Willingness to Pay for Temporary Transfusion Independence in Lower-Risk Myelodysplastic Syndromes: Evidence from a Cross-Sectional Study in China

Willingness to Pay for Temporary Transfusion Independence in Lower-Risk Myelodysplastic Syndromes: Evidence from a Cross-Sectional Study in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Willingness to Pay for Temporary Transfusion Independence in Lower-Risk Myelodysplastic Syndromes: Evidence from a Cross-Sectional Study in China Yanan You, Xin Wang, Shirui Chen, Jingrong Zhu, Xinran Han, Ziyan Xue, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9135332/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Red blood cell transfusion is the predominant supportive treatment for lower-risk myelodysplastic syndromes (LR-MDS) in China, yet long-term transfusion leads to complications such as iron overload and is constrained by persistent blood supply shortages. While innovative therapies that reduce transfusion dependence are emerging, little is known about how patients value such clinical benefits. This study aimed to quantify patients’ willingness to pay (WTP) for temporary transfusion independence and to identify key determinants influencing their preferences. Methods Adult patients with transfusion-dependent LR-MDS were recruited through multistage sampling strategy across 13 provinces in China. The multi-center design partially mitigated the sampling challenges inherent to the rare disease population. Pre-survey informed the bid design for the contingent valuation method (CVM). Consenting patients completed demographic information, blood transfusion burden and WTP questionnaires through offline surveys or remote online interviews in formal survey. The maximum WTP for achieving 16 weeks of transfusion independence was obtained by applying two-bound binary selection (DBDC) and open-ended questions (OE) in CVM. Results According to the responses of 97 patients, the average WTP was ࿥26,376.7 per 16 weeks (95% CI: [17,999.7, 34,753.7]), accounting for approximately 18% of the average annual household income, demonstrate a strong desire for temporary anemia relief. Regression analysis revealed that age, household income, marital status, education level, and disease morphology classification were positively associated with WTP, while medical insurance was negatively associated with WTP. Through subgroup regression, a higher WTP was observed among the people with lower hemoglobin levels and higher annual household income. Conclusion Patients with LR-MDS in China demonstrate a strong desire for temporary anemia relief, reflecting the substantial health and social burden of chronic transfusion. These insights provide critical evidence for health technology assessment, reimbursement policy, and clinical decision-making regarding novel therapies targeting anemia management in MDS. Willingness-to-pay Transfusion burden Anemia Transfusion-dependent MDS Figures Figure 1 Background Myelodysplastic syndromes (MDS) are a group of rare and heterogeneous clonal myeloid disorders characterized by ineffective hematopoiesis and refractory cytopenia [ 1 ] . Red blood cell (RBC) transfusion remains the primary supportive therapy to improve patients' quality of life [ 2 ] . For lower-risk MDS(LR-MDS) patients, particularly when other treatments have failed, transfusion often represents the only therapeutic option for sustaining survival. In China, however, limited blood supply poses a major barrier to timely transfusion. Seasonal shortages are frequent, affecting routine care of clinic patients and a variety of inpatient needs [ 3 ] , increasing disease burden among LR-MDS patients. The methods of asking the patient's family or friends to donate in a family replacement (FR) program and encouraging voluntary donations are effective in addressing short-term blood shortage but have different implications for total blood supply in the long run [ 4 ] . The red blood cell distribution rate per thousand people, a measure of blood institutions’ clinical supply capacity, remains significantly lower than that of some middle- and high-income countries, at 11.1 donations per thousand in 2020 and 3.4 milliliters of red blood cells per capita [ 5 ] . The predictive data indicates that the total blood donation volume is expected to show a slight downward trend in the future [ 6 ] . A study in the Taiwan region projects that by 2027, blood demand will outstrip supply, with the annual shortfall exceeding one million units by 2060 if current trends persist [ 7 ] . This persistent imbalance between supply and demand compromises access to timely transfusion, prolongs fatigue and weakness, restricts daily activities and social participation, and ultimately worsens prognosis. The Chinese Guidelines for the Diagnosis and Treatment of Myelodysplastic Syndromes recommend transfusion when hemoglobin levels fall below 60 g/L or when severe anemia symptoms are present [ 2 ] . Yet, in clinical practice, many patients do not receive transfusion until hemoglobin levels drop below 40 g/L, leading to profound impairment in HRQoL for most transfusion-dependent (TD) patients. RBC transfusions provide rapid relief of anemia-associated symptoms such as fatigue, and improve quality of life (QoL), but patients chronically receiving RBC transfusions are at an increased risk [ 8 ] . Compared with non–transfusion-dependent (NTD) patients, transfusion-dependent patients have poorer prognoses, higher risks of leukemic transformation, and significantly increased cumulative non-leukemia mortality [ 9 ] . Furthermore, long-term, frequent transfusions are also associated with secondary complications. These include transfusion-transmitted viral infections [ 10 ] and secondary iron overload, with excess iron deposition in the myocardium [ 11 ] , liver, pancreas, and pituitary gland, leading to organ dysfunction and corresponding clinical manifestations such as coagulation disorders, abnormalities of glucose metabolism, growth retardation, and osteoporosis. Several pharmacologic options have demonstrated potential in reducing transfusion dependence. Lenalidomide, for example, has shown efficacy in specific subtypes. In a multicenter prospective phase II trial, Schuler et al. reported that 67% of LR-MDS patients with del(5q) achieved transfusion independence, compared with only 26.9% of non-del(5q) patients, whose median response duration was less than one year [ 12 ] . Furthermore, Santini et al. [ 13 ] found that patients with baseline erythropoietin (EPO) ≤ 500 mU/mL were more likely to achieve transfusion independence, while the response rate among those with EPO > 500 mU/mL was only 15.5%. More recently, luspatercept has demonstrated significant efficacy in phase III trials. In the MEDALIST trial [ 14 ] , 38% of LR-MDS patients with ring sideroblasts achieved ≥ 8 weeks of transfusion independence within 1–24 weeks, and 28% achieved ≥ 12 weeks. In the COMMANDS trial, luspatercept was superior to erythropoiesis-stimulating agents (ESAs), with 60.4% versus 34.8% of patients achieving the primary endpoint of red-blood-cell transfusion independence (RBC-TI) for ≥ 12 weeks with a concurrent mean hemoglobin increase of ≥ 1.5 g/dL during 1–24 weeks, and luspatercept providing a median response duration of 127 weeks [ 9 ] . Although these therapies highlight clinical benefits, they do not address the economic valuation of reducing transfusion dependence from the patient perspective. At present, there is a critical evidence gap regarding patients’ willingness to pay (WTP) for reducing transfusion dependence in LR-MDS. Existing studies are largely restricted to cost-effectiveness analyses in cancer patients with chemotherapy-induced anemia, which estimate maximum WTP for erythropoietin therapy. However, chemotherapy-induced anemia is an iatrogenic and potentially reversible condition, whereas anemia in MDS is a primary, progressive, and often irreversible manifestation of disease. Due to the rarity and complex pathophysiology of MDS, patient population remains under-represented in health economic evaluations, existing evidence has limited applicability to MDS. Moreover, while the direct costs of transfusion in China remain relatively low, the associated risks of complications and long-term morbidity limit its value as a benchmark for evaluating innovative therapies. In summary, transfusion is an indispensable but risk-laden therapy for anemic LR-MDS patients. Despite its clinical necessity, evidence on how patients value temporary relief from anemia—particularly in terms of WTP—remains scarce. To address this gap, the present study investigates the WTP for transfusion among anemic LR-MDS patients in China and explores the factors influencing their preferences. These findings will provide an essential evidence base for health technology assessment (HTA) and pharmacoeconomic evaluations of innovative therapies aimed at reducing transfusion burden. Methods Study Design and Setting We conducted a cross-sectional, hospital-based study using a structured questionnaire administered via on-site face-to-face interviews and one-on-one online interviews (For the specific interview questions, please see Supplementary material 3). The study targeted adults with lower-risk myelodysplastic syndromes (LR-MDS) who were transfusion dependent according to the IWG 2018 criteria. All participants provided written informed consent prior to data collection; interviews were audio-recorded for verification and quality control. Definitions and selection criteria Transfusion dependency followed the IWG 2018 classification based on total red blood cell (RBC) units over a 16-week interval: non–transfusion-dependent (NTD, 0 units), low transfusion dependency (LTD, <3 units), moderate transfusion dependency (MTD, 3–7 units), and high transfusion dependency (HTD, ≥8 units) [15] . The valuation target for willingness-to-pay (WTP) was the probability-weighted benefit of becoming transfusion-independent within 16 weeks, operationalized as a transition from HTD (≥8 units/16 weeks) to NTD (0 units/16 weeks) within the same time frame. This endpoint represents the monetary value patients assign to temporarily eliminating transfusion dependence and was endorsed for clinical validity by an expert panel. Sampling and Recruitment A multistage sampling strategy was used to enhance representativeness across participating sites, thereby partially overcoming the limitations caused by the difficulty in recruiting patients with rare diseases. Hematologists from selected hospitals were invited and, upon agreement, screened clinic lists to identify potentially eligible patients. Recruitment proceeded via clinician referrals and standardized invitation letters that described study aims and interview logistics. Contact information was provided to schedule interviews. Eligibility Criteria 1. Inclusion Criteria: Patients need to meet the following criteria: Age ≥18 years. Confirmed lower-risk MDS diagnosis according to the Revised International Prognostic Scoring System (IPSS-R) with a score ≤ 3.5 points. Transfusion-dependent status defined by the IWG 2018 criteria, specifically requiring >0 units of red blood cell (RBC) transfusions administered over the preceding 16 weeks. 