Contributions of risk information frame to perceived risk, time orientation, and cancer drugs insurance purchasing decisions: based on a nationwide online survey experiment

Contributions of risk information frame to perceived risk, time orientation, and cancer drugs insurance purchasing decisions: based on a nationwide online survey experiment | 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 Article Contributions of risk information frame to perceived risk, time orientation, and cancer drugs insurance purchasing decisions: based on a nationwide online survey experiment Zhenyu Sun, Ziying Zhang, Xi Chen, Dongfu Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4570011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As one of the most populous countries with the highest number of cancer patients worldwide, China is actively promoting emerging commercial cancer drugs insurance (CDI) to address the increasingly serious cancer burden. However, providers are uncertain whether the risk information they design in their promotional advertising is effective for expanding CDI that primarily sold online. In this paper, we present a randomized online survey experiment nationally, to understand the unique ability of low/high risk information frame (LRIF/HRIF) in shaping individuals' CDI purchasing decisions. The results reveal that the LRIF which being used by providers is ineffective, the effect of shifting LRIF to HRIF in advertising increasing 43.7% for stimulating purchasing decisions. A possible mechanism may depend on HRIF restraining present-oriented attitude and reinforcing future-oriented attitude with the mediating role of perceived risk. These results suggest that further employing HRIF to develop advertising toolkits effectively would critical for promoting CDI expansion. Health sciences/Health care Health sciences/Health care/Health policy risk information frame perceived risk time orientation cancer drugs insurance China Introduction Cancer constitutes a significant portion of the global disease burden (Murray, 2020 ), and has been the leading cause of death in China from 2000 to 2020 (Cao et al., 2022 ). In 2020 alone, approximately 2.4 million Chinese residents succumbed to cancer (Qi et al., 2023 ), with the economic cost estimated to reach USD 6.1 trillion nationally from 2020 to 2050 (Chen et al., 2023 ). While specific anticancer drugs can significantly reduce mortality and tumor progression, their exorbitant prices pose a significant barrier (Michaeli & Michaeli, 2022 ). According to Allied Market Research (AMR), global spending on cancer drugs was approximately USD 135.5 billion in 2020, projected to reach USD 274.4 billion by 2030 (AMR, 2021). In China, domestic targeted anticancer drugs often exceed USD 3786 per month (Luo et al., 2023 ), rendering them unaffordable for most families (Prasad et al., 2017 ). The escalating costs of anticancer drugs have garnered international attention (Yasaitis et al., 2019 ). However, the public health insurance system does not adequately shield individual cancer patients from financial toxicity (Chambers et al., 2018 ; Desai & Gyawali, 2020 ; Xiao et al., 2022 ). Incentivizing specialty drugs coverage in commercial health plans may increase tumor treatments access for patients (Chambers et al., 2018 ; Xiao et al., 2022 ), while cancer drug insurance (CDI) commercial coverage in China remains inadequate. Disparities in health insurance participation were ongoing concerns, with social psychologists and behavioral economists positing that perceptions and attitudes toward time and risk may explain these disparities (Loewenstein et al., 2013 ; Rice, 2013 ), and leveraging the frame effect of information presentation in promotional strategies is considered an effective tool to influence health insurance behavior (Aizawa & Kim, 2015 ). In the context of China's burgeoning "Internet plus" development, online sales are catalyzing the rise of CDI, garnering increased attention in recent years. Nevertheless, as our observation for nowaday common online CDI advertisements (examples see Appendix A), we found that rough narrative of cancer-related risk information, and same risk information frame (RIF) for different CDI schemes is an universal phenomenon in CDI advertising. We refer to rough narratives as low risk information frame (LRIF) and meticulous narratives as high risk information frame (HRIF). Considering these factors together, it is reasonable to speculate that inefficiently linking advertising to risk information is a significant factor contributing to weakened CDI sales status in China, although we have not found studies confirming this. Also, despite existing studies offer initial insights into the correlation between perceived risk and time orientation and the use of RIF in promoting health behaviors, the majority were conducted using retrospective