Does pathologic type shape the hospitalization costs of advanced non- small cell lung cancer patients? A multicenter real-world data study

Does pathologic type shape the hospitalization costs of advanced non- small cell lung cancer patients? 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A multicenter real-world data study Yi Yang, Peng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3819071/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Lung cancer represents the highest incidence and mortality rates among all cancers in China. Limited studies have explored the hospitalization costs of advanced non-small cell lung cancer (NSCLC) among Chinese. This study aims to outline the hospitalization costs of NSCLC patients, differentiate influencing factors, examine different pathological types affecting hospitalization costs and evaluate influencing factors respectively. Methods: In this real-world, multicenter, retrospective study, we collected electronic medical record data from January 2017 to December 2020 in two types of hospitals: comprehensive hospitals and specialized oncology hospitals. A total of 5362 patients were included. Patients' information on sociodemographic characteristics, disease-related characteristics, healthcare service utilization, and hospitalization costs were collected. Descriptive analysis, the Wilcoxon rank-sum test, and the generalized linear model were employed. Results: The median hospitalization cost among advanced NSCLC patients was $17,254 per capita, with drug costs as the highest cost. The hospitalization cost among patients with non-squamous carcinoma ($18,003) was significantly higher than that among patients with squamous carcinoma ($15,024), and pathological type significantly influenced the costs (β=0.098, p<0.001). Common influencing factors of hospitalization costs for both types included health insurance, hospital type, department, hospitalization frequency, and average length of hospital stay. The varying significant factors comprised age, gender, and occupation type among non-squamous carcinoma patients, whereas these factors were not notable among squamous carcinoma patients. Conclusion: Hospitalization costs pose a substantial economic burden on advanced NSCLC patients in China, particularly for the non-squamous carcinoma. The higher costs hinder adequate utilization and appropriate treatment among vulnerable populations. Non-small cell lung cancer hospitalization cost non-squamous carcinoma squamous carcinoma influencing factors Figures Figure 1 Background Lung cancer is the most frequently diagnosed cancer in China accounting for over 20% of all new cancer cases, which also causes the most common death from cancer ( 1 ). It is estimated that 85–90% of lung cancer patients present with non-small cell cancer (NSCLC)( 2 , 3 ). A majority of patients are diagnosed with advanced or metastatic NSCLC( 4 ) resulting in poor prognosis( 4 ) and heavy disease burden due to the negative impact on disability, poor health-related quality of life, and significant financial burden ( 5 , 6 ). Cost for lung cancer patients, especially the hospitalization cost, is widely proven as the key factor in restraining adequate health utilization, equitable access to innovative treatment, and suitable drug use( 7 , 8 ). Globally, higher medical cost among NSCLC patients is found than among SCLC patients( 9 ), especially among NSCLC patients with the negative EGFR mutation or ALK( 10 , 11 ). NSCLC patients with the negative EGFR mutation or ALK rearrangement may have a bleaker prognosis and heavier disease burden because they lack the two most sensitized and treatable driver mutations( 12 , 13 ). It is necessary to pay attention to NSCLC patients at stage IIIB, IIIC, or IV, without EGFR/ALK mutation who are less benefited from targeted therapy and more suffered physically and economically( 14 , 15 ). To effectively alleviate the economic burden of cancer patients in China, more than 10 anti-cancer medications for NSCLC were featured in the National Reimbursement Drug List (NRDL) in 2017, and the prices were slashed by over 50%( 16 ). Thus, the impact of this policy on NSCLC patients’ medical costs needs to be observed in the following years. At the same time, the hospitalization cost is the greatest part of all costs as the main cost driver relating to advanced or metastatic NSCLC because they accepted a majority of treatment during hospitalization( 7 , 8 , 17 – 19 ). By analyzing the hospitalization cost of NSCLC patients with the negative EGFR/ALK mutation, we could provide basic information for researchers to make health economic evaluations and for decision-makers to optimize related policies. Recent studies have investigated the hospitalization costs associated with NSCLC in various countries. In Italy, a study in 2017 found the economic burden of NSCLC is extremely high with an overall healthcare cost of $ 25,859 per patient( 6 ), and another study found the highest direct costs were determined by hospitalizations( 20 ). In the United States, the average hospitalization cost for stage IB–IIIA NSCLC patients was $ 4,652 per month in 2021 USD dollars, with higher costs driven by higher NSCLC-related hospitalization rates( 21 ). In Germany, among stage IB-IIIA NSCLC patients, the largest cost drivers were associated with hospitalization/emergency costs of approximately $ 8,994 per capita in 2018( 18 ). The studies also found that with the emergency of immunotherapy medication and inhibitor therapy, a high economic burden occurred( 22 , 23 ). For example, annual hospitalization costs per capita for NSCLC patients in France treated with nivolumab was $ 10,309 in 2019, while it decreased over time( 23 ). However, the existing research has the following shortcomings. Firstly, to the best of our knowledge, there is no Chinese literature on the hospitalization costs of late-stage NSCLC patients, and global studies also mainly focused on early-stage NSCLC patients. There is room to provide new information about the hospitalization cost of advanced NSCLC patients, because multiple immunotherapies targeting NSCLC patients have been launched and the new NRDL including anti-cancer medications for NSCLC has been implemented in China after 2017, causing a change in patients’ costs and imposing the importance of updating related research. Secondly, existing research has not focused on the specific hospitalization costs of different pathological types, and has not identified and analyzed the influencing factors of costs among patients with squamous carcinoma and non-squamous carcinoma respectively( 24 ). Lastly, the related studies were mostly based on claimed data or small sample from a single-center( 25 ), and there is a lack of information regarding the hospitalization costs of advanced NSCLC patients based on real-world EMR data from multi-centers with more diverse hospital types including comprehensive hospitals and specialized hospitals. The aim of this study is to describe the hospitalization costs and structure of advanced NSCLC patients in China, identify potential influencing factors, and analyze the impact of pathological types on hospitalization costs and the influencing factors of hospitalization costs among advanced NSCLC patients with squamous carcinoma and non-squamous carcinoma. Methods Study design To provide a real-world cost representation of the hospitalization cost of advanced NSCLC patients, we performed a hospital-based retrospective study. Two kinds of centers were included, which were a general hospital and a specialized hospital in Jiangsu province China, providing a diversity of institution types. All the NSCLC patients’ data from multiple centers’ EMR between 1 January 2017 and 31 December 2020 were collected anonymously to gain a relatively long period of patients’ costs. This study protocol was approved by the institutional review board, Public Health School of Fudan University (IRB00002408 & FWA00002399), and all data were anonymized which not require informed consent from all patients. Study participants In each center, the real-world data from EMR were screened according to the following inclusion and exclusion criteria. Inclusion criteria were patients who were ( 1 ) histologically or cytologically confirmed with locally advanced or metastatic NSCLC (stage IIIB, IIIC, or IV) and ( 2 ) without EGFR/ALK mutation. Exclusion criteria were patients ( 1 ) with lung metastases from tumors elsewhere in the body, ( 2 ) combined with other serious systemic diseases, and ( 3 ) only utilized one hospitalization during three years to avoid potential bias. A total of 5362 patients were included in this study. Data collection The real-world data were extracted from the electronic medical records in each center, providing detailed information about four categories: ( 1 ) Sociodemographic characteristics: age, gender, marital status, ethnic group, occupation types, and health insurance. ( 2 ) Disease-related characteristics: pathological types, clinical stages, and gene drive (excluding EGFR/ALK mutation). ( 3 ) Healthcare service utilization: hospital type, referral, department, hospitalization frequency, and average length of hospital stay. ( 4 ) Hospitalization costs: drug costs, inspection and testing costs, and other costs. Measures The dependent variable is the hospitalization costs per capita, which comprises the following costs during hospitalization: ( 1 ) drug costs: Western medicine fee, Chinese medicine fee, antibacterial drug fee, albumin products fee, globulin products fee, coagulation factor product fee, cytokines product fee, etc. ( 2 ) inspection and testing costs: pathology diagnosis fee, laboratory diagnosis fee, imaging diagnosis fee, clinical diagnosis fee, etc. ( 3 ) other costs: surgical treatment fee, anesthesia fee, operation fee, nursing fee, clinical physical therapy fee, rehabilitation fee, transfusion fee, disposable medical materials fee, non-surgical treatment fee, other treatment fees, etc. Hence, the hospitalization costs per capita were computed by this formula: Cost hospitalization = Cost drug + Cost inspection and testing + Cost other The data on related costs were collected in Chinese yuan, and we converted those into US dollars in 2023. Firstly, we discounted the costs four times from 2018 to 2022 annually, using the Consumer Price Index for medical care in China (1.024% in 2019, 1.018% in 2020, 1.004% in 2021, and 1.006% in 2022). Then, we converted the costs into US dollars using the average annual exchange rate of 6.7261 yuan per dollar in 2022 from China Foreign Exchange Trade System. The independent variable we mostly focused on is pathological types, which were divided into the squamous carcinoma group and the non-squamous carcinoma group. Other control variables were ( 1 ) Sociodemographic characteristics: age (under and above 60 years old), gender (male/female), marital status (married/other status), ethnic group (Han Chinese/other minorities), occupation types (employee/farmer/retiree/other occupations), and health insurance (Urban Employee Basic Medical Insurance, also including free medical service/ Urban and Rural Residents Basic Medical Insurance/other insurance besides of basic insurance/no insurance including who are not enrolled in any health insurance program as well as those who