care at the secondary hospitals than UEBMI patients suggest the importance of making sure quality are comparable and continuous across secondary and tertiary hospitals. Limitations Several limitations of our study warrant caution in interpretation. First, there is an absence of comprehensive staging information for lung cancer patients in our dataset. While this precluded a more nuanced analysis of how detailed stages of lung cancer affect outcomes across SHI schemes, our subgroup analysis stratified by whether the cancer was metastatic should have addressed a substantial part of the patient’s cancer stage upon diagnosis. Second, due to the lack of more socio-economic variables about participants, we were not able to distinguish the effects of SHI scheme policies from the patients’ health literacy 55 and attitudes towards surgery 56 , as well as social and family support 56 which would be more positive among the UEBMI beneficiaries as compared to those under URRBMI. Third, while our study did not delve into the quality or health outcome of treatment received, or whether the unwarranted disparity represents under- or over-treatment, these aspects present important avenues for future research. Fourth, given the mobility of patients, the HRFP would not capture hospitalizations outside Shandong Province, which means an underestimation of treatments and expenditures. However, due to the province's large population and well-developed healthcare resources, cancer patients often rely heavily on local care. Hence, this underestimation is likely to be small. Finally, the data were obtained from medical institutions in Shandong Province means our results are not directly generalizable to other provinces or countries. However, the significant disparities we found are likely to be observed elsewhere in China, as the tiered SHI exists nation-wide. 5. Conclusion While utilization for cancer treatment services improved in recent years for patients with lung cancer under both UEBMI and URRBMI in China. UEBMI coverage was associated with a substantially higher likelihood of receiving a range of cancer therapeutics compared to URRBMI coverage, especially surgery among patients without metastasis, and lower out-of-pocket expenditures. Such disparities in both treatment and financial protection for cancer reflect rooms for better harmonization between SHI schemes in health systems with tiered SHI pools. Abbreviations UEBMI Urban Employee Basic Medical Insurance URRBMI Urban and Rural Residents Basic Medical Insurance Declarations Funding This study was supported in part by the National Natural Science Foundation of China (71804004), National Natural Science Foundation of China (82330108), and Henan Science and Technology Major Program (241100310300). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Contribution YZ conceptualised the research ideas, interpreted the data, performed the statistical analysis and wrote the first draft. YH conceptualised the research ideas, interpreted the data and reviewed the first draft. QW contributed to data collection and data management. YM, XX & SL reviewed the first draft. XJ, QZ, YW &YZ contributed to data collection. CL & LZ provided critical revisions to the manuscript and contributed to the discussion of the findings. DW contributed to data collection, funding acquisition, and project administration. FX contributed to data collection, data management, funding acquisition, and project administration, and reviewed the final draft. JX conceptualised the research ideas, interpreted the data, performed the statistical analysis and reviewed the draft. All authors reviewed and approved the first draft. Data statement and availability The dataset is collected by Shandong University and is not publicly available. 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BMJ Open. 2020;10(10):e040437. 10.1136/bmjopen-2020-040437 . Mao W, Tang S, Zhu Y, Xie Z, Chen W. Financial burden of healthcare for cancer patients with social medical insurance: a multi-centered study in urban China. Int J Equity Health. 2017;16(1):180. 10.1186/s12939-017-0675-y . Nwagbara UI, Ginindza TG, Hlongwana KW. Lung cancer awareness and palliative care interventions implemented in low-and middle-income countries: a scoping review. BMC Public Health. 2020;20(1):1466. 10.1186/s12889-020-09561-0 . Gonzalez-Saenz de Tejada M, Bilbao A, Baré M, et al. Association between social support, functional status, and change in health-related quality of life and changes in anxiety and depression in colorectal cancer patients. Psychooncology. 2017;26(9):1263–9. 10.1002/pon.4303 . Tables Table 1 Descriptive Statistics for the Study Sample, 2017-2021. Full sample 2 Propensity score–matched sample No. (%) 3 No. (%) 3 Overall 1 URRBMI UEBMI SMD 4 URRBMI UEBMI SMD 4 Characteristic (N=319677) (N=192684) (N=97639) (N=58389) (N=58389) Matching Variables Year of diagnosis 2017 48010 (15.0) 28577 (14.8) 14247 (14.6) -0.0024 9303 (15.9) 10850 (18.6) 0.0265 2018 55579 (17.4) 32906 (17.1) 16716 (17.1) 0.0004 10667 (18.3) 9161 (15.7) -0.0258 2019 64703 (20.2) 38147 (19.8) 20059 (20.5) 0.0075 12033 (20.6) 13378 (22.9) 0.0230 2020 70761 (22.1) 42973 (22.3) 21884 (22.4) 0.0011 13258 (22.7) 12738 (21.8) -0.0089 2021 80624 (25.2) 50081 (26.0) 24733 (25.3) -0.0066 13128 (22.5) 12262 (21.0) -0.0148 Age at diagnosis Mean (SD), years 64.8 (10.7) 65.5 (10.1) 63.5 (11.6) 63.7 (10.8) 63.3 (11.2) <45 10932 (3.4) 4362 (2.3) 5418 (5.5) 0.0329 2329 (4.0) 1931 (3.3) -0.0068 45-59 83109 (26.0) 46563 (24.2) 28787 (29.5) 0.0532 17703 (30.3) 20987 (35.9) 0.0562 60-75 175602 (54.9) 111920 (58.1) 48424 (49.6) -0.0849 30079 (51.5) 26488 (45.4) -0.0615 >75 50034 (15.7) 29839 (15.5) 15010 (15.4) -0.0011 8278 (14.2) 8983 (15.4) 0.0121 Gender Male 191603 (59.9) 113187 (58.7) 61383 (62.9) 35738 (61.2) 36078 (61.8) Female 128074 (40.1) 79497 (41.3) 36256 (37.1) -0.0413 22651 (38.8) 22311 (38.2) -0.0058 Ethnicity Han 316257 (98.9) 190634 (98.9) 96725 (99.1) 57872 (99.1) 57741 (98.9) Other 3420 (1.1) 2050 (1.1) 914 (0.9) -0.0013 517 (0.9) 648 (1.1) 0.0022 Marital status Single 7227 (2.3) 4985 (2.6) 1469 (1.5) -0.0108 631 (1.1) 1215 (2.1) 0.0100 Married 305317 (95.5) 183039 (95.0) 94328 (96.6) 0.0161 56906 (97.5) 56112 (96.1) -0.0136 Divorced 7133 (2.2) 4660 (2.4) 1842 (1.9) -0.0053 852 (1.5) 1062 (1.8) 0.0036 Occupation Employees/workers 29829 (9.3) 7292 (3.8) 20224 (20.7) 0.1693 7292 (12.5) 5988 (10.3) -0.0223 Non-practitioners 5 158322 (49.5) 130156 (67.5) 16045 (16.4) -0.5112 16045 (27.5) 16045 (27.5) 0.0000 Special Employees 6 39191 (12.3) 5130 (2.7) 30833 (31.6) 0.2892 5130 (8.8) 6990 (12.0) 0.0319 Unspecified 92335 (28.9) 50106 (26.0) 30537 (31.3) 0.0527 29922 (51.2) 29366 (50.3) -0.0095 Non-Matching Variables Types of lung cancer <0.001 <0.001 SCLC 29934 (9.4) 20651 (10.7) 7439 (7.6) 6065 (10.4) 4589 (7.9) NSCLC 203011 (63.5) 115477 (59.9) 68673 (70.3) 37296 (63.9) 40115 (68.7) Unspecified 86732 (27.1) 56556 (29.4) 21527 (22.0) 15028 (25.7) 13685 (23.4) Tumor metastasis <0.001 <0.001 No 250591 (78.4) 146405 (76.0) 80374 (82.3) 45469 (77.9) 47741 (81.8) Yes 69086 (21.6) 46279 (24.0) 17265 (17.7) 12920 (22.1) 10648 (18.2) CCI <0.05 = 2 39326 (12.3) 23662 (12.3) 12249 (12.5) 6709 (11.5) 7167 (12.3) Hospital level <0.001 <0.001 Secondary hospitals 82728 (25.9) 59222 (30.7) 15840 (16.2) 13244 (22.7) 9633 (16.5) Tertiary hospitals 234941 (73.5) 132318 (68.7) 81350 (83.3) 44819 (76.8) 48468 (83.0) Unclassified or other 2008 (0.6) 1144 (0.6) 449 (0.5) 326 (0.6) 288 (0.5) Hospital region 7 <0.001 <0.001 Eastern (Peninsula) Region 74962 (23.4) 32515 (16.9) 32765 (33.6) 18446 (31.6) 21999 (37.7) Northern Region 41561 (13.0) 30494 (15.8) 8761 (9.0) 6122 (10.5) 4768 (8.2) Southern Region 80323 (25.1) 60804 (31.6) 12869 (13.2) 12582 (21.5) 8092 (13.9) Central Region 122831 (38.4) 68871 (35.7) 43244 (44.3) 21239 (36.4) 23530 (40.3) Note. Abbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance; NSCLC, non-small cell lung cancer. SCLC, Small cell lung cancer; CCI, the Charlson Comorbidity Index; SMD, standardized mean difference (absolute value of difference in means divided by the standard deviation). 1 Other insurance types (e.g., public health insurance, private health insurance, supplementary health insurance, poverty assistance, etc.) and no insured patients (i.e., all paid out of pocket) represented 6.9% and 2.3% of the cohort and are not included in this table. 2 Sample is drawn from the overall data of URRBMI and UEBMI populations and is used for analyzing treatments and expenditures. 3 Values are written as No. (%) unless otherwise stated. 4 SMD is presented for matching variables as the PSM result, while P-value from Pearson's Chi-square test is shown for non-matching variables to evaluate intergroup difference significance. 5 Non-practitioners: Self-employed /Unemployed/ Freelance/Students /Farmers. 6 Special Employees: Retired (retired) staff/civil servants/Professional and technical staff. 