2. Exclusion criteria: Presence of psychiatric disorders. Diagnosis of cognitive impairment. Not willing to be audio-recorded during the interview. Survey Instrument This study designed a structured questionnaire comprising two components: Baseline characteristics. We collected demographics, socioeconomic indicators, clinical features, and treatment history with emphasis on transfusion-related variables (RBC transfusion units per 16 weeks, iron chelation therapy, and any transfusion delays). WTP elicitation. We applied a double-bounded dichotomous choice (DBDC) format within the contingent valuation method (CVM) under guided hypothetical scenarios, supplemented by an open-ended (OE) question to capture maximum WTP [16,17] . In the scenario where blood transfusion offered temporary relief from anemia and no other temporary relief was available, interviewees answered two rounds of questions to assess their acceptance of the bid for temporary relief from anemia. The term 'bid' referred to the proposed price that participants were asked to consider in their WTP evaluations. If respondents answered 'yes' to the initial bid (Bid0), they would be asked about a second higher bid (BidH); if they answered 'no', a second lower bid (BidL) would be inquired, as illustrated in Figure 1 To mitigate starting-point bias, two pretested bid schedules were developed from a pilot survey and randomly assigned during interviews. Data Collection and Quality Assurance Trained interviewers conducted on-site hospital interviews; online interviews followed the same script and protocol. All interviews were recorded in full. After data collection, interviewers transcribed and entered responses into the WJX platform using a standardized codebook. A two-person cross-validation procedure was implemented to recheck key variables and minimize manual entry errors. Discrepancies were resolved by consensus and, when necessary, by reviewing the original audio. Handling of Missing Data We performed a systematic audit of missingness for all primary variables. When feasible, respondents were recontacted to retrieve missing information. If data remained unavailable, we used pragmatic imputation: logistic regression–based imputation for binary variables and median (continuous) / mode (categorical) imputation otherwise. Data analysis Descriptive Statistics We summarized categorical variables as counts and percentages. Continuous variables (e.g., age, income) were summarized using means, standard deviations, medians, interquartile ranges, and ranges, as appropriate. Primary Model Based on the characteristics of the method used in the questionnaire, this study constructs a Doubleb model based on DBDC response data and combined with the indirect utility difference theory proposed by Hanemann, to estimate the mean WTP interval of respondents and its influencing factors. In this theoretical model, it is assumed that respondents generate an unobserved utility function based on attribute characteristics “ ”, and the difference can be expressed by the following formula: In the formula, is the vector of explanatory variables for the i th respondent, covering their personal characteristics and health-economic indicators; coefficient β is the vector of parameters to be estimated, representing the marginal impact of each explanatory variable on WTP; is the error term, assumed to follow a normal distribution with a mean of 0 and variance of σ2; σ represents the standard deviation of the error term, used to measure the dispersion of non-systematic errors. Based on respondents' responses to the two bid prices, an interval regression model is constructed and maximum likelihood estimation is used for parameter fitting. Robustness Test To evaluate robustness, we additionally fit an ordered probit (Oprobit) model using a three-level dependent variable (pay_level). Considering the complexity of the WTP data structure and the sensitivity of results to different models, in addition to the main model, this study also conducts alternative modeling and robustness tests. Based on the combined results of double-bounded responses, a three-level dependent variable "pay_level" is constructed to represent the level of patients' WTP for "conversion from high transfusion dependency to transfusion independence". If a patient accepts both bid0 and bidH, it is recorded as "high willingness" (pay_level=3); if a patient rejected both bid0 and bidL, it is recorded as "low willingness" (pay_level=1); other cases are "medium willingness" (pay_level=2). The independent variables of the Oprobit model are set consistent with the main model to ensure the comparability of model results. Subgroup and Heterogeneity Analyses To explore preference heterogeneity, we prespecified subgroup analyses by household income and hemoglobin level. The primary Doubleb model was refit within each stratum to compare determinants of WTP across groups and to identify populations with potentially higher valuation of achieving temporary transfusion independence. All analyses were performed in Stata 16.0. Results Pre-survey Analysis and Bid Design Twelve MDS patients (mean age 56.9 ± 15.2 years; 50% male) participated in the pre-survey. Detailed demographic and clinical characteristics are provided in Table S1 (available in Supplementary material 1). In the pre-survey, descriptive statistics of patients’ willingness to pay (WTP) were obtained (Table 1). The mean WTP was RMB 17,887 (USD 2,493) with a wide standard deviation (SD) of RMB 26,603 (USD 3,708), indicating substantial heterogeneity in patients’ valuation. Based on these data, bid values were designed to minimize starting-point bias and to capture the broad range of WTP. Two distinct bid sets were generated, each including three bid points. The first set started at RMB 15,000 (USD 2,091), and the second set at RMB 25,000 (USD 3,485), followed by higher and lower bid adjustments (Table 2). Table 1 Patients’ WTP in pre-survey Statistic WTP Average value RMB 17887(SD=26603) [USD=2493(SD=3708)] 1st percentile 1995(USD=278) 25th percentile 6000(USD=836) 50th percentile 9320(USD=1299) 75th percentile 14625(USD=2039) 99th percentile 91750(USD=12790) Table 2 Design of bids Type of Bid The First Set The Second Set The initial bid RMB 15000 (USD 2091) RMB 25000 (USD 3485) The higher bid RMB 25000 (USD 3485) RMB 50000 (USD 6970) The lower bid RMB 8000(USD 1115) RMB 15000(USD 2091) Demographic and Clinical Characteristics Formal survey questionnaires were obtained from 97 low-risk MDS patients with improved transfusion dependence across thirteen provinces in Anhui, Hebei, Henan, Heilongjiang, Jiangsu, Jiangxi, Inner Mongolia, Shandong, Shanxi, Tianjin, Xinjiang, Yunnan and Zhejiang. The detailed demographic and clinical characteristics of the patients are summarized in Table 3. These patients had an average age of 61.9 years (SD=13.5), and 52.6% of them were male. Most patients (77.3%) had an educational background below a college’s degree. By annual GDP of place of residence, 33.0% of the patients lived in regions with an annual GDP of more than 1 trillion dollars (high economic level zones) and 67.0% lived in regions with an annual GDP of less than 1 trillion dollars (low economic level zones). Married patients constituted the majority (91.8%), and the patient's annual household income after taxes was RMB 139640 (SD=24.7), equivalent to USD 19,475.6 (The exchange rate on August 14, 2025, was 1 USD=7.17 RMB). All patients were covered by the national basic medical insurance program, of which 49 patients (50.5%) had covered by the plan for urban and rural residents. 9.3% of the patients had participated in clinical trials or received clinical trial drugs. In terms of disease morphology, MDS with increased blasts (MDS-IB) accounted for 19.6%. Transfusion dependence was one of the most common clinical problems in patients with MDS. Hemoglobin (Hb) levels below 60 g/L were observed in 32.0% of participants, and 18.6% of the patients were currently in a state of severe transfusion dependence, i.e., transfusion of greater than or equal to 8 units. 56 (57.7%) patients answered questions related to transfusion, and of these patients, 26.8% reported having had iron loading tests. In terms of iron chelation therapy, Deferasirox was used in 10.7% of transfused patients. In terms of mode of administration, oral was the only form. 48.2% of them self-reported that they had experienced delayed transfusions and 19.6% reported experiencing insufficient units for a single transfusion. Table 3 Basic patient information and transfusion burden Items Patients sample (N = 97) Gender, n (%) man 51 (52.6) woman 46 (47.4) Age, years (SD) 61.9 (13.5) Marital status, n (%) married 89 (91.8) unmarried, divorced and others 8 (8.2) Education, n (%) education at or beyond the college level 16 (16.5) education below the college level 75 (77.3) other 6 (6.2) Residence, n (%) living in regions with an annual GDP of more than 1 trillion dollars 32 (33.0) living in regions with an annual GDP of less than 1 trillion dollars 65 (67.0) Insurance type, n (%) the national basic medical insurance program for urban and rural residents 49 (50.5) other 48 (49.5) Family income, RMB(SD) 139640 (24.7) Disease morphology, n (%) MDS-IB 19 (19.6) other 78 (80.4) Hb level, n (%) Hb＜60 g/L 31 (32.0) Hb>60 g/L 66 (68.0) Clinical trial experience, n (%) yes 9 (9.3) no 83 (85.6) other 5 (5.1) Blood transfusion units per 16 weeks, n (%) =8 18 (18.6) Iron load test, n (%) yes 15 (26.8) no 24 (42.9) other 17 (30.3) Iron-removing drugs, n (%) none 22 (39.3) Deferasirox 6 (10.7) other 28 (50.0) Use of iron-removing drugs, n (%) oral 6 (100.0) other 0 (0.0) Delayed transfusions experience, n (%) yes 27 (48.2) no 29 (51.8) Insufficient single transfusions, n (%) yes 11 (19.6) no 45 (80.4) WTP for temporary alleviation of anemia status Following randomization, 46 patients participated in the survey for the first set of bidding scenarios, and 51 patients engaged with the second set. Table 4 illustrates the patient responses regarding acceptance or rejection of the two bids presented. Low-risk MDS patients with improved transfusion dependence had an average WTP of RMB 26,376.7 per 16 weeks (95% CI [17999.7-34753.7], equivalent to USD 3,678.8). See Table 5 for further details on these results. Table 4 Patient response statistics The set of bids a NN NY YN YY The first set 16 7 3 20 The second set 21 9 6 15 Total 37 16 9 35 a The letter combinations denote the outcomes for two dichotomous boundaries, where NN No/No, NY No/Yes, YN Yes/No, YY Yes/Yes Table 5 Estimates of WTP a Coef. b Std.Err. Z c [95% CI] Log likelihood d AIC e BIC ¥26,376.7 ($3,678.8) 4274.1 6.2 17999.7-34753.7 -104.0 232.0 262.9 a coefficient b the standard error of the coefficient c confidence interval d Akaike information criterion e Bayesian information criterion Factors associated with WTP To explore the determinants of WTP for 1 unit of leukocyte-depleted red blood cells, the basic model incorporated all possible factors (variable definitions in Table S2, as detail in Supplementary material 2). The expanded model (Wald chi2(9) = 21.15, P=0.0201) was employed. The analysis revealed the following (Table 6): Older patients demonstrated a higher WTP, and each additional year of age was associated with 769.2 RMB increase in WTP (β=769.2, P=0.015). Furthermore, patients with education at or beyond the college level demonstrated higher WTP than those with education below the college level (β=24272.6, P=0.060), and married patients showed a higher WTP than female patients (P=0.035). Among other constant conditions, patients with higher annual per capita household incomes were willing to pay more than those with lower incomes (β=1140.6, P=0.019). Additionally, in terms of disease morphology, patients with MDS-IB demonstrated a higher WTP than patients with other disease morphology (β=36820.7, P=0.047). Patients covered by the national basic medical insurance program for urban and rural residents were willing to pay significantly less (P=0.041). Table 6 Doubleb model results Beta Coef. Std. Err z P>|z| [95% Conf. Interval] gender 6490.5 7700.6 0.84 0.399 -8602.4 21583.3 **A ge 769.2 316.8 2.43 0.015 148.3 1390.0 ** Marital status 33648.9 15930.2 2.11 0.035 2426.4 64871.5 * Education 24272.6 12907.1 1.88 0.060 -1024.8 49570.1 Residence -5162.8 8444.0 -0.61 0.541 -21712.8 11387.1 * Insurance type -16857.4 8229.4 -2.05 0.041 -32986.7 -728. ** Family income 1140.6 488.0 2.34 0.019 184.1 2097.1 ** Disease morphology 36820.7 18511.2 1.99 0.047 539.4 731020 Hb level 2441.9 8546.6 0.29 0.775 -14309.0 19192.9 Clinical trial experience -9720.7 12927.4 -0.75 0.452 -35058.0 15616.5 a _cons -67667.4 27445.1 -2.47 0.014 -121458.8 -13875.9 Sigma(_cons) 29218.9 4927.1 5.93 0.000 19561.9 38875.9 WTP 26,376 4274.1 6.17 0.000 17999.7 34753.7 Log likelihood -104.0 AIC 232.0 BIC 262.9 a Constant term * P<0.1; ** P<0.05; *** P<0.01 To further validate the robustness of the findings and to refine the analysis of the distribution probabilities of patients under different willingness-to-pay levels, this study used an ordered probit model to construct a tertiary dependent variable with the response situation in the double-boundary dichotomy(Table 7 and Table 8). The regression results showed that the overall fit