are unable to receive insurance reimbursement due to seeking medical treatment outside their registered region). ( 2 ) Disease-related characteristics: clinical stages (IIIB&IIIC/IV), and gene drive (including HER2, KRAS, BRAF, BRCA1/2, ATM, TP53, RET, and TET1 and excluding EGFR/ALK mutation). ( 3 ) Healthcare service utilization: hospital type (general hospital/ specialized hospital), referral (yes/no), department (the oncology department/ the respiratory department/other department), hospitalization frequency (times), and average length of hospital stay (days). All the variables were categorical variables except for hospitalization frequency and average length of hospital stay. Statistical analysis Baseline patient characteristics were described using frequency (%) for categorical variables and mean (± SD) for continuous variables. The costs were presented by the median and the Interquartile Range (IQR). To compare the hospitalization among different pathological types, the Wilcoxon rank-sum (Mann-Whitney) test was performed due to abnormal distribution and heterogeneity of variance. To explore the predictors of the hospitalization costs for advanced NSCLC, the generalized linear model (GLM) with a gamma distribution and log link function was applied in three models: ( 1 ) Model 1 was conducted to test the significance of pathological types after controlling other variables. ( 2 ) Model 2 and Model 3 were used to identify the influencing factors of the hospitalization costs in the squamous carcinoma group and the non-squamous carcinoma group, respectively. The statistical analyses were performed in STATA 17.0 with significant level was 0.05 (two sides). Results Sample characteristic As Table 1 , a total of 5362 patients with advanced NSCLC was included in this study. Most of them were over 60 years old (59.27%), male (64.88%), married (98.06%), and Han population (99.81%). 22.45% and 28.09% of them were employees and farmers, and 23.76% of them were retired. Only 27.73 of them weren’t covered by health insurance when they accepted treatment and half of them had basic health insurance (38.33% UEBMI and 22.92% URRBMI). As for disease-related characteristics, most of them were at stage IV (85.30%) and 743 patients had gene drive (13.86%). Regarding healthcare service utilization, 57.81% of them went to specialized hospitals and the other 42.19% of them went to general hospitals with 1.79% of them being transferred. Over half of them accepted treatment in the oncology department (52.28%) and 16.30% of them in the respiratory department. The mean hospitalization frequency was 7.85 times and the average length of hospital stay was 6.13 days. For different pathological types, 1138 patients were diagnosed with squamous carcinoma, and 4224 patients were diagnosed with non-squamous carcinoma. Compared to patients with non-squamous carcinoma, patients with squamous carcinoma were more likely to be over 60 years old, male, married, Han population, and farmers with no insurance. Less of the patients with squamous carcinoma were at stage IV (71.35% vs 89.06%), and a smaller proportion of them were treated at general hospital (37.70% vs 43.39%) and in the oncology department (47.28% vs 53.62%) than patients with non-squamous carcinoma. Patients with squamous carcinoma had fewer hospitalizations (6.90 times vs 8.11 times), while they had longer hospital stays (6.99 days vs 4.34 days) than the other pathological type. Table 1 Sample characteristic among patients with advanced NSCLC Variables Total n = 5362 (%) Squamous carcinoma n = 1138 (%) Non-squamous carcinoma n = 4224 (%) Age (years) 60 3178 (59.27) 828 (72.76) 2350 (55.63) Gender Male 3479 (64.88) 994 (87.35) 2485 (58.83) Female 1883 (35.12) 144 (12.65) 1739 (41.17) Marital status Married 5258 (98.06) 1121 (98.51) 4137 (97.94) Others 104 (1.94) 17 (1.49) 87 (2.06) Ethic group Han Chinese 5352 (99.81) 1138 (99.91) 4215 (99.79) Others 10 (0.19) 1 (0.09) 9 (0.21) Occupation type Employee 1204 (22.45) 203 (17.84) 1001 (23.70) Farmer 1506 (28.09) 402 (35.33) 1104 (26.14) Retiree 1274 (23.76) 275 (24.17) 999 (23.65) Others 1378 (25.70) 258 (22.67) 1120 (26.52) Health insurance* UEBMI 2055 (38.33) 382 (33.57) 1673 (39.61) URRBMI 1229 (22.92) 310 (27.24) 919 (21.76) Others 591 (11.02) 102 (8.96) 489 (11.58) No insurance 1487 (27.73) 344 (30.23) 1143 (27.06) Clinical stage IIIB&IIIC 788 (14.07) 326 (28.65) 462 (10.94) IV 4574 (85.30) 812 (71.35) 3762 (89.06) Gene Drive† Yes 743 (13.86) 28 (2.46) 715 (16.93) No 4619 (86.14) 1110 (97.54) 3509 (83.07) Hospital type General hospital 2262 (42.19) 429 (37.70) 1833 (43.39) Specialized hospital 3100 (57.81) 709 (62.30) 2391 (56.61) Referral Yes 96 (1.79) 27 (2.37) 69 (1.63) No 5266 (98.21) 1111 (97.63) 4155 (98.37) Department Oncology 2803 (52.28) 538 (47.28) 2265 (53.62) Respiratory 874 (16.30) 190 (16.70) 684 (16.19) Others 1685 (31.42) 410 (36.03) 1275 (30.18) Hospitalization frequency (times) 7.85 (6.19) 6.90 (5.43) 8.11 (6.35) Average length of hospital stay (days) 6.13 (4.56) 6.99 (5.20) 5.90 (4.34) * UEBMI, Urban Employee Basic Medical Insurance, also including free medical service; URRBMI, Urban and Rural Residents Basic Medical Insurance; Others, health insurance besides of basic medical insurance. No insurance including who are uninsured and unable to claim insurance reimbursement due to cross-regional medical care. †Positive driver genes include HER2, KRAS, BRAF, BRCA1/2, ATM, TP53, RET, and TET1,etc. The hospitalization cost of advanced NSCLC patients As shown in Fig. 1, the median hospitalization cost of advanced NSCLC patients was $ 17,254 per capita (IQR= $ 20,300). Among the three main components, the highest cost was drug costs (median= $ 10,160) and the median of the inspection and testing costs was $ 2,252. Additionally, the median of other costs was $ 3,489. The result of the Wilcoxon rank-sum test shows that there was a significant difference between the hospitalization cost of advanced NSCLC patients with squamous carcinoma and with non-squamous carcinoma (Z=-5.792, p < 0.001). The hospitalization cost of advanced NSCLC patients with non-squamous carcinoma was $ 18,003 (IQR= $ 21,372), which was higher than the $ 15,024 (IQR= $ 16,789) of patients with squamous carcinoma. Specifically, all three components, drug costs, inspection and testing costs, and other costs, were higher in the squamous carcinoma group than in the non-squamous carcinoma group. Figure 1. Hospitalization cost for advanced NSCLC per capita, by pathological type, USD. Influencing factors of the hospitalization cost associated with different pathological type The results of GLM with a gamma distribution are presented in Table 2 . In model 1, after controlling the related factors, the pathological type was a significant influencing factor and compared to the squamous carcinoma group, the non-squamous carcinoma group had the higher hospitalization cost (β = 0.098, 95%CI: 0.066,0.130). Also, it was observed that patients who were in older age (β=-0.038, 95%CI: -0.064, -0.010), female (β=-0.037, 95%CI: -0.064, -0.010), farmers (β=-0.057, 95%CI: -0.095, -0.018), and with no insurance (β=-0.150, 95%CI: -0.184, -0.116) had significantly lower hospitalization cost. Furthermore, patients who were treated in specialized hospitals (β = 0.118, 95%CI: 0.082,0.154), and in other departments (β = 0.054, 95%CI: 0.024,0.084) rather than in oncology and in respiratory, were more likely to spend more during hospitalization. When patients utilized more hospitalization (β = 0.104, 95%CI: 0.101,0.107) and had a longer hospital stay (β = 0.050, 95%CI: 0.046,0.053), the hospitalization cost was significantly higher. Among patients with squamous carcinoma in model 2, those who were not insured (β=-0.196, 95%CI: -0.013, -0.323) had significantly lower hospitalization costs. The significant factors in model 1, age, gender, occupation type, having URRBMI and other insurance, and hospital type became insignificant in model 2. Still, patients who were treated in other departments (β = 0.119, 95%CI: 0.058,0.180) with more hospitalization (β = 0.112, 95%CI: 0.105,0.118) and longer length of hospital stay (β = 0.056, 95%CI: 0.050,0.062), had the significantly higher hospitalization cost. It was newly found that patients who didn’t transfer hospitals spent more on hospitalization (β = 0.154, 95%CI: 0.013,0.323). Being similar with the results of model 1, the results of patients with squamous carcinoma in model 3 showed that the older age (β=-0.037, 95%CI: -0.066, -0.006), female (β=-0.044, 95%CI: -0.073, -0.016), farmers (β=-0.059, 95%CI: -0.103, -0.015), and having no insurance (β=-0.004, 95%CI: -0.002, -0.005) were the significant factors of the lower hospitalization cost, while having URRBMI (β = 0.067, 95%CI: 0.026,0.107) became a significant factor of the higher hospitalization cost. Consistent with model 1 and model 2, treatment in other departments (β = 0.035, 95%CI: 0.001,0.069), more hospitalization (β = 0.102, 95%CI: 0.099,0.105), and longer length of hospital stay (β = 0.048, 95%CI: 0.044,0.052)were significantly associated with the higher hospitalization cost. Table 2 Multiple factor analysis of the hospitalization cost associated with different pathological type Variables Model 1 # Model 2 Model 3 β 95%CI β 95%CI β 95%CI Pathological type Squamous carcinoma - - - - Non-squamous carcinoma 0.098*** (0.066,0.130) - - - - Age (years) 60 -0.038** (-0.064, -0.010) -0.045 (-0.104,0.015) -0.037** (-0.066, -0.006) Gender Male Female -0.037** (-0.064, -0.010) 0.002 (-0.063,0.081) -0.044** (-0.073, -0.016) Marital status Married Others -0.074 (-0.163,0.014) -0.060 (-0.268,0.147) -0.075 (-0.173,0.022) Ethic group Han Chinese Others 0.324** (0.043,0.606) -0.364 (-1.210,0.483) 0.375** (0.075,0.675) Occupation type Employee Farmer -0.057*** (-0.095, -0.018) -0.038 (-0.118,0.042) -0.059** (-0.103, -0.015) Retiree -0.029 (-0.067,0.009) -0.078 (-0.159,0.003) -0.016 (-0.059,0.027) Others -0.001** (-0.0008,0.0018) -0.029 (-0.111,0.052) -0.050** (-0.090, -0.010) Health insurance UEBMI URRBMI 0.065*** (0.030,0.101) 0.053 (-0.020,0.125) 0.067*** (0.026,0.107) Others 0.063** (0.016,0.109) -0.011 (-0.118,0.097) 0.079*** (0.027,0.131) No insurance -0.150*** (-0.184, -0.116) -0.196*** (-0.013, -0.323) -0.004*** (-0.002, -0.005) Clinical stage IIIB&IIIC IV -0.007 (-0.043,0.028) -0.017 (-0.073,0.039) -0.002 (-0.047,0.042) Gene Drive Yes No -0.007 (-0.043,0.028) -0.054 (-0.224,0.115) -0.023 (-0.068,0.020) Hospital type General hospital Specialized hospital 0.118*** (0.082,0.154) 0.021** (-0.053,0.096) 0.148*** (0.107,0.190) Referral Yes No 0.053 (-0.040,0.147) 0.154* (0.013,0.323) -0.0019 (-0.004,0.117) Department Oncology Respiratory 0.001 (-0.038,0.040) 0.053 (-0.031,0.138) -0.010 (-0.054,0.034) Others 0.054*** (0.024,0.084) 0.119*** (0.058,0.180) 0.035** (0.001,0.069) Hospitalization frequency (times) 0.104*** (0.101,0.107) 0.112*** (0.105,0.118) 0.102*** (0.099,0.105) Average length of hospital stay (days) 0.050*** (0.046,0.053) 0.056*** (0.050,0.062) 0.048*** (0.044,0.052) _cons 8.612*** (8.546,8.686) 8.612*** (8.411,8.814) 8.727*** (8.653,8.802) n 5,362 1,138 4,224 #: Model 1 is the multiple factor analysis of the hospitalization cost. Model 2 and Model 3 is the multiple factor analysis of the hospitalization costs in the squamous carcinoma group and