7 Based on the topography, population and culture of Shandong Province, the hospital regions are divided into four regions: the Jiaodong Peninsula region (Eastern (Peninsula) Region), the Luzhong region (Central Region), the Lubei region (Northern Region) and the Lunan region (Southern Region) Table 2 Differences in Lung cancer treatment in the first year after diagnosis, by health insurance schemes, 2017-21. No. (%) UEBMI vs URRBMI 1 Treatment Outcomes UEBMI (N=58389) URRBMI (N=58389) AME [95% CI] (%) P-value Surgery 6.76 [6.31, 7.20] <0.001 Yes 26866 (46.0) 20253 (34.7) No 31523 (54.0) 38136 (65.3) Radiotherapy / Chemotherapy -0.55 [-1.09, -0.02] <0.05 Yes 21890 (37.5) 22725 (38.9) No 36499 (62.5) 35664 (61.1) Targeted Therapy 2.39 [2.05, 2.73] <0.001 Yes 6731 (11.5) 5623 (9.6) No 51658 (88.5) 52766 (90.4) Immunotherapy 1.26 [1.00, 1.53] <0.001 Yes 3759 (6.4) 3132 (5.4) No 54630 (93.6) 55257 (94.6) Discharge Against Medical Advice -1.56 [-1.92, -1.20] <0.001 Yes 6013 (10.3) 7467 (12.8) No 52376 (89.7) 50922 (87.2) Note. Abbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance. AME, Adjusted Marginal Effect. 1 Models adjust for patient socio-demographics, year of diagnosis, clinical characteristics and healthcare provider characteristics. Treatment outcomes were analyzed using a multivariate regression analysis. Table 3 Comparison of Treatment between URRBMI and UEBMI in Non-metastatic and Metastatic Lung Cancer Patients, 2017-2021. No. (%) UEBMI vs URRBMI 2 Treatment UEBMI (N=47170) URRBMI (N=47170) AME [95% CI] (%) p-value Non-metastatic Group 1 Surgery 8.04 [7.52, 8.57] <0.001 Yes 26916 (57.1) 20482 (43.4) No 20254 (42.9) 26688 (56.6) Radiotherapy / Chemotherapy -1.40 [-1.98, -0.82] <0.001 Yes 15811 (33.5) 17114 (36.3) No 31359 (66.5) 30056 (63.7) Targeted Therapy 1.16 [0.82, 1.50] <0.001 Yes 3983 (8.4) 3395 (7.2) No 43187 (91.6) 43775 (92.8) Immunotherapy 0.86 [0.59, 1.14] <0.001 Yes 2572 (5.5) 2190 (4.6) No 44598 (94.5) 44980 (95.4) Discharge Against Medical Advice -1.79 [-2.16, -1.41] <0.001 Yes 3971 (8.4) 5349 (11.3) No 43199 (91.6) 41821 (88.7) No. (%) UEBMI vs URRBMI 2 Treatment UEBMI (N=11298) URRBMI (N=11298) AME [95% CI] (%) p-value Metastatic Group 1 Surgery 1.13 [0.52, 1.74] <0.001 Yes 732 (6.5) 571 (5.1) No 10566 (93.5) 10727 (94.9) Radiotherapy / Chemotherapy 3.49 [2.27, 4.71] <0.001 Yes 5873 (52.0) 5352 (47.4) No 5425 (48.0) 5946 (52.6) Targeted Therapy 7.87 [6.89, 8.86] <0.001 Yes 2893 (25.6) 1967 (17.4) No 8405 (74.4) 9331 (82.6) Immunotherapy 3.41 [2.69, 4.14] <0.001 Yes 1222 (10.8) 849 (7.5) No 10076 (89.2) 10449 (92.5) Discharge Against Medical Advice -1.15 [-2.14, -0.16] <0.05 Yes 1900 (16.8) 2099 (18.6) No 9398 (83.2) 9199 (81.4) Note. Abbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance. AME, Adjusted Marginal Effect. 1 This table is based on two subgroup samples including the presence or absence of cancer metastases. Descriptive statistics for the samples are provided in Appendix Table. 2 Models adjust for patient socio-demographics, year of diagnosis, clinical characteristics and healthcare provider characteristics. Treatment outcomes were analyzed using a multivariate regression analysis. Table 4 Differences in medical expenditure in the first year after diagnosis, by health insurance schemes, 2017-21 1 . Adjusted mean (95%CI) GLM results (UEBMI vs URRBMI) 2 Expenditures, RMB URRBMI 1 (N=40820) UEBMI 1 (N=40820) Exp(coefficient)-1 [95% CI] (%) 3 P-value Total expenditures 60,383.08 (59,868.90 - 60,897.26) 69,812.93 (69,268.91 - 70,356.94) 13.34 [13.08, 15.47] <0.001 Out-of-pocket expenditures 33,258.46 (32,940.02 - 33,576.90) 27,193.36 (26,921.38 - 27,465.35) -19.09 [-18.39, -16.36] <0.001 Surgical expenditures 7,929.47 (7,793.81 - 8,065.13) 9,606.28 (9,466.10 - 9,746.46) 18.57 [17.68, 23.19] <0.001 Drug expenditures 18,157.26 (17,906.03 - 18,408.48) 19,963.04 (19,679.57 - 20,246.51) 8.21 [6.87, 10.26] <0.001 Diagnosis-related expenditures 14,828.55 (14,700.75 - 14,956.34) 15,420.04 (15,298.49 - 15,541.60) 8.21 [6.90, 10.25] <0.001 Note. Abbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance. GLM, generalized linear model. 1 This table is based on a PSM sample that excludes cases with zero or missing medical expenditure. Descriptive statistics for the sample are provided in Appendix Table. 2 Models adjust for patient socio-demographics, year of diagnosis, clinical characteristics and healthcare provider characteristics. Expenditures outcomes were analyzed using a GLM with a gamma distribution and log link, with outcomes in 2021 inflation-adjusted terms. 3 Exp (coefficient) -1 (%) reflects the relative change proportion of the medical expenditures in the UEBMI group compared to the URRBMI group. Additional Declarations No competing interests reported. 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Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHCChAMMFQdADGYQwdhAnJYzJGoBKmsjRYvBjYSHhwvn3bHnb+A9bMzDYCO74QDzswf4tEjOSEg4PHPbs8QZB/iSk3kY0ow3HGAzN8CnhV8CqIV32+EEAwYe48M8DIcTNxzgYZPAp4UNrGXOYXuolv+EtUBsaTjMuAGoBeiwA4S1SPY8SDjMc+xw4ozDfMmGcwySjWceZjPDq8XgeE7yZ56aw/b87b2HJd5U2Mn2HW9+hlcLAwNPAoRm5gGZwACNHryA/QBML0Glo2AUjIJRMEIBAPQKR95u81EOAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-03-12 12:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6212081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6212081/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12939-025-02533-z","type":"published","date":"2025-06-05T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79673561,"identity":"80e98c06-7b3d-47f7-a54e-d5c866f8d0ad","added_by":"auto","created_at":"2025-04-01 11:49:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76180,"visible":true,"origin":"","legend":"\u003cp\u003eSample derivation\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6212081/v1/8d8a8566eac2d928e52f37df.png"},{"id":79673564,"identity":"c1a5b5e4-5bbf-44f6-8796-d600196e3db6","added_by":"auto","created_at":"2025-04-01 11:49:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81176,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in incidence of lung cancer treatments among URRBMI and UEBMI beneficiaries in China from 2017 to 2021\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: SHI, social health insurance; UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance; DAMA, discharge against medical advice.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSamples contain propensity score–matched lung cancer patients in 2017-21.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6212081/v1/8d0c9fe85a0a12446efec7e0.png"},{"id":84243197,"identity":"e0770e8e-7b03-4c24-a7e4-d178afd2dcd8","added_by":"auto","created_at":"2025-06-09 16:12:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1253916,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6212081/v1/f1964a36-103a-4453-be93-d83edd5fb46b.pdf"},{"id":79674904,"identity":"18db73f9-02ad-45d9-9da3-84d22304677a","added_by":"auto","created_at":"2025-04-01 11:57:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":81582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6212081/v1/9e094d6ae8661ae6e8c4af4b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disparities in Treatment and Expenditures among Lung Cancer Patients under Tiered Social Health Insurance: A Population-Based Study in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRisk pooling for health care, frequently established through social health insurance (SHI), is expected to enable patients to access essential health services when needed without financial hardship\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For diseases associated with high expenditures like cancer, unequal SHI coverage could make substantial differences in both access to quality services and financial vulnerability\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In health systems with tiered pools of SHI, which are common in low- and middle-income countries (LMICs)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, disparity of benefit package or copayment rates means potentially significant disparities of cancer care and financial protection\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn China, approximately 95% of the population was covered by two main types of SHI in 2023\u003csup\u003e12\u003c/sup\u003e. The Urban Employee Basic Medical Insurance (UEBMI) funded through premiums contributed by employers and individuals covers employees and retirees in the formal sector, who constitute about 28% of all SHI beneficiaries\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The other main scheme was the Urban and Rural Resident Basic Medical Insurance (URRBMI)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. UEBMI and URRBMI share the same benefit package. However, the former is more generous than the latter in reimbursement, though the gap in-between has been narrowing. In 2023, the average reimbursement rates for inpatient expenses under UEBMI and URRBMI were 84.6% and 68.1%, respectively\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Understanding the disparities in cancer care between schemes may provide important insights to narrow the benefit gaps in the context of tiered SHI\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA literature review from the United States found that cancer patients with no or inferior insurance coverage had lower utilization of high-cost treatments and systemic treatments, and higher chances of treatment delays\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In the case of lung cancer, despite recent advancement in treatment technology for patients with this condition, international studies consistently demonstrated significant disparities in cancer care and economic burden across health insurance schemes\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While the majority of current studies on the disparity between private and public health insurance, few studied the effects of tiered SHI on disparity in cancer care in China and other LMICs.