of the probit model was good (Wald chi2(9)=40.17, P=0.0000), suggesting that the included variables were effective in explaining the differences in willingness-to-pay grades to some extent . To ensure comparability, the independent variable settings in the oprobit model are consistent with the doubleb main model, and the robustness judgment criteria mainly include the consistency of the significant variables and whether the variable directions are consistent. In the study, all core explanatory variables under the two models are consistent in terms of significance and direction, indicating that the main conclusions of the study are highly robust. Table 7 Oprobit model results Beta Coef. Std. Err T value P value [95% Conf. Interval] Gender 0.38 0.26 1.46 0.14 -0.13 0.89 *Age 0.02 0.01 1.86 0.06 0.00 0.04 **Marital status 1.09 0.52 2.11 0.04 0.08 2.11 *Education 0.74 0.44 1.70 0.09 -0.11 1.59 Residence -0.08 0.29 -0.27 0.78 -0.65 0.49 **Insurance type -0.55 0.27 -2.00 0.05 -1.09 -0.01 **Family income 0.04 0.02 2.40 0.02 0.01 0.07 *Disease morphology 1.16 0.62 1.87 0.06 -0.06 2.39 Hb level 0.14 0.29 0.48 0.63 -0.43 0.70 Clinical trial experience -0.49 0.45 -1.09 0.27 -1.36 0.39 cut1 2.32 0.87 .b .b 0.62 4.02 cut2 3.19 0.89 .b .b 1.45 4.93 * P<0.1; ** P<0.05; *** P<0.01 cut1 and cut2 are categorization threshold parameters in the ordered probit model, representing the potential score points from “low willingness” to “medium willingness” and “medium willingness” to “high willingness”, respectively, which are used for internal probability estimation. "They are used for internal probability estimation and the values themselves are not used to explain the effects of the variables. Table 8 Oprobit model margin effect results Beta/Payment level Marginal effect Std. Err z P>|z| [95% Conf. Interval] Gender 1 -0.11 0.08 -1.50 0.13 -0.26 0.04 2 0.01 0.01 1.05 0.29 -0.01 0.04 3 0.10 0.07 1.49 0.14 -0.03 0.24 Age 1 -0.01 0.00 -1.91 0.06 -0.01 0.00 2 0.00 0.00 1.15 0.25 0.00 0.00 3 0.01 0.00 1.91 0.06 0.00 0.01 Marital status 1 -0.33 0.15 -2.24 0.03 -0.62 -0.04 2 0.04 0.03 1.30 0.19 -0.02 0.09 3 0.29 0.13 2.17 0.03 0.03 0.56 Education 1 -0.22 0.13 -1.73 0.08 -0.47 0.03 2 0.02 0.02 1.06 0.29 -0.02 0.07 3 0.20 0.11 1.76 0.08 -0.02 0.42 Residence 1 0.02 0.09 0.27 0.78 -0.15 0.19 2 0.00 0.01 -0.27 0.79 -0.02 0.02 3 -0.02 0.08 -0.27 0.78 -0.17 0.13 Insurance type 1 0.17 0.08 2.11 0.04 0.01 0.32 2 -0.02 0.01 -1.25 0.21 -0.05 0.01 3 -0.15 0.07 -2.06 0.04 -0.29 -0.01 Family income 1 -0.01 0.00 -2.48 0.01 -0.02 0.00 2 0.00 0.00 1.15 0.25 0.00 0.00 3 0.01 0.00 2.59 0.01 0.00 0.02 Disease morphology 1 -0.35 0.18 -1.93 0.05 -0.71 0.01 2 0.04 0.03 1.13 0.26 -0.03 0.10 3 0.31 0.16 1.94 0.05 0.00 0.63 Hb level 1 -0.04 0.09 -0.48 0.63 -0.21 0.13 2 0.00 0.01 0.47 0.64 -0.01 0.02 3 0.04 0.08 0.48 0.63 -0.11 0.19 Clinical trial experience 1 0.15 0.13 1.11 0.27 -0.11 0.41 2 -0.02 0.02 -0.90 0.37 -0.05 0.02 3 -0.13 0.12 -1.10 0.27 -0.36 0.10 1, 2, and 3 are the values of pay_level, representing low, medium, and high payment levels. Subgroup analysis The subgroup analysis results are presented in Table 9. Hb level differences: The results of the Hb level subgroup analysis indicated that patients with Hb 60g/L. Patients with HB >60g/L were willing to pay RMB 23627.2 per 16 weeks (95% CI [14730.0-32524.5]), equivalent to USD 3295.3, while patients with Hb <60g/L were willing to pay 43930.2 RMB per 16 weeks (95% CI [14599.5-73260.9]), equivalent to USD 6126.9. Income variability: The annual household income subgroup analysis revealed that, the willingness to pay (WTP) of patients with annual income higher than Chinese 2024 GDP per capita (RMB 95,749) was significantly higher than that of patients with annual income lower than Chinese 2024 GDP per capita. Specifically, the WTP per 16 weeks in patients with annual income lower than GDP per capita income was RMB 14,679.6 (95% CI [5641.7, 23717.4]), equivalent to USD 2,047.4, whereas the WTP per 16 weeks in patients with annual income higher than GDP per capita income was as high as RMB 33,242.2 (95% CI [17999.4, 48485]), equivalent to USD 4,636.3. Table 9 Results of subgroup analysis Factor Hb level Family per capita annual income Category Hb>60g/L Hb<60g/L RMB 95,749 and below RMB 95,749 above Coef. 23627.2 43930.2 14679.6 33242.2 Std. Err. 4539.5 14964.9 4611.2 7777.1 z 5.2 3.0 3.2 4.3 95%CI 14730.0-32524.5 14599.5-73260.9 5641.7-23717.4 17999.4-48485.0 Log likelihood -75.4 -22.1 -51.9 -52.1 AIC 172.8 66.2 125.9 126.1 BIC 196.9 81.9 147.4 146.0 Discussion This multi-center cross-sectional study provides novel evidence on the transfusion burden among LR-MDS patients in China and quantifies patients’ willingness to pay (WTP) for achieving temporary transfusion independence. By applying the double-bounded dichotomous choice contingent valuation method, the study further identifies sociodemographic and clinical factors influencing WTP, thereby contributing important real-world insights into the economic value patients place on innovative therapeutic options. Our findings confirm that anemia remains a dominant clinical feature of LR-MDS, and current symptom control in China is suboptimal, with a high reliance on red blood cell transfusion [ 15 ] . Consistent with prior evidence [ 18 , 19 ] , long-term transfusion is associated with serious risks such as iron overload, yet fewer than one-third of patients in this study reported undergoing iron load monitoring, and the uptake of iron chelation therapy was low. This is aligned with earlier studies in Chinese and international cohorts [ 20 ] , underscoring the urgent need to strengthen clinical awareness, monitoring, and management of transfusion-related complications. Another key finding concerns the persistent challenge of blood supply. Nearly half of patients who responded to transfusion-related questions reported delays in receiving transfusion, reflecting systemic supply-demand imbalances. This mirrors findings from a multinational survey across five European countries, where 65.6% of MDS patients experienced transfusion delays with an average wait time of 4.2 days [ 21 ] . Previous studies have indicated that Current annual blood donations in China have not kept pace with growing demand [ 22 ] . These realities highlight the pressing need for novel therapies that can reduce transfusion dependence and alleviate pressure on the healthcare system. The results show that in the Doubleb model that incorporates variables such as age, marital status, educational level, and family income, the estimated WTP value reaches 26,376.7yuan per 16 weeks, significantly higher than the current price of red blood cells at 210 yuan per unit. This objectively reflects the health value assessment of the patient group for the treatment goal of improving transfusion dependence. To some extent, this indicates that patients value the clinical efficacy of innovative therapies in improving transfusion dependence more than the treatment cost. A study on the willingness to pay of Chinese patients with transfusion-dependent β-thalassemia for temporary relief of transfusion dependence showed that respondents were willing to pay 29,259 yuan per year [ 23 ] . In contrast, the average WTP of MDS patients in this study in a one-time payment scenario is significantly higher, reflecting the patients' acceptance of potential new therapies and suggesting that MDS patients generally hope to alleviate their unmet clinical needs through more effective treatment methods. Although the WTP levels in different disease contexts cannot be directly compared, the high level of value recognition provides forward-looking information for future medical decision-making and resource allocation that better meets the needs of patients. Therefore, healthcare providers can use the WTP valuation of patients to preliminarily determine their demand intensity for new treatment options. Age, marital status, educational level, family income, insurance type and disease classification are the key factors influencing WTP in patients with low-risk MDS. Among them, age and family income have a significant positive effect on WTP, which is consistent with previous studies [ 24 , 25 ] . From the perspective of clinical needs, elderly patients have a more obvious perception of anemia symptoms due to the decline of physiological functions, and have a higher demand for blood transfusion. Facing more severe challenges in blood transfusion, from the perspective of economic capacity, elderly patients, due to their long-term accumulation of wealth, have a stronger ability to pay for medical care and are more inclined to invest economic resources in improving their own health. The dual drive of demand and capacity prompts them to offer a higher WTP. Economic capacity, as an indispensable fundamental condition in medical decision-making, directly restricts patients' actual payment behavior. When patients express their health preferences, disposable income determines the upper limit of their willingness to pay, and this influence is also supported in the Ordered probit model: For each additional unit of income, the probability of patients entering the "high willingness to pay" grade increased by 10.00%, reflecting the pull effect of income level on the payment grade. Being married and accepting higher education were associated with significantly higher WTP, and this conclusion has been confirmed by multiple studies [ 26 , 27 ] . The statistical differences in the formation of marital status may stem from the fact that married patients often have a more stable family payment system. Their spouses and children will jointly share the pressure of medical decision-making and the economic burden of diseases for them. In addition, married patients enjoy a more complete family mutual aid medical security policy. The dual-income structure and risk-sharing mechanism of the family can effectively improve their payment capacity. The positive correlation between higher-educational level and WTP may stem from the fact that patients with higher education levels usually have a more complete health awareness. This group tends to have a higher acceptance of innovative therapies, can evaluate treatment options more effectively, and form a clear willingness to pay. On the other hand, the accumulation of knowledge through education is the main way for individuals to improve their human capital, under the free competitive market conditions, higher education diplomas signify that the individual has higher skills, which translate into higher remuneration [ 28 ] . The positive correlation between higher-educational level and personal income directly affects patients' ability to pay. From the perspective of medical insurance types, whether one participates in the basic medical insurance for urban and rural residents has a significant negative impact on WTP. Compared with the basic medical insurance for urban employees or other supplementary commercial insurances, the basic medical insurance for urban and rural residents The high out-of-pocket ratio, the low reimbursement ratio, and the fact that many innovative therapies are not included in the reimbursement scope all lead to this security gap, which causes such patients to have to bear higher out-of-pocket medical expenses during the medical treatment process and face greater direct economic pressure. On the other hand, the participants of the basic medical insurance for urban and rural residents are mainly rural residents, flexible employees or low-income groups. These groups have low disposable income and often show strong price sensitivity when facing high-priced innovative therapies, which leads them to be more cautious in payment. For clinical experience, patients with the disease classification of MDS accompanied by an increase in blasts have a significantly higher willingness to pay. This statistical difference may be closely related to the unique disease characteristics and clinical prognosis of MDS-EB. Previous studies have shown that the prognosis of MDS-EB is poor, with a median overall survival of only 9 months to 2 years. The risk of progressing to AML within 5 years is approximately 50–90% [ 29 ] . Due to the high risk of the disease and the limitations of treatment options, the MDS-EB patient