the non-squamous carcinoma group, respectively. *p < 0.1, **p < 0.05, ***p < 0.001 Discussion This study thoroughly outlines the hospitalization costs of advanced NSCLC in China based on multicenter real-world data, and initially scrutinized the impact of pathological categories on the inpatient expenses and their influencing factors for advanced NSCLC sufferers. The hospitalization cost of advanced NSCLC patients was $ 17,254 per capita, with drug costs as the highest cost. There is a significant difference between the hospitalization cost of advanced NSCLC patients with squamous carcinoma ( $ 18,003) and with non-squamous carcinoma ( $ 15,024). The factors influencing the hospitalization costs also by different pathological types are found. The hospitalization cost imposes an enormous burden on advanced NSCLC patients in China. In 2022, the Gross Domestic Product (GDP) was $ 12,891 per capita and the disposable income was $ 5,410 per capita( 26 ), however, the hospitalization cost of advanced NSCLC patients per capita was over three times the disposable income per capita and even higher than the GDP per capita. For the three main components of total costs, the highest cost was drug costs (median= $ 10,160), which was also two times the disposable income per capita and accounted for 78% of the total hospitalization cost. For the hospitalization costs in other countries, it was reported that the hospitalization/emergency costs among stage IB-IIIA NSCLC Germany patients were approximately $ 8,994 per capita in 2018( 18 ). It can’t be ignored that the hospitalization cost was higher than this in Germany reflecting the considerable economic burden for NSCLC patients and associating with two potential reasons: firstly, the differences in clinical guidelines, pathways, and practices between the two countries, and secondly, variations in medical insurance payment models. Germany has already implemented Diagnosis-Related Groups (DRGs) payment for lung cancer, while China is without the DRGs full implementation at the time of this study potentially resulting in higher costs( 27 ). For the hospitalization costs of other cancers, the cost of advanced NSCLC patients was still higher than that of colorectal cancer( 28 ), prostate cancer( 29 ), and oral cancer( 30 ), which may be influenced by the characteristics of the disease and available innovative drugs in recent years. Lung cancer represents the leading cause of mortality and imposes a substantial economic burden in China, and both the disease burden and economic impact are particularly high, necessitating special attention and focused efforts. Intriguingly, the results of GLM show the pathological type is a significant influencing factor in hospitalization costs. A significant difference was found between the hospitalization cost of advanced NSCLC patients with squamous carcinoma and those with non-squamous carcinoma (Z=-5.792, p < 0.001). The hospitalization cost of advanced NSCLC patients with non-squamous carcinoma ( $ 18,003) was higher than that of patients with squamous carcinoma ( $ 15,024). On the one hand, the different pathological types may have an impact on the expression of blood telomerase activity, the specific symptoms, the treatment regimens, and the prognosis after the operation( 31 , 32 ). On the other hand, a higher number of innovative pharmaceuticals have been approved for non-squamous carcinoma patients than squamous carcinoma in China, such as immunotherapies (e.g., camrelizumab). Consequently, this increase in approved quantities potentially leads to elevated expenditures in both drug costs and overall hospitalization costs. Besides, the higher direct non-medical cost among advanced NSCLC non-squamous carcinoma patients in good health and the significant difference between the different pathological subtypes in China were also found( 33 ). Given that, the total financial spending of advanced NSCLC patients with non-squamous carcinoma is heavy, requiring multiple measures to reduce the economic burden. Furthermore, we analyzed the influencing factors of the hospitalization costs among advanced NSCLC patients with squamous carcinoma and non-squamous carcinoma, respectively. Commonly, patients in the two groups with no insurance, who are uninsured or unable to claim insurance reimbursement due to cross-regional medical care, spent less during hospitalization. It reflects that the absence of medical insurance may impede physicians' appropriate treatment due to considerations of patients' affordability and force them shift toward generic drugs prescription instead of innovative medications, and the high out-of-pocket expense may also restrain the abundant utilization of healthcare and the acceptance of expensive drugs or treatments for patients( 7 , 8 ). Then, the hospital type was a significant factor in two groups. Compared to general hospitals, patients hospitalized in specialized hospitals had higher costs, potentially indicating that more innovative but expensive treatments are more popularized in specialized hospitals( 34 ). Also, patients who received treatment in other departments rather than in oncology and in respiratory were more likely to spend more during hospitalization, which may explain that the professional treatment is beneficial to cost-saving( 5 ). Non-squamous carcinoma patients exhibit greater differences in coefficients and bigger impact on costs across different hospital types and departments compared to the squamous carcinoma patients. Lastly, the hospitalization frequency and the average length of hospital stay were potential predictive factors because these factors reflect the severity of the disease and the pattern of utilization( 35 ). The more intensive hospitalization and the longer the hospital stay, the higher the hospitalization costs among advanced patients with squamous carcinoma or with non-squamous carcinoma( 36 ). Considering the higher cost of patients with non-squamous carcinoma, we further explored the other different influencing factors for two pathological types. It was found that patients with non-squamous carcinoma who were older, female, and farmers had fewer costs during hospitalization indicating that the deprived population had lower medical expenses, however these factors are not significant among patients with squamous carcinoma. Non-squamous carcinoma patients face greater challenges related to the higher treatment costs, more affected by their economic status, affordability, and treatment adherence. Older patients may prematurely discontinue treatment due to the heavy financial burden, while male patients exhibit poorer treatment adherence compared to their counterparts. Moreover, patients from different occupational backgrounds exhibit variations in treatment choices and adherence due to differences in economic status and affordability, especially facing higher costs maybe beyond their willingness to pay. This study has profound policy implications. Firstly, given the heavy burden of NSCLC, it is necessary to reduce the disease burden of advanced NSCLC patients through multiple channels, such as increasing the reimbursement ratio of basic health insurance, expanding the list of drug reimbursements, providing social donations and aids, and calling on enterprises to donate innovative drugs. Secondly, the decision-makers should pay attention to patients with advanced non-squamous carcinoma NSCLC and provide policy support at the government level, such as major illness subsidies to promote universal coverage and health equity. Lastly, in practice, medical staffs should identify and follow up with patients who have been hospitalized multiple times or for long periods to adjust the suitable treatment. Also, the future studies could analyze the composition of the structure and change of high costs to figure out the reasons and optimize future treatment. Our research has several limitations. On the one hand, the data source of this study is from the economically developed region in eastern China, which may overestimate the hospitalization costs. However, this study could objectively reflect the overall situation, because in China lung cancer treatment mainly follows guidelines, and most drugs have achieved uniform national pricing through centralized procurement and national reimbursement drug negotiation, which is the main driver of total costs. There may be differences in the inspection and testing costs and other costs in different regions so it should be extrapolated nationally with caution. On the other hand, this study adopted the retrospective research design. Although the EMR data is relatively accurate real-world data, it lacks more comprehensive personal information such as patient income, social support, and health status, which can be further collected and analyzed in the future. Conclusion This study reports on real-world data from multiple clinical centers in China, describing the hospitalization costs and structure for advanced NSCLC patients. The results identify pathological type as a significant predictive factor, and analyze the impact of pathological types on hospitalization costs and the factors influencing hospitalization costs for advanced NSCLC patients with squamous and non-squamous carcinoma respectively. As far as we know, this is the first study in China that analyzed the hospitalization cost of advanced NSCLC patients with different pathological types to update basic information on disease economic burden for future relevant study and provide policy reference for medical reimbursement. Declarations Funding Declaration No funding was received in this study. Ethics approval Ethics approval was granted by the institutional review board, Public Health School of Fudan University (IRB00002408 & FWA00002399) and has been conducted in compliance with the Helsinki Declaration (1996). Author Contribution P.Z. conceptualized the study. Y.Y. collected and analyzed the data. All authors wrote the main manuscript text, prepared tables and figures, reviewed and revised the manuscript. References Zheng R, Zhang S, Zeng H, Wang S, Sun K, Chen R, et al. Cancer incidence and mortality in China, 2016. Journal of the National Cancer Center. 2022 2022-1-1;2(1):1–9. UK CR. Cancer Incidence Statistics 2020.; 2023. McPherson INAG. The progression of non-small cell lung cancer from diagnosis to surgery. European Journal of Surgical Oncology: The Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. 2020;46(10Pta1). Zeng H, Ran X, An L, Zheng R, He J. Disparities in stage at diagnosis for five common cancers in China: a multicentre, hospital-based, observational study. The Lancet Public Health. 2021;6(12):e877-87. Yang Y, Xia Y, Su C, Chen J, Long E, Zhang H, et al. Measuring the indirect cost associated with advanced non-small cell lung cancer: a nationwide cross-sectional study in China. J CANCER RES CLIN. 2022:1–10. Migliorino MR, Santo A, Romano G, Cortinovis D, Galetta D, Alabiso O, et al. Economic burden of patients affected by non-small cell lung cancer (NSCLC): the LIFE study. Journal of Cancer Research & Clinical Oncology. 2017;143(5):1–9. Darbà J, Marsà A. The cost of lost productivity due to premature lung cancer-related mortality: results from Spain over a 10-year period. BMC CANCER. 