\u003c/p\u003e \u003cp\u003eLung cancer, which has the highest incidence and mortality rates among all malignant tumors in China\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, causes a substantial medical and economic burden on both patients and society\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, little is known about the inequalities regarding lung cancer care and expenditure caused by the tiered SHI in China. To fill this knowledge gap, we sought investigating the disparities of lung cancer treatment and related expenditures between URRBMI and UEBMI beneficiaries in Shandong, one of the most populous provinces in China.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and study population\u003c/h2\u003e \u003cp\u003eShandong is a coastal province in eastern China. It had a population of approximately 101.2\u0026nbsp;million with level of economic development equivalent to a upper-middle income country\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. We used standardized hospital discharge data, formally known as hospitalization record front pages (HRFPs) from all secondary and tertiary hospitals in Shandong stored in the Cheeloo Lifespan Electronic Health Research Data-library (Cheeloo LEAD) (see Methodological Appendix Part 1 for details). HRFPs cover basic socio-demographics of patients, detailed information about disease diagnosis, treatment, and expenditure. Data on the platform were deidentified, with data for an individual linked via a unique encrypted identity number.\u003c/p\u003e \u003cp\u003eWe selected patients who were diagnosed with lung cancer (ICD-10: C34) and under URRBMI or UEBMI from January 1, 2017 to December 31, 2021. We set a window of at least four years to exclude patients with whom lung cancer had been previously diagnosed, by washing out repeated diagnoses emerging in the following years. The four-year wash-out period was adopted as the number of new cancer cases in 2021 remained stable when the time window was reset from four years (2017\u0026ndash;2021) to eight years (2013\u0026ndash;2021). Only cancer cases in individuals who received no cancer-specific diagnosis or treatment during the washout period were considered index cases\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and thus included in our analysis (see Methodological Appendix Part 2 for details). We further excluded patients aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years or \u0026gt;\u0026thinsp;100 years at diagnosis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes how the sample was derived. We followed up all patients for lung cancer-specific hospitalizations until one year after the incidence hospitalization (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in details).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e As this study used pre-existing secondary data for analysis, informed consent was waived. This study was approved by the Ethics Committee for Public Health of Shandong University (LL20241105). We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines to ensure the reporting of this observational study\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e[Please insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here.]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 Health insurance status and covariates\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eHealth insurance status was categorized as URRBMI and UEBMI, based on the insurance type recorded in HRFP. For the fewer than 10% of individuals who showed changes in scheme enrolment, we used the one that covered the bigger share of their hospitalization counts.\u003c/p\u003e \u003cp\u003eBuilding on previous literature\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, we included patient socio-demographics, clinical and healthcare provider characteristics as covariates. Socio-demographic characteristics included sex, age, marital status, ethnicity and occupation at diagnosis. Clinical characteristics included the histologic type of lung cancer, whether the cancer has metastasized, and non-cancer comorbidities. Patient comorbidities were assessed using the Charlson Comorbidity Index (CCI), as described by Deyo and colleagues (see Supplementary Table S2 for the score for each diagnostic code)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. We categorized patients into three groups based on non-cancer CCI score: 0, 1, and 2 or more. We specifically focused on non-cancer comorbidities to examine their impacts on the lung cancer care\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Meanwhile, we obtained information about the level of hospital (tertiary, secondary, and unclassified or other) from the official database of China\u0026rsquo;s National Health Commission (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zgcx.nhc.gov.cn\u003c/span\u003e\u003cspan address=\"https://zgcx.nhc.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The sites of hospitals were categorized into four regions based on proximity in geography and economic development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcomes\u003c/h2\u003e \u003cp\u003eOur main outcome variables include cancer care and expenditures within one year after the index hospitalization, because the intensity of medical treatment and expenditures for cancer patients are significantly higher in the first year compared to the years afterwards\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and that in the first year after diagnosis has a substantial impact on the prognosis of lung cancer\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe examined a range of cancer care variables, including surgery, radiation therapy or chemotherapy, targeted therapy, and immunotherapy identified via a series of ICD-10 and ICD-9-CM3 codes\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e (see Supplementary Table S3), as well as discharge against medical advice (DAMA). DAMA refers to the practice where a patient chooses to leave the hospital against medical assessment based on the patient\u0026rsquo;s conditions\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. It can lead to increased chances of morbidity and mortality\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, so we included it as a key indicator of disparity in cancer care. Besides, we assessed expenditures, including total, out-of-pocket, surgical, drug, and diagnostic expenditures. The expenditures were adjusted to 2021 Chinese \u003cem\u003eyuan\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables are reported as frequencies (%) while continuous variables are summarized as means with standard deviation (SD). Pearson's Chi-square test was used for assessing differences of categorical variables.\u003c/p\u003e \u003cp\u003eTo control for differences between beneficiaries of the two main categories of SHI, we established propensity score-matched samples (see Methodological Appendix Part 4). One-to-one propensity score matching (PSM) was performed using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Logistic regression was used to calculate a propensity score, which evaluates confounding by indication and/or baseline covariates between two insurance groups. The matching variables used in the PSM models were year of diagnosis, age group, gender, race, marital status, and occupation.\u003c/p\u003e \u003cp\u003eThe primary analytical approach was multivariable regression analysis of the propensity score-matched sample (see Methodological Appendix Part 3 for details). We utilized multivariable logistic regressions to measure associations between health insurance status and the receipt of treatments, and conducted subgroup analyses based on whether the cancer had metastasized. We reported marginal effects, which are interpreted as average differences in the probability of receiving any type of treatment had a beneficiary of URRBMI been covered by UEBMI\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Then, we applied a generalized linear model (GLM) with a gamma distribution and log link function to estimate the difference in expenditure attributed to health insurance status. All models were adjusted for covariates mentioned above. We also incorporated the timing of cancer diagnosis by considering fixed effects of the year of diagnosis. Additionally, expenditures were log transformed after adding 1 to all values to allow for zeros.\u003c/p\u003e \u003cp\u003eP values were 2-sided with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered indicative of statistical significance. All statistical analyses were performed using R version 4.3.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Patient characteristics and matching\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e319,677 patients were diagnosed with lung cancer between Jan 1, 2017, and Dec 31, 2021, of which 60.27% were insured by URRBMI and 30.54% were insured by UEBMI. The mean age of those covered by URRBMI was 65.5 (SD, 10.1) years, with males accounting for 58.7%. The mean age of UEBMI beneficiaries was 63.5 (SD, 11.6) years, with males accounting for 62.9%. Table 1 shows descriptive statistics for the unmatched and matched samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSocio-demographic characteristics across the 2 matched groups were well balanced, with all standardized mean differences smaller than 0.1. After matching, 68.7% of UEBMI patients were diagnosed with NSCLC, and 12.3% had CCI ≥ 2, both higher than the 63.9% and 11.5% observed in URRBMI patients. Furthermore, UEBMI patients were more likely to receive treatment at tertiary hospitals (83.0% vs. 76.8%) and at hospitals located in the Central Region (40.3% vs. 36.4%). Notably, 22.1% of URRBMI patients had tumor metastasis, which was higher than the 18.2% occurred among UEBMI patients.