population shows a strong unmet clinical need, and this group usually experiences a long disease progression period. Having a deep understanding of the limitations of existing therapies, this treatment experience further strengthens their recognition of the value of innovative therapies. Faced with a limited survival period, they often tend to adopt an active treatment strategy and are willing to pay an additional premium for innovative therapies. By eliciting the willingness to pay (WTP) for blood transfusion among lower-risk MDS patients with anemia, this study provides an empirical measure of patients’ perceived value of blood. This patient-centered assessment serves multiple purposes. Firstly, it offers a unique perspective on the real-world value of blood as a scarce and special medical resource, thereby addressing a notable gap in current research. Secondly, it generates evidence that can inform the value assessment of innovative therapies or technologies aimed at reducing transfusion burden. Finally, it provides foundational data to support subsequent pharmacoeconomic evaluations and related health economic research in the field of MDS. This study has several limitations. First, the cross-sectional design limits causal inference, and the sample may not fully represent all LR-MDS patients in China. Second, WTP was elicited through contingent valuation, which is inherently hypothetical and subject to bias despite our pre-survey calibration. Finally, we focused on short-term transfusion independence (16 weeks); future studies should assess long-term preferences and explore heterogeneity by treatment response trajectories. Conclusion Our study found that the average WTP of patients with low-risk MDS was 26,376.7 yuan per 16 weeks, which can provide a quantitative basis for setting the threshold of willingness to pay in subsequent health economics research. The identification of demographic and socioeconomic gradients in WTP suggests that interventions targeting transfusion independence may deliver differential value across patient subgroups. Incorporating patient preferences into cost-effectiveness evaluations could inform pricing and reimbursement decisions for novel therapies in MDS. In particular, the substantial WTP observed in older, married, wealthier, and better-educated patients indicates that these groups may experience disproportionate benefits from access to innovative treatments. Declarations Ethics approval and consent to participate This study was conducted in accordance with International Society for Pharmacoepidemiology (ISPE) Guidelines for Good Epidemiology Practices. The study protocol was approved by the ethics committee of Nanjing Medical University, and written informed consent was obtained from all participants. This study involves research conducted on human data and material, and hereby declares that all research procedures comply with the relevant requirements of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The data are not publicly available due to their containing information that could compromise the privacy of research participants. Competing interests Xingzhi Wang was an employee of Bristol-Myers Squibb Company when the study was conducted. Other authors of this manuscript declare they have no competing interests, financial or otherwise. Funding This work was supported by Bristol-Myers Squibb. Authors’ contributions Xin Wang, Xingzhi Wang and Xiaoyu Xi contributed to study conception and design. Yanan You, Shirui Chen, Jingrong Zhu, Xinran Han, Ziyan Xue, Kewei Hu and Yufeng Jiang were involved in the investigation, analysis, and interpretation of the data and then prepared the initial draft of the manuscript together. All the authors have read and approved the final manuscript. Acknowledgments Support for this study, including conducting the study and assistance on editorial, was provided by Bristol-Myers Squibb. 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Atlas Genet. Cytogenet. Oncol. Haematol. https://doi.org/10.4267/2042/68934 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.docx Supplementarymaterial2.docx Supplementarymaterial3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 30 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Red blood cell (RBC) transfusion remains the primary supportive therapy to improve patients' quality of life\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. For lower-risk MDS(LR-MDS) patients, particularly when other treatments have failed, transfusion often represents the only therapeutic option for sustaining survival.\u003c/p\u003e \u003cp\u003eIn China, however, limited blood supply poses a major barrier to timely transfusion. Seasonal shortages are frequent, affecting routine care of clinic patients and a variety of inpatient needs\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, increasing disease burden among LR-MDS patients. The methods of asking the patient's family or friends to donate in a family replacement (FR) program and encouraging voluntary donations are effective in addressing short-term blood shortage but have different implications for total blood supply in the long run\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The red blood cell distribution rate per thousand people, a measure of blood institutions\u0026rsquo; clinical supply capacity, remains significantly lower than that of some middle- and high-income countries, at 11.1 donations per thousand in 2020 and 3.4 milliliters of red blood cells per capita\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The predictive data indicates that the total blood donation volume is expected to show a slight downward trend in the future\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. A study in the Taiwan region projects that by 2027, blood demand will outstrip supply, with the annual shortfall exceeding one million units by 2060 if current trends persist\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. This persistent imbalance between supply and demand compromises access to timely transfusion, prolongs fatigue and weakness, restricts daily activities and social participation, and ultimately worsens prognosis. The \u003cem\u003eChinese Guidelines for the Diagnosis and Treatment of Myelodysplastic Syndromes\u003c/em\u003e recommend transfusion when hemoglobin levels fall below 60 g/L or when severe anemia symptoms are present\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Yet, in clinical practice, many patients do not receive transfusion until hemoglobin levels drop below 40 g/L, leading to profound impairment in HRQoL for most transfusion-dependent (TD) patients.\u003c/p\u003e \u003cp\u003eRBC transfusions provide rapid relief of anemia-associated symptoms such as fatigue, and improve quality of life (QoL), but patients chronically receiving RBC transfusions are at an increased risk\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Compared with non\u0026ndash;transfusion-dependent (NTD) patients, transfusion-dependent patients have poorer prognoses, higher risks of leukemic transformation, and significantly increased cumulative non-leukemia mortality\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Furthermore, long-term, frequent transfusions are also associated with secondary complications. These include transfusion-transmitted viral infections\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and secondary iron overload, with excess iron deposition in the myocardium\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, liver, pancreas, and pituitary gland, leading to organ dysfunction and corresponding clinical manifestations such as coagulation disorders, abnormalities of glucose metabolism, growth retardation, and osteoporosis.\u003c/p\u003e \u003cp\u003eSeveral pharmacologic options have demonstrated potential in reducing transfusion dependence. Lenalidomide, for example, has shown efficacy in specific subtypes. In a multicenter prospective phase II trial, Schuler et al. reported that 67% of LR-MDS patients with del(5q) achieved transfusion independence, compared with only 26.9% of non-del(5q) patients, whose median response duration was less than one year\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Furthermore, Santini et al.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e found that patients with baseline erythropoietin (EPO)\u0026thinsp;\u0026le;\u0026thinsp;500 mU/mL were more likely to achieve transfusion independence, while the response rate among those with EPO\u0026thinsp;\u0026gt;\u0026thinsp;500 mU/mL was only 15.5%. More recently, luspatercept has demonstrated significant efficacy in phase III trials. In the MEDALIST trial\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, 38% of LR-MDS patients with ring sideroblasts achieved\u0026thinsp;\u0026ge;\u0026thinsp;8 weeks of transfusion independence within 1\u0026ndash;24 weeks, and 28% achieved\u0026thinsp;\u0026ge;\u0026thinsp;12 weeks. In the COMMANDS trial, luspatercept was superior to erythropoiesis-stimulating agents (ESAs), with 60.4% versus 34.8% of patients achieving the primary endpoint of red-blood-cell transfusion independence (RBC-TI) for \u0026ge;\u0026thinsp;12 weeks with a concurrent mean hemoglobin increase of \u0026ge;\u0026thinsp;1.5 g/dL during 1\u0026ndash;24 weeks, and luspatercept providing a median response duration of 127 weeks\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Although these therapies highlight clinical benefits, they do not address the economic valuation of reducing transfusion dependence from the patient perspective.\u003c/p\u003e \u003cp\u003eAt present, there is a critical evidence gap regarding patients\u0026rsquo; willingness to pay (WTP) for reducing transfusion dependence in LR-MDS. Existing studies are largely restricted to cost-effectiveness analyses in cancer patients with chemotherapy-induced anemia, which estimate maximum WTP for erythropoietin therapy. However, chemotherapy-induced anemia is an iatrogenic and potentially reversible condition, whereas anemia in MDS is a primary, progressive, and often irreversible manifestation of disease. Due to the rarity and complex pathophysiology of MDS, patient population remains under-represented in health economic evaluations, existing evidence has limited applicability to MDS. Moreover, while the direct costs of transfusion in China remain relatively low, the associated risks of complications and long-term morbidity limit its value as a benchmark for evaluating innovative therapies. In summary, transfusion is an indispensable but risk-laden therapy for anemic LR-MDS patients. Despite its clinical necessity, evidence on how patients value temporary relief from anemia\u0026mdash;particularly in terms of WTP\u0026mdash;remains scarce. To address this gap, the present study investigates the WTP for transfusion among anemic LR-MDS patients in China and explores the factors influencing their preferences. These findings will provide an essential evidence base for health technology assessment (HTA) and pharmacoeconomic evaluations of innovative therapies aimed at reducing transfusion burden.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Design and Setting\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional, hospital-based study using a structured questionnaire administered via on-site face-to-face interviews and one-on-one online interviews (For the specific interview questions, please see Supplementary material 3). The study targeted adults with lower-risk myelodysplastic syndromes (LR-MDS) who were transfusion dependent according to the IWG 2018 criteria. All participants provided written informed consent prior to data collection; interviews were audio-recorded for verification and quality control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDefinitions and selection criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransfusion dependency followed the IWG 2018 classification based on total red blood cell (RBC) units over a 16-week interval: non\u0026ndash;transfusion-dependent (NTD, 0 units), low transfusion dependency (LTD, \u0026lt;3 units), moderate transfusion dependency (MTD, 3\u0026ndash;7 units), and high transfusion dependency (HTD, \u0026ge;8 units)\u003csup\u003e[15]\u003c/sup\u003e.