2019;19. Cherny N, Sullivan R, Torode J, Saar M, Eniu A. ESMO European Consortium Study on the availability, out-of-pocket costs and accessibility of antineoplastic medicines in Europe. ANN ONCOL. 2016;27(8):1423–43. Cicin I, Oksuz E, Karadurmus N, Malhan S, Gumus M, Yilmaz U, et al. Economic burden of lung cancer in Turkey: a cost of illness study from payer perspective. HEALTH ECON REV. 2021;11(1). Wood R, Taylor-Stokes G. Cost burden associated with advanced non-small cell lung cancer in Europe and influence of disease stage. BMC CANCER. 2019;19. Migliorino MR, Santo A, Romano G, Cortinovis D, Galetta D, Alabiso O, et al. Economic burden of patients affected by non-small cell lung cancer (NSCLC): the LIFE study. Journal of Cancer Research & Clinical Oncology. 2017;143(5):1–9. Cheng YI, Gan YC, Liu D, Davies MPA, Field JK. Potential genetic modifiers for somatic EGFR mutation in lung cancer: A meta-Analysis and literature review. BMC CANCER. 2019;19(1). Singhi EK, Horn L, Sequist LV, Heymach J, Langer CJ. Advanced Non–Small Cell Lung Cancer: Sequencing Agents in the EGFR-Mutated/ALK-Rearranged Populations. American Society of Clinical Oncology (ASCO); 2019; 2019. p. e187-97. Pennell NA, Arcila ME, Gandara DR, West H. Biomarker Testing for Patients With Advanced Non–Small Cell Lung Cancer: Real-World Issues and Tough Choices.; 2019; 2019. p. 531 – 42. Lindeman NI, Cagle PT, Aisner DL, Arcila ME, Yatabe Y. Updated Molecular Testing Guideline for the Selection of Lung Cancer Patients for Treatment With Targeted Tyrosine Kinase Inhibitors. J THORAC ONCOL. 2018;13(3). People's Republic of China. List of medicines for national basic medical insurance, Work-related injury insurance and Maternity insurance.; 2019. TÜRK M, YILDIRIM F, YURDAKUL AS, ÖZTÜRK C. Hospitalization costs of lung cancer diagnosis in Turkey: Is there a difference between histological types and stages? Tuberkuloz ve Toraks. 2016 2016-12-28;64(4):263–8. Andreas S, Chouaid C, Danson S, Siakpere O, Benjamin L, Ehness R, et al. Economic burden of resected (stage IB-IIIA) non-small cell lung cancer in France, Germany and the United Kingdom: A retrospective observational study (LuCaBIS). LUNG CANCER. 2018;124:298–309. Bremner KE, Krahn MD, Warren JL, Hoch JS, Barrett MJ, Liu N, et al. An international comparison of costs of end-of-life care for advanced lung cancer patients using health administrative data. PALLIATIVE MED. 2015;29(10):918–28. Perrone F, Benelli G, Lopatriello S, Portalone L, Salvati F, Zorat P, et al. Cost of non-small cell lung cancer in Italy. Results of the longitudinal study ALCEA (Advanced Lung Cancer Economic Assessment). Journal of Clinical Oncology Official Journal of the American Society of Clinical Oncology. 2004;22(14_suppl):8265. Apple J, DerSarkissian M, Shah A, Chang R, Chen Y, He X, et al. Economic burden of early-stage non-small-cell lung cancer: an assessment of healthcare resource utilization and medical costs. J COMP EFFECT RES. 2023 2023-8-31;11(2):123–37. Lin HM, Pan X, Hou P, Huang H, Wu Y, Ren K, et al. Economic burden in patients with ALK + non-small cell lung cancer, with or without brain metastases, receiving second-line anaplastic lymphoma kinase (ALK) inhibitors. J MED ECON. 2020;2(12):1–12. Grumberg V, Chouaid C, Cotte FE, Jouaneton B, Jolivel R, Gaudin AF, et al. Long-term hospital resource utilization and associated costs of care for patients initiating nivolumab in advanced non-small cell lung cancer in France. J MED ECON. 2022 2022-1-1;25(1):691–9. Li-Ming L, Wei C, Hong Y, Li-Ying Z, Yan J, Xuan Z, et al. Hospitalization Expenses and Influencing Factors for Patients with Lung Cancer in Beijing. China Cancer. 2019;2(11):23–30. Li Z, Jiang S, He RB, Dong YH, Pan ZJ, Xu CZ, et al. Trajectories of Hospitalization Cost Among Patients of End-Stage Lung Cancer: A Retrospective Study in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2018 2018-1-1;15(12). The State Council of the People's Republic of China. Resident income and consumption expenditure in 2022.; 2023. Feng C, Mei-Yan JI, Yun LU. Germany's G-DRG Payment System and the Implications for China. Chinese Health Economics. 2016. Yuan GL, Liang LZ, Zhang ZF, Liang QL, Huang ZY, Zhang HJ, et al. Hospitalization costs of treating colorectal cancer in China A retrospective analysis. MEDICINE. 2019 2019-1-1;98(33). Yan B, Cai Y, Shen Z. Tendency Analysis of Average Hospitalization Cost of Prostate Cancer in China from 2013 to 2016. Chinese Journal of Health Statistics. 2018 2018-1-1;35(3):359 – 61, 367. Yang J, Wan SQ, Huang L, Zhong WJ, Zhang BL, Song J, et al. Analysis of hospitalization costs and length of stay for oral cancer patients undergoing surgery: Evidence from Hunan, China. ORAL ONCOL. 2021 2021-1-1;119. Hu J, Sun P, Li R. Correlation of pathological types and clinical stages of NSCLC with the blood telomerase activity. Chinese Journal of Experimental Surgery. 2004 2004-1-1;21(10):1257-8. Edwards T, Bishop P, Crosbie P, Booton R, Evison M, Al-Najjar H. Non-small cell lung cancer (NSCLC) pathological sub-typing in convex and radial endobronchial ultrasound (EBUS) specimens compared to surgical resection. EUR RESPIR J. 2016 2016-1-1;48. Xia Y, Chen Y, Chen J, Gan Y, Su C, Zhang H, et al. Measuring direct non-medical burden among patients with advanced non-small cell lung cancer in China: is there a difference in health status? FRONT PUBLIC HEALTH. 2023 2023-5-4;11. Jean RA, Bongiovanni T, Soulos PR, Chiu AS, Herrin J, Kim N, et al. Hospital Variation in Spending for Lung Cancer Resection in Medicare Beneficiaries. The Annals of Thoracic Surgery. 2019;108(6):1710–6. Hai-Hong H, Yun F, Zheng-Jun D. Influencing Factor Analysis on the Economic Burden of Internal Inpatients with Lung Cancer. Hospital Administration Journal of Chinese People's Liberation Army. 2012. Xiang Q YZLA. Analyzing the direct economic burden of hospitalized elderly poverty with chronic diseases in rural areas and its influencing factors. Chin Health Serv Manag. 2020;1(37):525–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3819071","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264891011,"identity":"80ee2547-efda-4abf-923d-f424b95188d1","order_by":0,"name":"Yi Yang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yang","suffix":""},{"id":264891012,"identity":"10fb9269-0d79-4e70-8a32-ae476538395c","order_by":1,"name":"Peng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYLACxgYgwd7AcADGJlILzwGGAwdI0yKRwMBAlBZz9t7DL3/usMuTj3z+8PAHBhvZDQeYnz3Ap8Wy51yaheSZ5GLD2zkGQIelGW84wGZugE+LwY0cMwPDNubEjbNzQH45nLjhAA+bBF4t99+YGSS21SdunHn8AVDLfyK03OAxfnCw7XDifAkGkMMOENZi2ZNjxtjYdjxxAw/QL2cMko1nHmYzw6vFnP2M8cefbdWJ89uPP/5QUWEn23e8+Rl+hzEwQJxhcADKZWDGpx6ihvkDiCHfQEDlKBgFo2AUjFwAAHgqVHXMh2Y0AAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-12-29 01:59:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3819071/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3819071/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49238601,"identity":"f65e1ddb-b1fd-44f5-966a-e5910e6a91ed","added_by":"auto","created_at":"2024-01-05 18:12:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61812,"visible":true,"origin":"","legend":"\u003cp\u003eHospitalization cost for advanced NSCLC per capita, by pathological type, USD.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3819071/v1/07d1afa7f44ed8ce50c0238d.png"},{"id":51631870,"identity":"6fa5ff44-8f9b-4b43-8a66-14514b7e6b57","added_by":"auto","created_at":"2024-02-26 09:45:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3819071/v1/fa6fd031-47ed-4372-944d-ef252c4e3008.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does pathologic type shape the hospitalization costs of advanced non- small cell lung cancer patients? A multicenter real-world data study","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer is the most frequently diagnosed cancer in China accounting for over 20% of all new cancer cases, which also causes the most common death from cancer (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is estimated that 85\u0026ndash;90% of lung cancer patients present with non-small cell cancer (NSCLC)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A majority of patients are diagnosed with advanced or metastatic NSCLC(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) resulting in poor prognosis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and heavy disease burden due to the negative impact on disability, poor health-related quality of life, and significant financial burden (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCost for lung cancer patients, especially the hospitalization cost, is widely proven as the key factor in restraining adequate health utilization, equitable access to innovative treatment, and suitable drug use(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Globally, higher medical cost among NSCLC patients is found than among SCLC patients(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), especially among NSCLC patients with the negative EGFR mutation or ALK(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). NSCLC patients with the negative EGFR mutation or ALK rearrangement may have a bleaker prognosis and heavier disease burden because they lack the two most sensitized and treatable driver mutations(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It is necessary to pay attention to NSCLC patients at stage IIIB, IIIC, or IV, without EGFR/ALK mutation who are less benefited from targeted therapy and more suffered physically and economically(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To effectively alleviate the economic burden of cancer patients in China, more than 10 anti-cancer medications for NSCLC were featured in the National Reimbursement Drug List (NRDL) in 2017, and the prices were slashed by over 50%(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Thus, the impact of this policy on NSCLC patients\u0026rsquo; medical costs needs to be observed in the following years. At the same time, the hospitalization cost is the greatest part of all costs as the main cost driver relating to advanced or metastatic NSCLC because they accepted a majority of treatment during hospitalization(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). By analyzing the hospitalization cost of NSCLC patients with the negative EGFR/ALK mutation, we could provide basic information for researchers to make health economic evaluations and for decision-makers to optimize related policies.