\u003c/p\u003e\n\u003cp\u003e[Please insert Table 1 here.]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Cancer treatment disparity after PSM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 demonstrates trends in cancer treatment among URRBMI and UEBMI beneficiaries diagnosed with lung cancer. From 2017 to 2021, there were increasing proportions of matched samples receiving surgery (UEBMI: 29.7% in 2017 to 54% in 2021; URRBMI: 24.6% in 2017 to 46.7% in 2021), targeted therapy (UEBMI: 4.7% in 2017 to 20.2% in 2021; URRBMI: 2.2% in 2017 to 19.5% in 2021), and immunotherapy (UEBMI: 2.9% in 2017 to 13.2% in 2021; URRBMI: 1.1% in 2017 to 12.8% in 2021), with the gradually decreasing difference between the two schemes. Meanwhile, the proportions of DAMA were decreasing among matched samples in both groups (UEBMI: 13.0% in 2017 to 7.8% in 2021; URRBMI: 15% in 2017 to 10.4% in 2021).\u003c/p\u003e\n\u003cp\u003e[Please insert Figure 2 here.]\u003c/p\u003e\n\u003cp\u003eTable 2 displays cancer-directed treatment and the average marginal effects of UEBMI beneficiaries over URRBMI beneficiaries on the likelihood of receiving cancer therapy for the matched sample. UEBMI beneficiaries were more likely, compared with URRBMI beneficiaries, to receive surgery (46% vs 34.7%), targeted therapy (11.5% vs 9.6%), and immunotherapy (6.4% vs 5.4%). According to the results of the multivariable logistic regression, UEBMI was associated with increased probabilities of receiving surgery (average marginal difference [AME]: 6.76%; 95% CI, 6.31% to 7.20%), targeted therapy (AME, 2.39%; 95% CI, 2.05% to 2.73%) and immunotherapy (AME, 1.26%; 95% CI, 1% to 1.53%). In contrast, UEBMI was associated with lower probabilities to experience chemotherapy or radiotherapy (AME, -0.55%; 95% CI, -1.09 to -0.02) and DAMA (AME, -1.56%; 95% CI, -1.92 to -1.20).\u003c/p\u003e\n\u003cp\u003e[Please insert Table 2 here.]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroups analysis of cancer treatment disparity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3\u0026nbsp;presents the cancer-directed treatment and the AMEs of UEBMI over URRBMI on the likelihood of receiving cancer therapy for matched subgroups, categorized by the presence or absence of metastasis. Distribution of the propensity scores for the unmatched and matched subgroups is provided in Supplementary Tables S4-5.\u003c/p\u003e\n\u003cp\u003eIn patients without metastasis, UEBMI were associated with a higher rate of surgery (AME, 8.04%; 95% CI, 7.52% to 8.57%) but a lower rate of chemotherapy or radiotherapy alone (AME, -1.40%; 95% CI, -1.98% to -0.82%). Among patients with metastasis, the AME of UEBMI was 1.13% (95% CI, 0.52% to 1.74%) for surgery, and 3.49% (95% CI, 2.27% to 4.71%) for chemotherapy or radiotherapy. Additionally, in both subgroups, UEBMI beneficiaries were consistently associated with higher likelihoods of receiving both targeted therapy and immunotherapy (particularly among the group with metastatic cancer), and a lower likelihood of DAMA (non-metastatic group: AME, -1.79%; 95% CI, -2.16% to -1.41%; metastatic group: AME, -1.15%; 95% CI, -2.14% to -0.16%).\u003c/p\u003e\n\u003cp\u003e[Please insert Table 3 here.]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Disparity in expenditures after PSM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents differences in expenditures across SHI schemes in the matched sample (see Supplementary Table S6 for further descriptive statistics about the unmatched and matched samples) and the additional expenditures as a proportion of expenditures under URRBMI associated with URRBMI after controlling for covariates. Patients under UEBMI had 13.34% higher total expenditures compared with those under URRBMI (95% CI, 13.08% to 15.47%). Specifically, surgical expenditures were 18.57% higher for UEBMI patients (95% CI, 17.68% to 23.19%), drug expenditures were 8.21% higher (95% CI, 6.87% to 10.26%), and diagnostic expenditures were 8.21% higher (95% CI, 6.90% to 10.25%). However, out-of-pocket expenditures for UEBMI patients was 19.09% lower than for URRBMI patients (95% CI, -18.39% to -16.36%).\u003c/p\u003e\n\u003cp\u003e[Please insert Table 4 here.]\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTaking advantage of population-wide discharge data from one of the most populous provinces in China, we analyzed the inequalities between URRBMI and UEBMI beneficiaries in lung cancer treatment and related expenditures. Using the PSM method to adjust for different patient characteristics across the two schemes, we observed an increase in utilization of treatment services for lung cancer patients in Shandong, China. However, significant inequalities remained in both cancer treatment and financial protection between URRBMI and UEBMI beneficiaries in China, with a notable disparity in surgical treatment, particularly among those without metastatic cancer.\u003c/p\u003e \u003cp\u003eOur findings about the treatment inequalities between cancer patients under different SHI schemes in China, particularly in terms of surgery, are consistent with findings from multiple existing studies showing more generous insurance to be associated with higher rates of receiving cancer treatment (especially curative surgery)\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Crucially, we found the inequalities in surgery to be more pronounced among patients with non-metastatic lung cancer. Stokes et al\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and Wakeam et al\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e also demonstrated that uninsured and Medicaid patients were less likely to receive surgery among patients with early-stage lung cancer compared with Medicare patients in the United States. Similar studies also identified discrepancies in delays of care and receipt of resection related to insurance within pancreatic, colorectal and hepatocellular cancers\u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. As early surgical intervention is associated with significantly longer survival for lung cancer\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, the treatment inequalities in early-stage cancer likely translate into inequalities in survival. Meanwhile, the rapid rise of incidence cases and rates of surgery (nearly doubled during the study period) may also reflect potential over diagnosis of lung cancer and overuse of surgery. However, further analysis on this issue is beyond the scope of this study.\u003c/p\u003e \u003cp\u003eThe centralized procurement of innovative anticancer drugs by China\u0026rsquo;s National Healthcare Security Administration since 2018 led to an increasing number of receiving SHI reimbursement, which likely contributed to the rising proportion of people receiving targeted therapy or immunotherapy observed in our study. Particularly among patients with metastatic lung cancer, the main care advantage associated with UEBMI in comparison to URRBMI shifted to the utilization of radiotherapy, chemotherapy, targeted therapy and immunotherapy.\u003c/p\u003e \u003cp\u003eMoreover, our study also found that URRBMI (compared with UEBMI) was associated with a higher rate of DAMA, which might result in rapid deterioration of the disease and shortened survival time\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Previous studies also found the type of health insurance might affect patients' treatment choices and the continuity of care they receive\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In stage IV non-small-cell lung cancer patients, Duma et al\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e observed Medicare and uninsured patients were more likely to refuse treatment compared to patients with private insurance.\u003c/p\u003e \u003cp\u003eIn our study, the persisted disparities in lung cancer care across SHI schemes after controlling for potential confounders, reveal potentially substantial unwarranted variations in cancer that could not be explained by illness severity or patient preference\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Besides factors on the demand-side, potential explanation from the provider perspectives is that physicians adjust their clinical management in response to patients\u0026rsquo; insurance schemes\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e that provide differed financial incentives and constraints. In other words, the more generous reimbursement rates of UEBMI may encourage physicians to take more aggressive cancer care.