\u003cbr\u003e\u0026nbsp;The valuation target for willingness-to-pay (WTP) was the probability-weighted benefit of becoming transfusion-independent within 16 weeks, operationalized as a transition from HTD (\u0026ge;8 units/16 weeks) to NTD (0 units/16 weeks) within the same time frame. This endpoint represents the monetary value patients assign to temporarily eliminating transfusion dependence and was endorsed for clinical validity by an expert panel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSampling and Recruitment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multistage sampling strategy was used to enhance representativeness across participating sites, thereby partially overcoming the limitations caused by the difficulty in recruiting patients with rare diseases. Hematologists from selected hospitals were invited and, upon agreement, screened clinic lists to identify potentially eligible patients. Recruitment proceeded via clinician referrals and standardized invitation letters that described study aims and interview logistics. Contact information was provided to schedule interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEligibility Criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Inclusion Criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients need to meet the following criteria:\u003c/p\u003e\n\u003col class=\"decimal_type\" style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eAge \u0026ge;18 years.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Confirmed lower-risk MDS diagnosis according to the Revised International Prognostic Scoring System (IPSS-R) with a score \u0026le; 3.5 points.\u003c/li\u003e\n \u003cli\u003eTransfusion-dependent status defined by the IWG 2018 criteria, specifically requiring \u0026gt;0 units of red blood cell (RBC) transfusions administered over the preceding 16 weeks.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2. Exclusion criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003ePresence of psychiatric disorders.\u003c/li\u003e\n \u003cli\u003eDiagnosis of cognitive impairment.\u003c/li\u003e\n \u003cli\u003eNot willing to be audio-recorded during the interview.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSurvey Instrument\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study designed a structured questionnaire comprising two components:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eBaseline characteristics.\u003c/strong\u003e We collected demographics, socioeconomic indicators, clinical features, and treatment history with emphasis on transfusion-related variables (RBC\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003etransfusion units per 16 weeks,\u0026nbsp;iron chelation therapy, and any transfusion delays).\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eWTP elicitation.\u003c/strong\u003e We applied a double-bounded dichotomous choice (DBDC) format within the contingent valuation method (CVM) under guided hypothetical scenarios, supplemented by an open-ended (OE) question to capture maximum WTP\u003csup\u003e[16,17]\u003c/sup\u003e. In the scenario where blood transfusion offered temporary relief from anemia and no other temporary relief was available, interviewees answered two rounds of questions to assess their acceptance of the bid for temporary relief from anemia. The term \u0026apos;bid\u0026apos; referred to the proposed price that participants were asked to consider in their WTP evaluations. If respondents answered \u0026apos;yes\u0026apos; to the initial bid (Bid0), they would be asked about a second higher bid (BidH); if they answered \u0026apos;no\u0026apos;, a second lower bid (BidL) would be inquired, as illustrated in Figure 1 To mitigate starting-point bias, two pretested bid schedules were developed from a pilot survey and randomly assigned during interviews.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Collection and Quality Assurance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained interviewers conducted on-site hospital interviews; online interviews followed the same script and protocol. All interviews were recorded in full. After data collection, interviewers transcribed and entered responses into the WJX platform using a standardized codebook. A two-person cross-validation procedure was implemented to recheck key variables and minimize manual entry errors. Discrepancies were resolved by consensus and, when necessary, by reviewing the original audio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHandling of Missing Data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a systematic audit of missingness for all primary variables. When feasible, respondents were recontacted to retrieve missing information. If data remained unavailable, we used pragmatic imputation: logistic regression\u0026ndash;based imputation for binary variables and median (continuous) / mode (categorical) imputation otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe summarized categorical variables as counts and percentages. Continuous variables (e.g., age, income) were summarized using means, standard deviations, medians, interquartile ranges, and ranges, as appropriate.\u003c/p\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003ePrimary Model\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBased on the characteristics of the method used in the questionnaire, this study constructs a Doubleb model based on DBDC response data and combined with the indirect utility difference theory proposed by Hanemann, to estimate the mean WTP interval of respondents and its influencing factors. In this theoretical model, it is assumed that respondents generate an unobserved utility function based on attribute characteristics \u0026ldquo;\u003cimg width=\"10\" height=\"16\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1777470792.jpg\" v:shapes=\"Picture_x0020_10001\" alt=\"image\"\u003e\u0026rdquo;, \u003c/p\u003e\n\u003cp\u003eand the difference can be expressed by the following formula:\u003cbr\u003e \u003cimg width=\"145\" height=\"17\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1777470782.gif\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the formula, \u003cimg width=\"10\" height=\"16\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1777470792.jpg\" v:shapes=\"Picture_x0020_10002\" alt=\"image\"\u003e is the vector of explanatory variables for the i\u003csub\u003eth\u003c/sub\u003e respondent, covering their personal characteristics and health-economic indicators; coefficient \u0026beta; is the vector of parameters to be estimated, representing the marginal impact of each explanatory variable on WTP; \u003cimg width=\"12\" height=\"16\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1777470802.jpg\" v:shapes=\"Picture_x0020_10003\" alt=\"image\"\u003e\u0026nbsp;is the error term, assumed to follow a normal distribution with a mean of 0 and variance of \u0026sigma;2; \u0026sigma; represents the standard deviation of the error term, used to measure the dispersion of non-systematic errors. Based on respondents\u0026apos; responses to the two bid prices, an interval regression model is constructed and maximum likelihood estimation is used for parameter fitting.\u003c/p\u003e\n\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eRobustness Test\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo evaluate robustness, we additionally fit an ordered probit (Oprobit) model using a three-level dependent variable (pay_level).\u003c/p\u003e\n\u003cp\u003eConsidering the complexity of the WTP data structure and the sensitivity of results to different models, in addition to the main model, this study also conducts alternative modeling and robustness tests. Based on the combined results of double-bounded responses, a three-level dependent variable \u0026quot;pay_level\u0026quot; is constructed to represent the level of patients\u0026apos; WTP for \u0026quot;conversion from high transfusion dependency to transfusion independence\u0026quot;. If a patient accepts both bid0 and bidH, it is recorded as \u0026quot;high willingness\u0026quot; (pay_level=3); if a patient rejected both bid0 and bidL, it is recorded as \u0026quot;low willingness\u0026quot; (pay_level=1); other cases are \u0026quot;medium willingness\u0026quot; (pay_level=2). The independent variables of the Oprobit model are set consistent with the main model to ensure the comparability of model results.\u003c/p\u003e\n\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eSubgroup and Heterogeneity Analyses\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo explore preference heterogeneity, we prespecified subgroup analyses by household income and hemoglobin level. The primary Doubleb model was refit within each stratum to compare determinants of WTP across groups and to identify populations with potentially higher valuation of achieving temporary transfusion independence. All analyses were performed in Stata 16.0.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePre-survey Analysis and Bid Design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve MDS patients (mean age 56.9 \u0026plusmn; 15.2 years; 50% male) participated in the pre-survey. Detailed demographic and clinical characteristics are provided in Table S1 (available in \u0026nbsp;Supplementary material 1).\u003c/p\u003e\n\u003cp\u003eIn the pre-survey, descriptive statistics of patients\u0026rsquo; willingness to pay (WTP) were obtained (Table 1). The mean WTP was RMB 17,887 (USD 2,493) with a wide standard deviation (SD) of RMB 26,603 (USD 3,708), indicating substantial heterogeneity in patients\u0026rsquo; valuation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on these data, bid values were designed to minimize starting-point bias and to capture the broad range of WTP. Two distinct bid sets were generated, each including three bid points. The first set started at RMB 15,000 (USD 2,091), and the second set at RMB 25,000 (USD 3,485), followed by higher and lower bid adjustments (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Patients\u0026rsquo; WTP in pre-survey\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWTP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eAverage value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eRMB 17887(SD=26603) [USD=2493(SD=3708)]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1st percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1995(USD=278)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e25th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e6000(USD=836)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e50th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e9320(USD=1299)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e14625(USD=2039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e99th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e91750(USD=12790)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Design of bids\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Bid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe First Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe Second Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eThe initial bid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eRMB 15000 (USD 2091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eRMB 25000 (USD 3485)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eThe higher bid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eRMB 25000 (USD 3485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eRMB 50000 (USD 6970)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eThe lower bid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eRMB 8000(USD 1115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eRMB 15000(USD 2091)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographic and Clinical Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal survey questionnaires were obtained from 97 low-risk MDS patients with improved transfusion dependence across thirteen provinces in Anhui, Hebei, Henan, Heilongjiang, Jiangsu, Jiangxi, Inner Mongolia, Shandong, Shanxi, Tianjin, Xinjiang, Yunnan and Zhejiang. The detailed demographic and clinical characteristics of the patients are summarized in Table 3. These patients had an average age of 61.9 years (SD=13.5), and 52.6% of them were male. Most patients (77.3%) had an educational background below a college\u0026rsquo;s degree. By annual GDP of place of residence, 33.0% of the patients lived in regions with an annual GDP of more than 1 trillion dollars (high economic level zones) and 67.0% lived in regions with an annual GDP of less than 1 trillion dollars (low economic level zones). Married patients constituted the majority (91.8%), and the patient\u0026apos;s annual household income after taxes was RMB 139640 (SD=24.7), equivalent to USD 19,475.6 (The exchange rate on August 14, 2025, was 1 USD=7.17 RMB). All patients were covered by the national basic medical insurance program, of which 49 patients (50.5%) had covered by the plan for urban and rural residents. 