\u003c/p\u003e \u003cp\u003eRecent studies have investigated the hospitalization costs associated with NSCLC in various countries. In Italy, a study in 2017 found the economic burden of NSCLC is extremely high with an overall healthcare cost of \u003cspan\u003e$\u003c/span\u003e25,859 per patient(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and another study found the highest direct costs were determined by hospitalizations(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In the United States, the average hospitalization cost for stage IB\u0026ndash;IIIA NSCLC patients was \u003cspan\u003e$\u003c/span\u003e4,652 per month in 2021 USD dollars, with higher costs driven by higher NSCLC-related hospitalization rates(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In Germany, among stage IB-IIIA NSCLC patients, the largest cost drivers were associated with hospitalization/emergency costs of approximately \u003cspan\u003e$\u003c/span\u003e8,994 per capita in 2018(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The studies also found that with the emergency of immunotherapy medication and inhibitor therapy, a high economic burden occurred(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). For example, annual hospitalization costs per capita for NSCLC patients in France treated with nivolumab was \u003cspan\u003e$\u003c/span\u003e10,309 in 2019, while it decreased over time(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the existing research has the following shortcomings. Firstly, to the best of our knowledge, there is no Chinese literature on the hospitalization costs of late-stage NSCLC patients, and global studies also mainly focused on early-stage NSCLC patients. There is room to provide new information about the hospitalization cost of advanced NSCLC patients, because multiple immunotherapies targeting NSCLC patients have been launched and the new NRDL including anti-cancer medications for NSCLC has been implemented in China after 2017, causing a change in patients\u0026rsquo; costs and imposing the importance of updating related research. Secondly, existing research has not focused on the specific hospitalization costs of different pathological types, and has not identified and analyzed the influencing factors of costs among patients with squamous carcinoma and non-squamous carcinoma respectively(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Lastly, the related studies were mostly based on claimed data or small sample from a single-center(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and there is a lack of information regarding the hospitalization costs of advanced NSCLC patients based on real-world EMR data from multi-centers with more diverse hospital types including comprehensive hospitals and specialized hospitals.\u003c/p\u003e \u003cp\u003eThe aim of this study is to describe the hospitalization costs and structure of advanced NSCLC patients in China, identify potential influencing factors, and analyze the impact of pathological types on hospitalization costs and the influencing factors of hospitalization costs among advanced NSCLC patients with squamous carcinoma and non-squamous carcinoma.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eTo provide a real-world cost representation of the hospitalization cost of advanced NSCLC patients, we performed a hospital-based retrospective study. Two kinds of centers were included, which were a general hospital and a specialized hospital in Jiangsu province China, providing a diversity of institution types. All the NSCLC patients\u0026rsquo; data from multiple centers\u0026rsquo; EMR between 1 January 2017 and 31 December 2020 were collected anonymously to gain a relatively long period of patients\u0026rsquo; costs.\u003c/p\u003e \u003cp\u003eThis study protocol was approved by the institutional review board, Public Health School of Fudan University (IRB00002408 \u0026amp; FWA00002399), and all data were anonymized which not require informed consent from all patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eIn each center, the real-world data from EMR were screened according to the following inclusion and exclusion criteria. Inclusion criteria were patients who were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) histologically or cytologically confirmed with locally advanced or metastatic NSCLC (stage IIIB, IIIC, or IV) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) without EGFR/ALK mutation. Exclusion criteria were patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) with lung metastases from tumors elsewhere in the body, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) combined with other serious systemic diseases, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) only utilized one hospitalization during three years to avoid potential bias. A total of 5362 patients were included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe real-world data were extracted from the electronic medical records in each center, providing detailed information about four categories: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Sociodemographic characteristics: age, gender, marital status, ethnic group, occupation types, and health insurance. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Disease-related characteristics: pathological types, clinical stages, and gene drive (excluding EGFR/ALK mutation). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Healthcare service utilization: hospital type, referral, department, hospitalization frequency, and average length of hospital stay. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Hospitalization costs: drug costs, inspection and testing costs, and other costs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eThe dependent variable is the hospitalization costs per capita, which comprises the following costs during hospitalization: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) drug costs: Western medicine fee, Chinese medicine fee, antibacterial drug fee, albumin products fee, globulin products fee, coagulation factor product fee, cytokines product fee, etc. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) inspection and testing costs: pathology diagnosis fee, laboratory diagnosis fee, imaging diagnosis fee, clinical diagnosis fee, etc. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) other costs: surgical treatment fee, anesthesia fee, operation fee, nursing fee, clinical physical therapy fee, rehabilitation fee, transfusion fee, disposable medical materials fee, non-surgical treatment fee, other treatment fees, etc. Hence, the hospitalization costs per capita were computed by this formula:\u003c/p\u003e \u003cp\u003eCost \u003csub\u003ehospitalization\u003c/sub\u003e= Cost \u003csub\u003edrug\u003c/sub\u003e+ Cost \u003csub\u003einspection and testing\u003c/sub\u003e + Cost \u003csub\u003eother\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eThe data on related costs were collected in Chinese yuan, and we converted those into US dollars in 2023. Firstly, we discounted the costs four times from 2018 to 2022 annually, using the Consumer Price Index for medical care in China (1.024% in 2019, 1.018% in 2020, 1.004% in 2021, and 1.006% in 2022). Then, we converted the costs into US dollars using the average annual exchange rate of 6.7261 yuan per dollar in 2022 from China Foreign Exchange Trade System.\u003c/p\u003e \u003cp\u003eThe independent variable we mostly focused on is pathological types, which were divided into the squamous carcinoma group and the non-squamous carcinoma group. Other control variables were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Sociodemographic characteristics: age (under and above 60 years old), gender (male/female), marital status (married/other status), ethnic group (Han Chinese/other minorities), occupation types (employee/farmer/retiree/other occupations), and health insurance (Urban Employee Basic Medical Insurance, also including free medical service/ Urban and Rural Residents Basic Medical Insurance/other insurance besides of basic insurance/no insurance including who are not enrolled in any health insurance program as well as those who are unable to receive insurance reimbursement due to seeking medical treatment outside their registered region). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Disease-related characteristics: clinical stages (IIIB\u0026amp;IIIC/IV), and gene drive (including HER2, KRAS, BRAF, BRCA1/2, ATM, TP53, RET, and TET1 and excluding EGFR/ALK mutation). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Healthcare service utilization: hospital type (general hospital/ specialized hospital), referral (yes/no), department (the oncology department/ the respiratory department/other department), hospitalization frequency (times), and average length of hospital stay (days). All the variables were categorical variables except for hospitalization frequency and average length of hospital stay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline patient characteristics were described using frequency (%) for categorical variables and mean (\u0026plusmn;\u0026thinsp;SD) for continuous variables. The costs were presented by the median and the Interquartile Range (IQR). To compare the hospitalization among different pathological types, the Wilcoxon rank-sum (Mann-Whitney) test was performed due to abnormal distribution and heterogeneity of variance. To explore the predictors of the hospitalization costs for advanced NSCLC, the generalized linear model (GLM) with a gamma distribution and log link function was applied in three models: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Model 1 was conducted to test the significance of pathological types after controlling other variables. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Model 2 and Model 3 were used to identify the influencing factors of the hospitalization costs in the squamous carcinoma group and the non-squamous carcinoma group, respectively. The statistical analyses were performed in STATA 17.0 with significant level was 0.05 (two sides).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristic\u003c/h2\u003e \u003cp\u003eAs Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 5362 patients with advanced NSCLC was included in this study. Most of them were over 60 years old (59.27%), male (64.88%), married (98.06%), and Han population (99.81%). 22.45% and 28.09% of them were employees and farmers, and 23.76% of them were retired. Only 27.73 of them weren\u0026rsquo;t covered by health insurance when they accepted treatment and half of them had basic health insurance (38.33% UEBMI and 22.92% URRBMI). As for disease-related characteristics, most of them were at stage IV (85.30%) and 743 patients had gene drive (13.86%). Regarding healthcare service utilization, 57.81% of them went to specialized hospitals and the other 42.19% of them went to general hospitals with 1.79% of them being transferred. Over half of them accepted treatment in the oncology department (52.28%) and 16.30% of them in the respiratory department. The mean hospitalization frequency was 7.85 times and the average length of hospital stay was 6.13 days.\u003c/p\u003e \u003cp\u003eFor different pathological types, 1138 patients were diagnosed with squamous carcinoma, and 4224 patients were diagnosed with non-squamous carcinoma. Compared to patients with non-squamous carcinoma, patients with squamous carcinoma were more likely to be over 60 years old, male, married, Han population, and farmers with no insurance. Less of the patients with squamous carcinoma were at stage IV (71.35% vs 89.06%), and a smaller proportion of them were treated at general hospital (37.70% vs 43.39%) and in the oncology department (47.28% vs 53.62%) than patients with non-squamous carcinoma. Patients with squamous carcinoma had fewer hospitalizations (6.90 times vs 8.11 times), while they had longer hospital stays (6.99 days vs 4.34 days) than the other pathological type.