\u003c/p\u003e \u003cp\u003eIn terms of expenditures, patients under UEBMI experienced better financial protection than those under URRBMI, which is consistent with previous research\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. This economic advantage appears to translate into disparities in cancer healthcare, where UEBMI patients demonstrated greater access to cancer care than their URRBMI patients\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The heightened financial burden faced by URRBMI beneficiaries carries particular implications for vulnerable populations. As Mao et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e demonstrated in their study, cancer patients over 60 years old faced a heavier financial burden, with high hospitalization costs potentially becoming a barrier for the elderly.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplications for policy and practice\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral implications can be drawn for this study. First, the double inequalities in treatment and financial protection for lung cancer patients imply that inadequacy in cancer care for the URRBMI beneficiaries. Particularly for non-metastatic or early-stage lung cancer patients, it is important to consider narrowing the inequalities in reimbursement rates for surgery between URRBMI and UEBMI to enable URRBMI beneficiaries to afford necessary surgery at the right stage and to protect them from catastrophic expenditure. Indeed, policies on benefit packages and reimbursement rates may be further coordinated, so that incremental harmonization of SHI schemes prioritize raising reimbursement rates for good value cancer care. Second, the disparity in radiotherapy/chemotherapy, targeted therapy and immunotherapy in (particularly metastatic) lung cancer patients across SHI schemes in China should also raise discussions about standardization of cancer care and \u0026ldquo;value for money\u0026rdquo;. It is possible that some expenditures of UEBMI in the late-stage cancer care could be made to much better use in URRBMI for patients with an earlier stage cancer. Third, the fact that a much larger proportion of URRBMI patients receive care at the secondary hospitals than UEBMI patients suggest the importance of making sure quality are comparable and continuous across secondary and tertiary hospitals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral limitations of our study warrant caution in interpretation. First, there is an absence of comprehensive staging information for lung cancer patients in our dataset. While this precluded a more nuanced analysis of how detailed stages of lung cancer affect outcomes across SHI schemes, our subgroup analysis stratified by whether the cancer was metastatic should have addressed a substantial part of the patient\u0026rsquo;s cancer stage upon diagnosis. Second, due to the lack of more socio-economic variables about participants, we were not able to distinguish the effects of SHI scheme policies from the patients\u0026rsquo; health literacy\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and attitudes towards surgery\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, as well as social and family support\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e which would be more positive among the UEBMI beneficiaries as compared to those under URRBMI. Third, while our study did not delve into the quality or health outcome of treatment received, or whether the unwarranted disparity represents under- or over-treatment, these aspects present important avenues for future research. Fourth, given the mobility of patients, the HRFP would not capture hospitalizations outside Shandong Province, which means an underestimation of treatments and expenditures. However, due to the province's large population and well-developed healthcare resources, cancer patients often rely heavily on local care. Hence, this underestimation is likely to be small. Finally, the data were obtained from medical institutions in Shandong Province means our results are not directly generalizable to other provinces or countries. However, the significant disparities we found are likely to be observed elsewhere in China, as the tiered SHI exists nation-wide.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWhile utilization for cancer treatment services improved in recent years for patients with lung cancer under both UEBMI and URRBMI in China. UEBMI coverage was associated with a substantially higher likelihood of receiving a range of cancer therapeutics compared to URRBMI coverage, especially surgery among patients without metastasis, and lower out-of-pocket expenditures. Such disparities in both treatment and financial protection for cancer reflect rooms for better harmonization between SHI schemes in health systems with tiered SHI pools.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUEBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrban Employee Basic Medical Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eURRBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrban and Rural Residents Basic Medical Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by the National Natural Science Foundation of China (71804004), National Natural Science Foundation of China (82330108), and Henan Science and Technology Major Program (241100310300). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ conceptualised the research ideas, interpreted the data, performed the statistical analysis and wrote the first draft. YH conceptualised the research ideas, interpreted the data and reviewed the first draft. QW contributed to data collection and data management. YM, XX \u0026amp; SL reviewed the first draft. XJ, QZ, YW \u0026amp;YZ contributed to data collection. CL \u0026amp; LZ provided critical revisions to the manuscript and contributed to the discussion of the findings. DW contributed to data collection, funding acquisition, and project administration. FX contributed to data collection, data management, funding acquisition, and project administration, and reviewed the final draft. JX conceptualised the research ideas, interpreted the data, performed the statistical analysis and reviewed the draft. All authors reviewed and approved the first draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData statement and availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset is collected by Shandong University and is not publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee for Public Health of Shandong University (LL20241105)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Psychooncology. 2017;26(9):1263\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/pon.4303\u003c/span\u003e\u003cspan address=\"10.1002/pon.4303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u0026nbsp;Table 1 Descriptive Statistics for the Study Sample, 2017-2021.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"933\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eFull sample\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003ePropensity score\u0026ndash;matched sample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eNo. (%)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eNo. (%)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOverall\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eURRBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eUEBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSMD\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eURRBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eUEBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSMD\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(N=319677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e(N=192684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e(N=97639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e(N=58389)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e(N=58389)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatching Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eYear of diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e48010 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e28577 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e14247 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e9303 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e10850 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e55579 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e32906 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e16716 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e10667 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e9161 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e64703 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e38147 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e20059 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12033 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e13378 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e70761 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e42973 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e21884 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e13258 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12738 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e80624 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e50081 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e24733 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e13128 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12262 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eAge at diagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eMean (SD), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e64.8 