9.3% of the patients had participated in clinical trials or received clinical trial drugs. In terms of disease morphology, MDS with increased blasts (MDS-IB) accounted for 19.6%.\u003c/p\u003e\n\u003cp\u003eTransfusion dependence was one of the most common clinical problems in patients with MDS. Hemoglobin (Hb) levels below 60 g/L were observed in 32.0% of participants, and 18.6% of the patients were currently in a state of severe transfusion dependence, i.e., transfusion of greater than or equal to 8 units. 56 (57.7%) patients answered questions related to transfusion, and of these patients, 26.8% reported having had iron loading tests. In terms of iron chelation therapy, Deferasirox was used in 10.7% of transfused patients. In terms of mode of administration, oral was the only form. 48.2% of them self-reported that they had experienced delayed transfusions and 19.6% reported experiencing insufficient units for a single transfusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Basic patient information and transfusion burden\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients sample (N = 97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eunmarried, divorced and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eeducation at or beyond the college level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eeducation below the college level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eliving in regions with an annual GDP of more than 1 trillion dollars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eliving in regions with an annual GDP of less than 1 trillion dollars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance type, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003ethe national basic medical insurance program for urban and rural residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily income, RMB(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e139640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease morphology, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eMDS-IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eHb＜60 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eHb\u0026gt;60 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trial experience, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood transfusion units per 16 weeks, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026gt;=8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIron load test, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIron-removing drugs, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eDeferasirox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of iron-removing drugs, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed transfusions experience, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsufficient single transfusions, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e(80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWTP for temporary alleviation of anemia status\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing randomization, 46 patients participated in the survey for the first set of bidding scenarios, and 51 patients engaged with the second set. Table 4 illustrates the patient responses regarding acceptance or rejection of the two bids presented. Low-risk MDS patients with improved transfusion dependence had an average WTP of RMB 26,376.7 per 16 weeks (95% CI [17999.7-34753.7], equivalent to USD 3,678.8). See Table 5 for further details on these results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Patient response statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe set of bids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;NN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eThe first set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eThe second set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e The letter combinations denote the outcomes for two dichotomous boundaries, where NN No/No, NY No/Yes, YN Yes/No, YY Yes/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Estimates of WTP\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Coef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Std.Err.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog likelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;BIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026yen;26,376.7 ($3,678.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4274.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e17999.7-34753.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-104.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e232.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e262.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e coefficient\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e the standard error of the coefficient\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e confidence interval\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Akaike information criterion\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Bayesian information criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactors associated with WTP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the determinants of WTP for 1 unit of leukocyte-depleted red blood cells, the basic model incorporated all possible factors (variable definitions in Table S2, as detail in Supplementary material 2). The expanded model (Wald chi2(9) = 21.15, P=0.0201) was employed. The analysis revealed the following (Table 6): Older patients demonstrated a higher WTP, and each additional year of age was associated with 769.2 RMB increase in WTP (\u0026beta;=769.2,\u0026nbsp;P=0.015). Furthermore, patients with education at or beyond the college level demonstrated higher WTP than those with education below the college level (\u0026beta;=24272.6,\u0026nbsp;P=0.060), and married patients showed a higher WTP than female patients (P=0.035). Among other constant conditions, patients with higher annual per capita household incomes were willing to pay more than those with lower incomes (\u0026beta;=1140.6,\u0026nbsp;P=0.019). Additionally, in terms of disease morphology, patients with MDS-IB demonstrated a higher WTP than patients with other disease morphology (\u0026beta;=36820.7,\u0026nbsp;P=0.047). Patients covered by the national basic medical insurance program for urban and rural residents were willing to pay significantly less (P=0.041).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Doubleb model results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% Conf. Interval]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003egender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6490.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7700.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-8602.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e21583.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e**A\u003cstrong\u003ege\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e769.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e316.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e148.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1390.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e**\u003cstrong\u003e\u0026nbsp;Marital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e33648.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15930.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2426.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e64871.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e* \u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e24272.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12907.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1024.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e49570.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-5162.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8444.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-21712.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11387.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e* \u003cstrong\u003eInsurance type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-16857.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8229.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-32986.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-728.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e** \u003cstrong\u003eFamily income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1140.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e488.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e184.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2097.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e**\u003cstrong\u003e\u0026nbsp;Disease morphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e36820.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e18511.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e539.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e731020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2441.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8546.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-14309.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19192.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trial experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-9720.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12927.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-35058.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15616.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e _cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-67667.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e27445.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-121458.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-13875.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eSigma(_cons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e29218.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4927.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e19561.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e38875.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eWTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e26,376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4274.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e17999.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e34753.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-104.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e232.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e262.