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristic among patients with advanced NSCLC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;5362 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquamous carcinoma\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1138 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-squamous carcinoma\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4224 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2184 (40.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (27.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1874 (44.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3178 (59.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e828 (72.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2350 (55.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3479 (64.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e994 (87.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2485 (58.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1883 (35.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (12.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1739 (41.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5258 (98.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1121 (98.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4137 (97.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (2.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan Chinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5352 (99.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1138 (99.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4215 (99.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1204 (22.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (17.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1001 (23.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1506 (28.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402 (35.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1104 (26.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetiree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1274 (23.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275 (24.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e999 (23.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1378 (25.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (22.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1120 (26.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUEBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2055 (38.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382 (33.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1673 (39.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURRBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1229 (22.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (27.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e919 (21.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (8.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e489 (11.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1487 (27.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344 (30.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1143 (27.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIIB\u0026amp;IIIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e788 (14.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e326 (28.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e462 (10.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4574 (85.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e812 (71.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3762 (89.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Drive\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e743 (13.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e715 (16.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4619 (86.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1110 (97.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3509 (83.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2262 (42.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429 (37.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1833 (43.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialized hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3100 (57.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e709 (62.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2391 (56.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5266 (98.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1111 (97.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4155 (98.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2803 (52.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e538 (47.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2265 (53.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e874 (16.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190 (16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e684 (16.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1685 (31.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410 (36.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1275 (30.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHospitalization frequency (times)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.85 (6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.11 (6.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage length of hospital stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.13 (4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.99 (5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.90 (4.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* UEBMI, Urban Employee Basic Medical Insurance, also including free medical service; URRBMI, Urban and Rural Residents Basic Medical Insurance; Others, health insurance besides of basic medical insurance. No insurance including who are uninsured and unable to claim insurance reimbursement due to cross-regional medical care.\u003c/p\u003e \u003cp\u003e\u0026dagger;Positive driver genes include HER2, KRAS, BRAF, BRCA1/2, ATM, TP53, RET, and TET1,etc.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe hospitalization cost of advanced NSCLC patients\u003c/h2\u003e \u003cp\u003e As shown in Fig.\u0026nbsp;1, the median hospitalization cost of advanced NSCLC patients was \u003cspan\u003e$\u003c/span\u003e17,254 per capita (IQR=\u003cspan\u003e$\u003c/span\u003e20,300). Among the three main components, the highest cost was drug costs (median=\u003cspan\u003e$\u003c/span\u003e10,160) and the median of the inspection and testing costs was \u003cspan\u003e$\u003c/span\u003e2,252. Additionally, the median of other costs was \u003cspan\u003e$\u003c/span\u003e3,489.\u003c/p\u003e \u003cp\u003e The result of the Wilcoxon rank-sum test shows that there was a significant difference between the hospitalization cost of advanced NSCLC patients with squamous carcinoma and with non-squamous carcinoma (Z=-5.792, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The hospitalization cost of advanced NSCLC patients with non-squamous carcinoma was \u003cspan\u003e$\u003c/span\u003e18,003 (IQR=\u003cspan\u003e$\u003c/span\u003e21,372), which was higher than the \u003cspan\u003e$\u003c/span\u003e15,024 (IQR=\u003cspan\u003e$\u003c/span\u003e16,789) of patients with squamous carcinoma. Specifically, all three components, drug costs, inspection and testing costs, and other costs, were higher in the squamous carcinoma group than in the non-squamous carcinoma group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Hospitalization cost for advanced NSCLC per capita, by pathological type, USD.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInfluencing factors of the hospitalization cost associated with different pathological type\u003c/h2\u003e \u003cp\u003eThe results of GLM with a gamma distribution are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In model 1, after controlling the related factors, the pathological type was a significant influencing factor and compared to the squamous carcinoma group, the non-squamous carcinoma group had the higher hospitalization cost (β\u0026thinsp;=\u0026thinsp;0.098, 95%CI: 0.066,0.130). Also, it was observed that patients who were in older age (β=-0.038, 95%CI: -0.064, -0.010), female (β=-0.037, 95%CI: -0.064, -0.010), farmers (β=-0.057, 95%CI: -0.095, -0.018), and with no insurance (β=-0.150, 95%CI: -0.184, -0.116) had significantly lower hospitalization cost. Furthermore, patients who were treated in specialized hospitals (β\u0026thinsp;=\u0026thinsp;0.118, 95%CI: 0.082,0.154), and in other departments (β\u0026thinsp;=\u0026thinsp;0.054, 95%CI: 0.024,0.084) rather than in oncology and in respiratory, were more likely to spend more during hospitalization. When patients utilized more hospitalization (β\u0026thinsp;=\u0026thinsp;0.104, 95%CI: 0.101,0.107) and had a longer hospital stay (β\u0026thinsp;=\u0026thinsp;0.050, 95%CI: 0.046,0.053), the hospitalization cost was significantly higher.\u003c/p\u003e \u003cp\u003eAmong patients with squamous carcinoma in model 2, those who were not insured (β=-0.196, 95%CI: -0.013, -0.323) had significantly lower hospitalization costs. The significant factors in model 1, age, gender, occupation type, having URRBMI and other insurance, and hospital type became insignificant in model 2. Still, patients who were treated in other departments (β\u0026thinsp;=\u0026thinsp;0.119, 95%CI: 0.058,0.180) with more hospitalization (β\u0026thinsp;=\u0026thinsp;0.112, 95%CI: 0.105,0.118) and longer length of hospital stay (β\u0026thinsp;=\u0026thinsp;0.056, 95%CI: 0.050,0.062), had the significantly higher hospitalization cost. It was newly found that patients who didn\u0026rsquo;t transfer hospitals spent more on hospitalization (β\u0026thinsp;=\u0026thinsp;0.154, 95%CI: 0.013,0.323).\u003c/p\u003e \u003cp\u003eBeing similar with the results of model 1, the results of patients with squamous carcinoma in model 3 showed that the older age (β=-0.037, 95%CI: -0.066, -0.006), female (β=-0.044, 95%CI: -0.073, -0.016), farmers (β=-0.059, 95%CI: -0.103, -0.015), and having no insurance (β=-0.004, 95%CI: -0.002, -0.005) were the significant factors of the lower hospitalization cost, while having URRBMI (β\u0026thinsp;=\u0026thinsp;0.067, 95%CI: 0.026,0.107) became a significant factor of the higher hospitalization cost. Consistent with model 1 and model 2, treatment in other departments (β\u0026thinsp;=\u0026thinsp;0.035, 95%CI: 0.001,0.069), more hospitalization (β\u0026thinsp;=\u0026thinsp;0.102, 95%CI: 0.099,0.105), and longer length of hospital stay (β\u0026thinsp;=\u0026thinsp;0.048, 95%CI: 0.044,0.052)were significantly associated with the higher hospitalization cost.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple factor analysis of the hospitalization cost associated with different pathological type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-squamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.066,0.130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.038**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.064, -0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.104,0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.037**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.066, -0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.037**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.064, -0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.063,0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.044**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.073, -0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.163,0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.268,0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.173,0.