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e65.5 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e63.5 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e63.7 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e63.3 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026lt;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e10932 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e4362 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e5418 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e2329 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1931 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e83109 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e46563 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e28787 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e17703 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e20987 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e60-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e175602 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e111920 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e48424 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e30079 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e26488 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026gt;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e50034 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e29839 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e15010 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e8278 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e8983 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e191603 (59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e113187 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e61383 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e35738 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e36078 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e128074 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e79497 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e36256 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e22651 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e22311 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e316257 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e190634 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e96725 (99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e57872 (99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e57741 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3420 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e2050 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e914 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e517 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e648 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e7227 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e4985 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1469 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e631 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1215 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e305317 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e183039 (95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e94328 (96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e56906 (97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e56112 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e7133 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e4660 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1842 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e852 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1062 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eEmployees/workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e29829 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e7292 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e20224 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e7292 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e5988 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eNon-practitioners\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e158322 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e130156 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e16045 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.5112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e16045 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e16045 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSpecial Employees\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e39191 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e5130 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e30833 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e5130 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e6990 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e92335 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e50106 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e30537 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e29922 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e29366 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Matching Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eTypes of lung cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e29934 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e20651 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e7439 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e6065 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e4589 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e203011 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e115477 (59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e68673 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e37296 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e40115 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e86732 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e56556 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e21527 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e15028 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e13685 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eTumor metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e250591 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e146405 (76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e80374 (82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e45469 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e47741 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e69086 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e46279 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e17265 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12920 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e10648 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCCI = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e198764 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e119260 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e60608 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e37088 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e36535 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCCI = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e81587 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e49762 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e24782 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e14592 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e14687 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCCI \u0026gt;= 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e39326 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e23662 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12249 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e6709 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e7167 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eHospital level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSecondary hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e82728 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e59222 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e15840 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e13244 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e9633 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eTertiary hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e234941 (73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e132318 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e81350 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e44819 (76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e48468 (83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eUnclassified or\u0026nbsp;other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2008 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1144 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e449 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e326 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e288 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eHospital region\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eEastern (Peninsula) Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e74962 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e32515 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e32765 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e18446 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e21999 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eNorthern Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e41561 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e30494 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e8761 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e6122 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e4768 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSouthern Region\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e80323 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e60804 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12869 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e12582 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e8092 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003eCentral Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e122831 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e68871 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e43244 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e21239 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e23530 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: UEBMI,\u0026nbsp;Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance; NSCLC, non-small cell lung cancer.