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eConstant term\u003c/p\u003e\n \u003cp\u003e* P\u0026lt;0.1; ** P\u0026lt;0.05; *** P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo further validate the robustness of the findings and to refine the analysis of the distribution probabilities of patients under different willingness-to-pay levels, this study used an ordered probit model to construct a tertiary dependent variable with the response situation in the double-boundary dichotomy(Table 7 and Table 8). The regression results showed that the overall fit of the probit model was good (Wald chi2(9)=40.17, P=0.0000), suggesting that the included variables were effective in explaining the differences in willingness-to-pay grades to some extent . To ensure comparability, the independent variable settings in the oprobit model are consistent with the doubleb main model, and the robustness judgment criteria mainly include the consistency of the significant variables and whether the variable directions are consistent. In the study, all core explanatory variables under the two models are consistent in terms of significance and direction, indicating that the main conclusions of the study are highly robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 Oprobit model results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eT value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% Conf. Interval]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e**Marital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e**Insurance type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e**Family income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Disease morphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trial experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ecut1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ecut2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e* P\u0026lt;0.1; ** P\u0026lt;0.05; *** P\u0026lt;0.01\u003c/p\u003e\n \u003cp\u003ecut1 and cut2 are categorization threshold parameters in the ordered probit model, representing the potential score points from \u0026ldquo;low willingness\u0026rdquo; to \u0026ldquo;medium willingness\u0026rdquo; and \u0026ldquo;medium willingness\u0026rdquo; to \u0026ldquo;high willingness\u0026rdquo;, respectively, which are used for internal probability estimation. \u0026quot;They are used for internal probability estimation and the values themselves are not used to explain the effects of the variables.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8 Oprobit model margin effect results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eBeta/Payment level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMarginal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[95% Conf. Interval]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease morphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trial experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1, 2, and 3 are the values of pay_level, representing low, medium, and high payment levels.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubgroup analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis results are presented in Table 9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHb level differences:\u003c/strong\u003e The results of the Hb level subgroup analysis indicated that patients with Hb \u0026lt;60g/L showed a higher WTP than patients with HB \u0026gt;60g/L. Patients with HB \u0026gt;60g/L were willing to pay RMB 23627.2 per 16 weeks (95% CI [14730.0-32524.5]), equivalent to USD 3295.3, while patients with Hb \u0026lt;60g/L were willing to pay 43930.2 RMB per 16 weeks (95% CI [14599.5-73260.9]), equivalent to USD 6126.9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncome variability:\u003c/strong\u003e The annual household income subgroup analysis revealed that, the willingness to pay (WTP) of patients with annual income higher than Chinese 2024 GDP per capita (RMB 95,749) was significantly higher than that of patients with annual income lower than Chinese 2024 GDP per capita. Specifically, the WTP per 16 weeks in patients with annual income lower than GDP per capita income was RMB 14,679.6 (95% CI [5641.7, 23717.4]), equivalent to USD 2,047.4, whereas the WTP per 16 weeks in patients with annual income higher than GDP per capita income was as high as RMB 33,242.2 (95% CI [17999.4, 48485]), equivalent to USD 4,636.3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9 Results of subgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily per capita annual income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb\u0026gt;60g/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb\u0026lt;60g/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMB 95,749 and below\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMB 95,749 above\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e23627.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e43930.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e14679.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e33242.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eStd. Err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e4539.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e14964.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4611.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e7777.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e14730.0-32524.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e14599.5-73260.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e5641.7-23717.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e17999.4-48485.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e-51.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e172.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e66.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e125.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e126.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e196.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e147.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e146.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multi-center cross-sectional study provides novel evidence on the transfusion burden among LR-MDS patients in China and quantifies patients\u0026rsquo; willingness to pay (WTP) for achieving temporary transfusion independence. By applying the double-bounded dichotomous choice contingent valuation method, the study further identifies sociodemographic and clinical factors influencing WTP, thereby contributing important real-world insights into the economic value patients place on innovative therapeutic options.\u003c/p\u003e \u003cp\u003eOur findings confirm that anemia remains a dominant clinical feature of LR-MDS, and current symptom control in China is suboptimal, with a high reliance on red blood cell transfusion\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Consistent with prior evidence\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, long-term transfusion is associated with serious risks such as iron overload, yet fewer than one-third of patients in this study reported undergoing iron load monitoring, and the uptake of iron chelation therapy was low. This is aligned with earlier studies in Chinese and international cohorts\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, underscoring the urgent need to strengthen clinical awareness, monitoring, and management of transfusion-related complications.\u003c/p\u003e \u003cp\u003eAnother key finding concerns the persistent challenge of blood supply. Nearly half of patients who responded to transfusion-related questions reported delays in receiving transfusion, reflecting systemic supply-demand imbalances. This mirrors findings from a multinational survey across five European countries, where 65.6% of MDS patients experienced transfusion delays with an average wait time of 4.2 days\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Previous studies have indicated that Current annual blood donations in China have not kept pace with growing demand\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. These realities highlight the pressing need for novel therapies that can reduce transfusion dependence and alleviate pressure on the healthcare system.\u003c/p\u003e \u003cp\u003eThe results show that in the Doubleb model that incorporates variables such as age, marital status, educational level, and family income, the estimated WTP value reaches 26,376.7yuan per 16 weeks, significantly higher than the current price of red blood cells at 210 yuan per unit. This objectively reflects the health value assessment of the patient group for the treatment goal of improving transfusion dependence. To some extent, this indicates that patients value the clinical efficacy of innovative therapies in improving transfusion dependence more than the treatment cost. A study on the willingness to pay of Chinese patients with transfusion-dependent β-thalassemia for temporary relief of transfusion dependence showed that respondents were willing to pay 29,259 yuan per year\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In contrast, the average WTP of MDS patients in this study in a one-time payment scenario is significantly higher, reflecting the patients' acceptance of potential new therapies and suggesting that MDS patients generally hope to alleviate their unmet clinical needs through more effective treatment methods. Although the WTP levels in different disease contexts cannot be directly compared, the high level of value recognition provides forward-looking information for future medical decision-making and resource allocation that better meets the needs of patients. Therefore, healthcare providers can use the WTP valuation of patients to preliminarily determine their demand intensity for new treatment options.\u003c/p\u003e \u003cp\u003eAge, marital status, educational level, family income, insurance type and disease classification are the key factors influencing WTP in patients with low-risk MDS. Among them, age and family income have a significant positive effect on WTP, which is consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. From the perspective of clinical needs, elderly patients have a more obvious perception of anemia symptoms due to the decline of physiological functions, and have a higher demand for blood transfusion. Facing more severe challenges in blood transfusion, from the perspective of economic capacity, elderly patients, due to their long-term accumulation of wealth, have a stronger ability to pay for medical care and are more inclined to invest economic resources in improving their own health. The dual drive of demand and capacity prompts them to offer a higher WTP. Economic capacity, as an indispensable fundamental condition in medical decision-making, directly restricts patients' actual payment behavior. When patients express their health preferences, disposable income determines the upper limit of their willingness to pay, and this influence is also supported in the Ordered probit model: For each additional unit of income, the probability of patients entering the \"high willingness to pay\" grade increased by 10.00%, reflecting the pull effect of income level on the payment grade.\u003c/p\u003e \u003cp\u003eBeing married and accepting higher education were associated with significantly higher WTP, and this conclusion has been confirmed by multiple studies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The statistical differences in the formation of marital status may stem from the fact that married patients often have a more stable family payment system. Their spouses and children will jointly share the pressure of medical decision-making and the economic burden of diseases for them. In addition, married patients enjoy a more complete family mutual aid medical security policy. The dual-income structure and risk-sharing mechanism of the family can effectively improve their payment capacity. The positive correlation between higher-educational level and WTP may stem from the fact that patients with higher education levels usually have a more complete health awareness. This group tends to have a higher acceptance of innovative therapies, can evaluate treatment options more effectively, and form a clear willingness to pay. On the other hand, the accumulation of knowledge through education is the main way for individuals to improve their human capital, under the free competitive market conditions, higher education diplomas signify that the individual has higher skills, which translate into higher remuneration\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The positive correlation between higher-educational level and personal income directly affects patients' ability to pay.