data or laboratory experiments with students as subjects, providing inconclusive evidence of the potential efficacy of RIF in health policy making. Undoubtedly, effective debiasing strategies could be practically applied to gain causal understanding of factors contributing to bias, and online survey experiments may counter biased data associated with health-related decision-making (Cao & Li, 2023 ). In summary, this study provides a contextualized simulation aligned with current emerging CDI online sales advertising, aiming to understand the unique ability of RIF in shaping individuals' time orientation, perceived risk, and focusing specifically on CDI purchasing decision mechanisms. Such research has powerful implications for making health policies to promote CDI, which as an emerging and prominent insurance product in China. Unless specified otherwise, the CDI discussed in present study is commercial. Information framing Information framing refers to how individuals respond differently based on the positive or negative description of information (Ferguson & Gallagher, 2007 ). Two prominent models (risk, and gain-loss framed) propose that the titer effects of information frame are moderated by framing methods(Kwasny et al., 2022 ) and perceived risk(Pakseresht et al., 2022 ). Risky and temporal framing are the two common formats for framing information or advertising in health domain (Kees, 2010 ). There is evidence that the way information is framed influences decision-making with respect to a variety of health behaviors(Aizawa & Kim, 2015 ; Kim & Nan, 2019 ; Lewis & Atad, 2023 ). For example, results from a controlled experiment on temporal framing effects in HPV vaccination indicated that a present-oriented message presented in a narrative format led to more favorable attitudes, stronger intentions, and perceived efficacy towards the vaccine (Kim & Nan, 2019 ), exposure to narrative messages was also found to be positively associated with intentions to discuss influenza vaccination (Lewis & Atad, 2023 ). Many theories could be used to interpret the effectiveness of information frames and how they work. Prospect theory (PT), temporal discounting theory (TDT), and construal level theory (CLT) mainly in this regard. The framing postulate of PT indicates that health-relevant information presented in terms of value and risk can shape individual perceptions to motivate healthy behavior (Rothman & Salovey, 1997 ). TDT treats the reduction in the value of an intensifier as a function of its receiver delay, revealing the psychological phenomenon that an individual's value assessment for events decreases over time (Bickel et al., 2021 ). CLT proposes the concept of temporal construal, suggesting that the time-distance effect arises from an active network of mental representations towards future objects, predicting that feasibility concerns should receive less weight as time distance increases (Lange et al., 2012 ). Consequently, we observe a close relationship between information, time, risk, and value evaluation. Perceived risk The weighting of benefits and costs in insurance decision-making may also depend on individuals' perceptions and attitudes toward the risks associated with cancer (Soane et al., 2010 ). Perceived risk arises from uncertainty about carcinogenic factors and the economic burden prior to cancer diagnosis (Sum & Nordin, 2018 ). Individual subjective assessments of their risk and recovery probabilities from health and financial trauma caused by cancer development seem pivotal in enrolling in CDI. Theoretical models examining the correlation between perceived risk and medical insurance purchasing yield mixed predictions. There is evidence that perceived risk clearly pushes the average willingness to pay for health insurance below the fair price (Baillon et al., 2022 ). However, there is also evidence that perceived risk related to the chances of getting cancer is not associated with health behaviors like cancer exams (Bowen et al., 2004 ). Researchers are committed to deepening our understanding of the driving mechanism of perceived risk, and exploring how to improve the public's risk identification ability and decision-making quality through efficient risk communication. Existing research indicates that message content framing in combination with health-related risk communications is able to have significant and measurable effects on consumer cognition, emotion, and behavior (Pechmann & Catlin, 2016 ). Additionally, some analyses have shown that perceived risk plays a mediating role in the influence of online food safety information acquisition on food risk prevention behaviors (You et al., 2023 ). While, this potentially important framing technique that has not received enough attention is the differentiated risk narratives in the context of online sales advertising for CDI. Time orientation Time