\u0026nbsp;SCLC, Small cell lung cancer; CCI, the Charlson Comorbidity Index; SMD, standardized mean difference (absolute value of difference in means divided by the standard deviation).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eOther insurance types (e.g., public health insurance, private health insurance, supplementary health insurance, poverty assistance, etc.) and no insured patients (i.e., all paid out of pocket) represented 6.9% and 2.3% of the cohort and are not included in this table.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eSample is drawn from the overall data of URRBMI and UEBMI populations and is used for analyzing treatments and expenditures.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eValues are written as No. (%) unless otherwise stated.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eSMD is presented for matching variables as the PSM result, while P-value from Pearson\u0026apos;s Chi-square test is shown for non-matching variables to evaluate intergroup difference significance.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eNon-practitioners: Self-employed /Unemployed/ Freelance/Students /Farmers.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003eSpecial Employees: Retired (retired) staff/civil servants/Professional and technical staff.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e7\u003c/sup\u003eBased on the topography, population and culture of Shandong Province, the hospital regions are divided into four regions: the Jiaodong Peninsula region (Eastern (Peninsula) Region), the Luzhong region (Central Region), the Lubei region (Northern Region) and the Lunan region (Southern Region)\u003c/p\u003e\n\u003cp\u003eTable 2 Differences in Lung cancer treatment in the first year after diagnosis, by health insurance schemes, 2017-21.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eNo. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003eUEBMI vs URRBMI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eTreatment Outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eUEBMI (N=58389)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eURRBMI (N=58389)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAME [95% CI] (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e6.76 [6.31, 7.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e26866 (46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e20253 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\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: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e31523 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e38136 (65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eRadiotherapy / Chemotherapy\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: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.55 [-1.09, -0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e21890 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e22725 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e36499 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e35664 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eTargeted Therapy\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: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.39 [2.05, 2.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e6731 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e5623 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e51658 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e52766 (90.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eImmunotherapy\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: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.26 [1.00, 1.53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e3759 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e3132 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\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: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e54630 (93.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e55257 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\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: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eDischarge Against Medical Advice\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: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-1.56 [-1.92, -1.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e6013 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e7467 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e52376 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e50922 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance. AME, Adjusted Marginal Effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eModels adjust for patient socio-demographics, year of diagnosis, clinical characteristics and healthcare provider characteristics. Treatment outcomes were analyzed using a multivariate regression analysis.\u003c/p\u003e\n\u003cp\u003eTable 3 Comparison of Treatment between URRBMI and UEBMI in Non-metastatic and Metastatic Lung Cancer Patients, 2017-2021.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 196px;\"\u003e\n \u003cp\u003eNo. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eUEBMI vs URRBMI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eUEBMI\u003c/p\u003e\n \u003cp\u003e(N=47170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eURRBMI\u003c/p\u003e\n \u003cp\u003e(N=47170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAME [95% CI] (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-metastatic\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Group\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8.04 [7.52, 8.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e26916 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e20482 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e20254 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e26688 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eRadiotherapy / Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.40 [-1.98, -0.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e15811 (33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e17114 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e31359 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e30056 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTargeted Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.16 [0.82, 1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3983 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3395 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e43187 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e43775 (92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.86 [0.59, 1.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2572 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2190 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e44598 (94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e44980 (95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eDischarge Against Medical Advice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.79 [-2.16, -1.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3971 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5349 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e43199 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e41821 (88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eNo. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eUEBMI vs URRBMI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eUEBMI\u003c/p\u003e\n \u003cp\u003e(N=11298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eURRBMI\u003c/p\u003e\n \u003cp\u003e(N=11298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAME [95% CI] (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastatic Group\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.13 [0.52, 1.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e732 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e571 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10566 (93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10727 (94.