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan Chinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.043,0.606)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.210,0.483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.375**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.075,0.675)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.057***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.095, -0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.118,0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.059**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.103, -0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetiree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.067,0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.159,0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.059,0.027)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.0008,0.0018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.111,0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.050**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.090, -0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUEBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURRBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.030,0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.020,0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.067***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.026,0.107)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.016,0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.118,0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.027,0.131)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.184, -0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.196***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.013, -0.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.002, -0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIIB\u0026amp;IIIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.043,0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.073,0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.047,0.042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Drive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.043,0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.224,0.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.068,0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialized hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.082,0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.053,0.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.148***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.107,0.190)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.040,0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.013,0.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.004,0.117)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.038,0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.031,0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.054,0.034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.024,0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.058,0.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.001,0.069)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHospitalization frequency (times)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.101,0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.112***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.105,0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.099,0.105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage length of hospital stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.046,0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.050,0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.044,0.052)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.612***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8.546,8.686)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.612***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(8.411,8.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.727***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(8.653,8.802)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e#: Model 1 is the multiple factor analysis of the hospitalization cost. Model 2 and Model 3 is the multiple factor analysis of the hospitalization costs in the squamous carcinoma group and the non-squamous carcinoma group, respectively. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study thoroughly outlines the hospitalization costs of advanced NSCLC in China based on multicenter real-world data, and initially scrutinized the impact of pathological categories on the inpatient expenses and their influencing factors for advanced NSCLC sufferers. The hospitalization cost of advanced NSCLC patients was \u003cspan\u003e$\u003c/span\u003e17,254 per capita, with drug costs as the highest cost. There is a significant difference between the hospitalization cost of advanced NSCLC patients with squamous carcinoma (\u003cspan\u003e$\u003c/span\u003e18,003) and with non-squamous carcinoma (\u003cspan\u003e$\u003c/span\u003e15,024). The factors influencing the hospitalization costs also by different pathological types are found.\u003c/p\u003e \u003cp\u003eThe hospitalization cost imposes an enormous burden on advanced NSCLC patients in China. In 2022, the Gross Domestic Product (GDP) was \u003cspan\u003e$\u003c/span\u003e12,891 per capita and the disposable income was \u003cspan\u003e$\u003c/span\u003e5,410 per capita(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), however, the hospitalization cost of advanced NSCLC patients per capita was over three times the disposable income per capita and even higher than the GDP per capita. For the three main components of total costs, the highest cost was drug costs (median=\u003cspan\u003e$\u003c/span\u003e10,160), which was also two times the disposable income per capita and accounted for 78% of the total hospitalization cost. For the hospitalization costs in other countries, it was reported that the hospitalization/emergency costs among stage IB-IIIA NSCLC Germany patients were approximately \u003cspan\u003e$\u003c/span\u003e8,994 per capita in 2018(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). It can\u0026rsquo;t be ignored that the hospitalization cost was higher than this in Germany reflecting the considerable economic burden for NSCLC patients and associating with two potential reasons: firstly, the differences in clinical guidelines, pathways, and practices between the two countries, and secondly, variations in medical insurance payment models. Germany has already implemented Diagnosis-Related Groups (DRGs) payment for lung cancer, while China is without the DRGs full implementation at the time of this study potentially resulting in higher costs(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). For the hospitalization costs of other cancers, the cost of advanced NSCLC patients was still higher than that of colorectal cancer(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), prostate cancer(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and oral cancer(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), which may be influenced by the characteristics of the disease and available innovative drugs in recent years. Lung cancer represents the leading cause of mortality and imposes a substantial economic burden in China, and both the disease burden and economic impact are particularly high, necessitating special attention and focused efforts.\u003c/p\u003e \u003cp\u003eIntriguingly, the results of GLM show the pathological type is a significant influencing factor in hospitalization costs. A significant difference was found between the hospitalization cost of advanced NSCLC patients with squamous carcinoma and those with non-squamous carcinoma (Z=-5.792, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The hospitalization cost of advanced NSCLC patients with non-squamous carcinoma (\u003cspan\u003e$\u003c/span\u003e18,003) was higher than that of patients with squamous carcinoma (\u003cspan\u003e$\u003c/span\u003e15,024). On the one hand, the different pathological types may have an impact on the expression of blood telomerase activity, the specific symptoms, the treatment regimens, and the prognosis after the operation(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). On the other hand, a higher number of innovative pharmaceuticals have been approved for non-squamous carcinoma patients than squamous carcinoma in China, such as immunotherapies (e.g., camrelizumab). Consequently, this increase in approved quantities potentially leads to elevated expenditures in both drug costs and overall hospitalization costs. Besides, the higher direct non-medical cost among advanced NSCLC non-squamous carcinoma patients in good health and the significant difference between the different pathological subtypes in China were also found(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Given that, the total financial spending of advanced NSCLC patients with non-squamous carcinoma is heavy, requiring multiple measures to reduce the economic burden.\u003c/p\u003e \u003cp\u003eFurthermore, we analyzed the influencing factors of the hospitalization costs among advanced NSCLC patients with squamous carcinoma and non-squamous carcinoma, respectively. Commonly, patients in the two groups with no insurance, who are uninsured or unable to claim insurance reimbursement due to cross-regional medical care, spent less during hospitalization. It reflects that the absence of medical insurance may impede physicians' appropriate treatment due to considerations of patients' affordability and force them shift toward generic drugs prescription instead of innovative medications, and the high out-of-pocket expense may also restrain the abundant utilization of healthcare and the acceptance of expensive drugs or treatments for patients(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Then, the hospital type was a significant factor in two groups. Compared to general hospitals, patients hospitalized in specialized hospitals had higher costs, potentially indicating that more innovative but expensive treatments are more popularized in specialized hospitals(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Also, patients who received treatment in other departments rather than in oncology and in respiratory were more likely to spend more during hospitalization, which may explain that the professional treatment is beneficial to cost-saving(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Non-squamous carcinoma patients exhibit greater differences in coefficients and bigger impact on costs across different hospital types and departments compared to the squamous carcinoma patients. Lastly, the hospitalization frequency and the average length of hospital stay were potential predictive factors because these factors reflect the severity of the disease and the pattern of utilization(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The more intensive hospitalization and the longer the hospital stay, the higher the hospitalization costs among advanced patients with squamous carcinoma or with non-squamous carcinoma(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the higher cost of patients with non-squamous carcinoma, we further explored the other different influencing factors for two pathological types. It was found that patients with non-squamous carcinoma who were older, female, and farmers had fewer costs during hospitalization indicating that the deprived population had lower medical expenses, however these factors are not significant among patients with squamous carcinoma. Non-squamous carcinoma patients face greater challenges related to the higher treatment costs, more affected by their economic status, affordability, and treatment adherence. Older patients may prematurely discontinue treatment due to the heavy financial burden, while male patients exhibit poorer treatment adherence compared to their counterparts. Moreover, patients from different occupational backgrounds exhibit variations in treatment choices and adherence due to differences in economic status and affordability, especially facing higher costs maybe beyond their willingness to pay.