\u003c/p\u003e \u003cp\u003eFrom the perspective of medical insurance types, whether one participates in the basic medical insurance for urban and rural residents has a significant negative impact on WTP. Compared with the basic medical insurance for urban employees or other supplementary commercial insurances, the basic medical insurance for urban and rural residents The high out-of-pocket ratio, the low reimbursement ratio, and the fact that many innovative therapies are not included in the reimbursement scope all lead to this security gap, which causes such patients to have to bear higher out-of-pocket medical expenses during the medical treatment process and face greater direct economic pressure. On the other hand, the participants of the basic medical insurance for urban and rural residents are mainly rural residents, flexible employees or low-income groups. These groups have low disposable income and often show strong price sensitivity when facing high-priced innovative therapies, which leads them to be more cautious in payment.\u003c/p\u003e \u003cp\u003eFor clinical experience, patients with the disease classification of MDS accompanied by an increase in blasts have a significantly higher willingness to pay. This statistical difference may be closely related to the unique disease characteristics and clinical prognosis of MDS-EB. Previous studies have shown that the prognosis of MDS-EB is poor, with a median overall survival of only 9 months to 2 years. The risk of progressing to AML within 5 years is approximately 50\u0026ndash;90%\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Due to the high risk of the disease and the limitations of treatment options, the MDS-EB patient population shows a strong unmet clinical need, and this group usually experiences a long disease progression period. Having a deep understanding of the limitations of existing therapies, this treatment experience further strengthens their recognition of the value of innovative therapies. Faced with a limited survival period, they often tend to adopt an active treatment strategy and are willing to pay an additional premium for innovative therapies.\u003c/p\u003e \u003cp\u003eBy eliciting the willingness to pay (WTP) for blood transfusion among lower-risk MDS patients with anemia, this study provides an empirical measure of patients\u0026rsquo; perceived value of blood. This patient-centered assessment serves multiple purposes. Firstly, it offers a unique perspective on the real-world value of blood as a scarce and special medical resource, thereby addressing a notable gap in current research. Secondly, it generates evidence that can inform the value assessment of innovative therapies or technologies aimed at reducing transfusion burden. Finally, it provides foundational data to support subsequent pharmacoeconomic evaluations and related health economic research in the field of MDS.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional design limits causal inference, and the sample may not fully represent all LR-MDS patients in China. Second, WTP was elicited through contingent valuation, which is inherently hypothetical and subject to bias despite our pre-survey calibration. Finally, we focused on short-term transfusion independence (16 weeks); future studies should assess long-term preferences and explore heterogeneity by treatment response trajectories.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study found that the average WTP of patients with low-risk MDS was 26,376.7 yuan per 16 weeks, which can provide a quantitative basis for setting the threshold of willingness to pay in subsequent health economics research. The identification of demographic and socioeconomic gradients in WTP suggests that interventions targeting transfusion independence may deliver differential value across patient subgroups. Incorporating patient preferences into cost-effectiveness evaluations could inform pricing and reimbursement decisions for novel therapies in MDS. In particular, the substantial WTP observed in older, married, wealthier, and better-educated patients indicates that these groups may experience disproportionate benefits from access to innovative treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with International Society for Pharmacoepidemiology (ISPE) Guidelines for Good Epidemiology Practices. The study protocol was approved by the ethics committee of Nanjing Medical University, and written informed consent was obtained from all participants. This study involves research conducted on human data and material, and hereby declares that all research procedures comply with the relevant requirements of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly available due to their containing information that could compromise the privacy of research participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXingzhi Wang was an employee of Bristol-Myers Squibb Company when the study was conducted. Other authors of this manuscript declare they have no competing interests, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Bristol-Myers Squibb.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXin Wang, Xingzhi Wang and Xiaoyu Xi contributed to study conception and design. Yanan You, Shirui Chen, Jingrong Zhu, Xinran Han, Ziyan Xue, Kewei Hu and Yufeng Jiang were involved in the investigation, analysis, and interpretation of the data and then prepared the initial draft of the manuscript together. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupport for this study, including conducting the study and assistance on editorial, was provided by Bristol-Myers Squibb.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUemura, S., Hasegawa, D., Yoshida, N., Keino, D., Hasegawa, D., Yokosuka, T., Hama, A., Sato, M., Okuno, K., Nakashima, K., Sasahara, Y., Saito, S., Takahashi, Y., Hashii, Y., Matsumoto, K., Tabuchi, K., \u0026amp; Yamamoto, S. (2026). Impact of Conditioning Regimen Type on Outcomes in Pediatric Patients with De Novo Advanced Myelodysplastic Syndrome Undergoing Hematopoietic Stem Cell Transplantation. 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Comparison of four contingent valuation methods to estimate the economic value of a pneumococcal vaccine in Bangladesh. International Journal of Technology Assessment in Health Care, 24(4), 481～487. https://doi.org/10.1017/S026646230808063X\u003c/li\u003e\n\u003cli\u003eCost-Benefit Analysis of Environmental Goods by Applying the Contingent Valuation Method. (2006). Springer Japan. https://doi.org/10.1007/4-431-28950-X\u003c/li\u003e\n\u003cli\u003eSekeres, M. A., \u0026amp; Taylor, J. (2022). Diagnosis and Treatment of Myelodysplastic Syndromes: A Review. JAMA, 328(9), 872～880. https://doi.org/10.1001/jama.2022.14578\u003c/li\u003e\n\u003cli\u003eKaka, S., Jahangirnia, A., Beauregard, N., Davis, A., Tinmouth, A., \u0026amp; Chin-Yee, N. (2022). Red blood cell transfusion in myelodysplastic syndromes: A systematic review. Transfusion Medicine (Oxford, England), 32(1), 3～23. https://doi.org/10.1111/tme.12841\u003c/li\u003e\n\u003cli\u003eZhang, Y., Xiao, C., Li, J., Song, L. X., Zhao, Y. S., Zhao, J. G., \u0026amp; Chang, C. K. (2022). [Influencing factors of iron metabolism assessment in patients with myelodysplastic syndrome: A retrospective study]. Zhonghua Xue Ye Xue Za Zhi = Zhonghua Xueyexue Zazhi, 43(4), 293～299. https://doi.org/10.3760/cma.j.issn.0253-2727.2022.04.005\u003c/li\u003e\n\u003cli\u003eGupta, S., Kulasekararaj, A. G., Costantino, H., Grisolano, J., Tang, J., Jones, S., \u0026amp; Tang, D. (2023). Physicians\u0026rsquo; experience in blood supply shortages and the top factors that impact the clinical, economic, and humanistic outcomes of patients with myelodysplastic syndromes in 5 European countries. Current Medical Research and Opinion, 39(2), 239～247. https://doi.org/10.1080/03007995.2022.2151735\u003c/li\u003e\n\u003cli\u003eZhu, Y., Xie, D., Wang, X., \u0026amp; Qian, K. (2017). Challenges and Research in Managing Blood Supply in China. 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Frontiers in Public Health, 11, 1036110. https://doi.org/10.3389/fpubh.2023.1036110\u003c/li\u003e\n\u003cli\u003eNoor Aizuddin, A., Sulong, S. \u0026amp; Aljunid, S. M. (2012). Factors influencing willingness to pay for healthcare. BMC Public Health, 12(S2), A37. https://doi.org/10.1186/1471-2458-12-S2-A37\u003c/li\u003e\n\u003cli\u003eKumlachew Abate. (2015). Association Between Socioeconomic Status and Willingness to Pay for Medical Care Among Government School Teachers in Addis Ababa. Science Journal of Public Health, 3(5), 677. https://doi.org/10.11648/j.sjph.20150305.23\u003c/li\u003e\n\u003cli\u003eWeiss A. (1995). Human Capital vs. Signalling Explanations of Wages. Journal of Economic Perspectives, 9(4), 133～154. https://doi.org/10.1257/jep.9.4.133\u003c/li\u003e\n\u003cli\u003eBayerl, M. (2018). Myelodysplastic syndrome with excess blasts. Atlas Genet. Cytogenet. Oncol. Haematol. https://doi.org/10.4267/2042/68934 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Willingness-to-pay, Transfusion burden, Anemia, Transfusion-dependent MDS","lastPublishedDoi":"10.21203/rs.3.rs-9135332/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9135332/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRed blood cell transfusion is the predominant supportive treatment for lower-risk myelodysplastic syndromes (LR-MDS) in China, yet long-term transfusion leads to complications such as iron overload and is constrained by persistent blood supply shortages. While innovative therapies that reduce transfusion dependence are emerging, little is known about how patients value such clinical benefits. This study aimed to quantify patients\u0026rsquo; willingness to pay (WTP) for temporary transfusion independence and to identify key determinants influencing their preferences.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAdult patients with transfusion-dependent LR-MDS were recruited through multistage sampling strategy across 13 provinces in China. The multi-center design partially mitigated the sampling challenges inherent to the rare disease population. Pre-survey informed the bid design for the contingent valuation method (CVM). Consenting patients completed demographic information, blood transfusion burden and WTP questionnaires through offline surveys or remote online interviews in formal survey. The maximum WTP for achieving 16 weeks of transfusion independence was obtained by applying two-bound binary selection (DBDC) and open-ended questions (OE) in CVM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the responses of 97 patients, the average WTP was ࿥26,376.7 per 16 weeks (95% CI: [17,999.7, 34,753.7]), accounting for approximately 18% of the average annual household income, demonstrate a strong desire for temporary anemia relief. Regression analysis revealed that age, household income, marital status, education level, and disease morphology classification were positively associated with WTP, while medical insurance was negatively associated with WTP. Through subgroup regression, a higher WTP was observed among the people with lower hemoglobin levels and higher annual household income.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePatients with LR-MDS in China demonstrate a strong desire for temporary anemia relief, reflecting the substantial health and social burden of chronic transfusion. These insights provide critical evidence for health technology assessment, reimbursement policy, and clinical decision-making regarding novel therapies targeting anemia management in MDS.\u003c/p\u003e","manuscriptTitle":"Willingness to Pay for Temporary Transfusion Independence in Lower-Risk Myelodysplastic Syndromes: Evidence from a Cross-Sectional Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:47:13","doi":"10.21203/rs.3.rs-9135332/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T12:43:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T09:15:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T06:08:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T14:49:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-28T14:44:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ccf3026-5bd4-49b5-a9ae-de5683b2de71","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:47:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:47:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9135332","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9135332","identity":"rs-9135332","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}