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eRadiotherapy / Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.49 [2.27, 4.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5873 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5352 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5425 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5946 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTargeted Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7.87 [6.89, 8.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2893 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e1967 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8405 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9331 (82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.41 [2.69, 4.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e1222 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e849 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10076 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10449 (92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eDischarge Against Medical Advice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.15 [-2.14, -0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e1900 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2099 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9398 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9199 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: UEBMI,\u0026nbsp;Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance. AME, Adjusted Marginal Effect.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThis table is based on two subgroup samples including the presence or absence of cancer metastases. Descriptive statistics for the samples are provided in Appendix Table.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eModels adjust for patient socio-demographics, year of diagnosis, clinical characteristics and healthcare provider characteristics. Treatment outcomes were analyzed using a multivariate regression analysis.\u003c/p\u003e\n\u003cp\u003eTable 4 Differences in medical expenditure in the first year after diagnosis, by health insurance schemes, 2017-21\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAdjusted mean (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eGLM results (UEBMI vs URRBMI)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eExpenditures, RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eURRBMI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(N=40820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eUEBMI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(N=40820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eExp(coefficient)-1 [95% CI] (%)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTotal expenditures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e60,383.08\u003c/p\u003e\n \u003cp\u003e(59,868.90 - 60,897.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e69,812.93\u003c/p\u003e\n \u003cp\u003e(69,268.91 - 70,356.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e13.34 [13.08, 15.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eOut-of-pocket expenditures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e33,258.46\u003c/p\u003e\n \u003cp\u003e(32,940.02 - 33,576.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e27,193.36\u003c/p\u003e\n \u003cp\u003e(26,921.38 - 27,465.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-19.09 [-18.39, -16.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSurgical expenditures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7,929.47\u003c/p\u003e\n \u003cp\u003e(7,793.81 - 8,065.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e9,606.28\u003c/p\u003e\n \u003cp\u003e(9,466.10 - 9,746.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e18.57 [17.68, 23.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDrug expenditures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18,157.26\u003c/p\u003e\n \u003cp\u003e(17,906.03 - 18,408.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e19,963.04\u003c/p\u003e\n \u003cp\u003e(19,679.57 - 20,246.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e8.21 [6.87, 10.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDiagnosis-related expenditures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14,828.55\u003c/p\u003e\n \u003cp\u003e(14,700.75 - 14,956.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e15,420.04\u003c/p\u003e\n \u003cp\u003e(15,298.49 - 15,541.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e8.21 [6.90, 10.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: UEBMI, Urban Employee Basic Medical Insurance; URRBMI, Urban and Rural Residents Basic Medical Insurance. GLM, generalized linear model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThis table is based on a PSM sample that excludes cases with zero or missing medical expenditure. Descriptive statistics for the sample are provided in Appendix Table.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eModels adjust for\u0026nbsp;patient socio-demographics, year of diagnosis, clinical characteristics and healthcare provider characteristics. Expenditures outcomes were analyzed using a GLM with a gamma distribution and log link, with outcomes in 2021 inflation-adjusted terms.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Exp (coefficient) -1 (%) reflects the relative change proportion of the medical expenditures in the UEBMI group compared to the URRBMI group.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, disparity, treatment, expenditures, social health insurance","lastPublishedDoi":"10.21203/rs.3.rs-6212081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6212081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTiered social health insurance (SHI) schemes exist in many countries and may lead to significant disparities of healthcare and financial protection. The degree of cancer care inequalities under tiered SHI in China and other low- and middle-income countries (LMICs) remain poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 319,677 patients diagnosed with lung cancer between 2017 and 2021 in Shandong, we established propensity score-matched samples under the Urban and Rural Resident Basic Medical Insurance (URRBMI) and those under the Urban Employee Basic Medical Insurance (UEBMI). We ran multivariable regressions to assess the effects of SHI schemes on cancer treatment and expenditures. Subgroup analyses of cancer treatment were conducted based on whether the cancer had metastasized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the matched samples, utilization of cancer care increased under both schemes from 2017 to 2021. Higher proportions of cancer care use were seen in those under UEBMI compared those under URRBMI consistently with statistical significance. UEBMI was associated with a higher probability of receiving surgery in patients without metastasis, and higher probabilities of receiving radiotherapy or chemotherapy, targeted therapy, and immunotherapy in patients with metastasis. Patients under UEBMI were also less likely to be discharged against medical advice than those under URRBMI. Furthermore, UEBMI beneficiaries had 13.3% higher total expenditures but 19.1% lower out-of-pocket expenditures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant gaps remained in access to and financial protection for lung cancer, particularly in surgery for non-metastatic cancer. Targeted harmonization of benefit packages is needed to address pressing disparities in cancer care in LMICs with tiered SHI.\u003c/p\u003e","manuscriptTitle":"Disparities in Treatment and Expenditures among Lung Cancer Patients under Tiered Social Health Insurance: A Population-Based Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 11:49:08","doi":"10.21203/rs.3.rs-6212081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-10T01:25:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T12:11:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T08:13:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339626777788615325672770897079348630331","date":"2025-03-26T05:42:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43283535781301345562985938955434186120","date":"2025-03-24T04:32:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-23T21:15:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T12:16:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-14T01:43:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2025-03-12T12:29:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9899cde1-2c83-4829-9e3b-d528bd29f22c","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:11:20+00:00","versionOfRecord":{"articleIdentity":"rs-6212081","link":"https://doi.org/10.1186/s12939-025-02533-z","journal":{"identity":"international-journal-for-equity-in-health","isVorOnly":false,"title":"International Journal for Equity in Health"},"publishedOn":"2025-06-05 15:57:11","publishedOnDateReadable":"June 5th, 2025"},"versionCreatedAt":"2025-04-01 11:49:08","video":"","vorDoi":"10.1186/s12939-025-02533-z","vorDoiUrl":"https://doi.org/10.1186/s12939-025-02533-z","workflowStages":[]},"version":"v1","identity":"rs-6212081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6212081","identity":"rs-6212081","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}