\u003c/p\u003e \u003cp\u003eThis study has profound policy implications. Firstly, given the heavy burden of NSCLC, it is necessary to reduce the disease burden of advanced NSCLC patients through multiple channels, such as increasing the reimbursement ratio of basic health insurance, expanding the list of drug reimbursements, providing social donations and aids, and calling on enterprises to donate innovative drugs. Secondly, the decision-makers should pay attention to patients with advanced non-squamous carcinoma NSCLC and provide policy support at the government level, such as major illness subsidies to promote universal coverage and health equity. Lastly, in practice, medical staffs should identify and follow up with patients who have been hospitalized multiple times or for long periods to adjust the suitable treatment. Also, the future studies could analyze the composition of the structure and change of high costs to figure out the reasons and optimize future treatment.\u003c/p\u003e \u003cp\u003eOur research has several limitations. On the one hand, the data source of this study is from the economically developed region in eastern China, which may overestimate the hospitalization costs. However, this study could objectively reflect the overall situation, because in China lung cancer treatment mainly follows guidelines, and most drugs have achieved uniform national pricing through centralized procurement and national reimbursement drug negotiation, which is the main driver of total costs. There may be differences in the inspection and testing costs and other costs in different regions so it should be extrapolated nationally with caution. On the other hand, this study adopted the retrospective research design. Although the EMR data is relatively accurate real-world data, it lacks more comprehensive personal information such as patient income, social support, and health status, which can be further collected and analyzed in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reports on real-world data from multiple clinical centers in China, describing the hospitalization costs and structure for advanced NSCLC patients. The results identify pathological type as a significant predictive factor, and analyze the impact of pathological types on hospitalization costs and the factors influencing hospitalization costs for advanced NSCLC patients with squamous and non-squamous carcinoma respectively. As far as we know, this is the first study in China that analyzed the hospitalization cost of advanced NSCLC patients with different pathological types to update basic information on disease economic burden for future relevant study and provide policy reference for medical reimbursement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eNo funding was received in this study.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eEthics approval was granted by the institutional review board, Public Health School of Fudan University (IRB00002408 \u0026amp; FWA00002399) and has been conducted in compliance with the Helsinki Declaration (1996).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.Z. conceptualized the study. Y.Y. collected and analyzed the data. All authors wrote the main manuscript text, prepared tables and figures, reviewed and revised the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng R, Zhang S, Zeng H, Wang S, Sun K, Chen R, et al. Cancer incidence and mortality in China, 2016. Journal of the National Cancer Center. 2022 2022-1-1;2(1):1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eUK CR. Cancer Incidence Statistics 2020.; 2023.\u003c/li\u003e\n\u003cli\u003eMcPherson INAG. The progression of non-small cell lung cancer from diagnosis to surgery. European Journal of Surgical Oncology: The Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. 2020;46(10Pta1).\u003c/li\u003e\n\u003cli\u003eZeng H, Ran X, An L, Zheng R, He J. Disparities in stage at diagnosis for five common cancers in China: a multicentre, hospital-based, observational study. 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ANN ONCOL. 2016;27(8):1423\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eCicin I, Oksuz E, Karadurmus N, Malhan S, Gumus M, Yilmaz U, et al. Economic burden of lung cancer in Turkey: a cost of illness study from payer perspective. HEALTH ECON REV. 2021;11(1).\u003c/li\u003e\n\u003cli\u003eWood R, Taylor-Stokes G. Cost burden associated with advanced non-small cell lung cancer in Europe and influence of disease stage. BMC CANCER. 2019;19.\u003c/li\u003e\n\u003cli\u003eMigliorino MR, Santo A, Romano G, Cortinovis D, Galetta D, Alabiso O, et al. Economic burden of patients affected by non-small cell lung cancer (NSCLC): the LIFE study. Journal of Cancer Research \u0026amp; Clinical Oncology. 2017;143(5):1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eCheng YI, Gan YC, Liu D, Davies MPA, Field JK. Potential genetic modifiers for somatic EGFR mutation in lung cancer: A meta-Analysis and literature review. 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J MED ECON. 2020;2(12):1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eGrumberg V, Chouaid C, Cotte FE, Jouaneton B, Jolivel R, Gaudin AF, et al. Long-term hospital resource utilization and associated costs of care for patients initiating nivolumab in advanced non-small cell lung cancer in France. J MED ECON. 2022 2022-1-1;25(1):691\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLi-Ming L, Wei C, Hong Y, Li-Ying Z, Yan J, Xuan Z, et al. Hospitalization Expenses and Influencing Factors for Patients with Lung Cancer in Beijing. China Cancer. 2019;2(11):23\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eLi Z, Jiang S, He RB, Dong YH, Pan ZJ, Xu CZ, et al. Trajectories of Hospitalization Cost Among Patients of End-Stage Lung Cancer: A Retrospective Study in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2018 2018-1-1;15(12).\u003c/li\u003e\n\u003cli\u003eThe State Council of the People's Republic of China. Resident income and consumption expenditure in 2022.; 2023.\u003c/li\u003e\n\u003cli\u003eFeng C, Mei-Yan JI, Yun LU. Germany's G-DRG Payment System and the Implications for China. Chinese Health Economics. 2016.\u003c/li\u003e\n\u003cli\u003eYuan GL, Liang LZ, Zhang ZF, Liang QL, Huang ZY, Zhang HJ, et al. Hospitalization costs of treating colorectal cancer in China A retrospective analysis. MEDICINE. 2019 2019-1-1;98(33).\u003c/li\u003e\n\u003cli\u003eYan B, Cai Y, Shen Z. Tendency Analysis of Average Hospitalization Cost of Prostate Cancer in China from 2013 to 2016. Chinese Journal of Health Statistics. 2018 2018-1-1;35(3):359\u0026thinsp;\u0026ndash;\u0026thinsp;61, 367.\u003c/li\u003e\n\u003cli\u003eYang J, Wan SQ, Huang L, Zhong WJ, Zhang BL, Song J, et al. Analysis of hospitalization costs and length of stay for oral cancer patients undergoing surgery: Evidence from Hunan, China. ORAL ONCOL. 2021 2021-1-1;119.\u003c/li\u003e\n\u003cli\u003eHu J, Sun P, Li R. 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Chin Health Serv Manag. 2020;1(37):525\u0026ndash;8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer, hospitalization cost, non-squamous carcinoma, squamous carcinoma, influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-3819071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3819071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eLung cancer represents the highest incidence and mortality rates among all cancers in China. Limited studies have explored the hospitalization costs of advanced non-small cell lung cancer (NSCLC) among Chinese. This study aims to outline the hospitalization costs of NSCLC patients, differentiate influencing factors, examine different pathological types affecting hospitalization costs and evaluate influencing factors respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this real-world, multicenter, retrospective study, we collected electronic medical record data from January 2017 to December 2020 in two types of hospitals: comprehensive hospitals and specialized oncology hospitals. A total of 5362 patients were included. Patients' information on sociodemographic characteristics, disease-related characteristics, healthcare service utilization, and hospitalization costs were collected. Descriptive analysis, the Wilcoxon rank-sum test, and the generalized linear model were employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe median hospitalization cost among advanced NSCLC patients was $17,254 per capita, with drug costs as the highest cost. The hospitalization cost among patients with non-squamous carcinoma ($18,003) was significantly higher than that among patients with squamous carcinoma ($15,024), and pathological type significantly influenced the costs (β=0.098, p\u0026lt;0.001). Common influencing factors of hospitalization costs for both types included health insurance, hospital type, department, hospitalization frequency, and average length of hospital stay. The varying significant factors comprised age, gender, and occupation type among non-squamous carcinoma patients, whereas these factors were not notable among squamous carcinoma patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHospitalization costs pose a substantial economic burden on advanced NSCLC patients in China, particularly for the non-squamous carcinoma. The higher costs hinder adequate utilization and appropriate treatment among vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Does pathologic type shape the hospitalization costs of advanced non- small cell lung cancer patients? A multicenter real-world data study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 18:12:37","doi":"10.21203/rs.3.rs-3819071/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5a2fe41-92a2-464b-b98d-cb8204c6aef3","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T09:36:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-05 18:12:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3819071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3819071","identity":"rs-3819071","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}