orientation refers to how individuals value distant outcomes relative to present ones (Goldzahl, 2017 ). Evidence drawn from CLT suggests that health messages linked temporal framing effects could influence individuals' time orientation towards hazards and losses in health, and temporal framing effects in health advertising have also been found to affect future-orientation on perceived risk and behavioral intentions (Kees, 2010 ; Li et al., 2016 ). The latest findings from a multilevel meta-analysis on temporal framing effects showed that gain (versus loss) framing was a significant moderator for temporal influence on promoting healthy eating and anti-smoking/drinking behaviors, and proximal frames were more effective at increasing risk perception than distal frames (Huang & Xu, 2024 ). Nevertheless, although numerous literature indicated that a close connection between perceived risk and time orientation, some scholars do not believe that framing risk information would systematically change time orientation (Kees, 2010 ). Time orientation with systematic differences distinguishes individuals who stress-emphasize immediate versus delayed consequences (present-orientation versus future-orientation) of their actions, and individuals with different time orientations show heterogeneity in health-related behaviors (Tórtora & Ares, 2018 ). An online experiment revealed that stronger present-orientation predicted greater smoking intentions, while future-orientation predicted greater quit intentions, and in addition, future (versus present) thinking significantly improved intentions to give up smoking by enhancing perceived self-efficacy for cessation (Nan & Qin, 2019 ). Given that CDI purchasing decisions involve trade-offs between delayed benefits (relieve financial burden if found cancer, or give security sense during guarantee period) and immediate costs (premium cost, monetary loss aversion), individuals' weighting of long-term versus short-term consequences may influence their decisions. Therefore, future-oriented individuals may place higher value on health insurance benefits than present-oriented individuals (Han, 2018 ). Literature review mentioned as above, the influence of risky framing on time orientation is not well understood, especially regarding whether existing risk advertising is effective that has been applied in practice for CDI promotion, whether framing differentiated risk narratives would drive individuals' consumption on CDI options that dependent on time orientation and its action mechanism. Thereupon, we proposed the following basic hypotheses regarding RIF, perceived risk, and time orientation. H1: A much more meticulous risk information narrative would facilitate individual's CDI purchasing decisions for insuring or insuring long-term schemes. H2: A much more meticulous risk information narrative would strengthen individual's future-orientation and weaken present-orientation regarding CDI purchasing. H3: A much more meticulous risk information narrative would increase individual's perceived risk related to developing cancer. H4: The perceived risk would play a mediating role in the influence of time orientation on CDI purchasing decisions. Method Data source This study utilized an online survey experiment, which was approved by Nanjing Medical University's Institutional Review Board in October 2023 and was implemented by wenjuan.com in November 2023. Wenjuan.com recruited contributing subjects willing to engage in the experiment from its sample bank of 8 million people. For making the sample more nationally representative, we required provincial and gender distribution characteristics of the samples that wenjuan.com collected were similar to the data published by the National Bureau of Statistics of China in 2022. The experiment was self-administered and accessible at any time during the designated period. Upon completion of each experiment, wenjuan.com randomly awarded subjects with 200 to 500 website points and CNY 1 to 3 cash rewards. Subjects were allowed to complete the experiment only once and could leave at any time. Study sample This study initially included a random sample of 6044 adults aged from 18 to 60 years old, who were members of the wenjuan.com online research panel. Upon initiation of the study, participants were unaware of whether they were in the treatment (HRIF) or control (LRIF) group. Subjects were informed that the survey pertained to consumption on CDI, and no precise information on the survey's topic was provided during the recruitment process (Goldzahl, 2017 ). After excluding invalid samples based on criteria such as minimum answer time, empty value limitation, and repeated responses, a total of 5583 valid samples were included for analysis. Online survey experiment design We conducted a randomized online survey experiment nationally to explore a 2 (Part A, RIF: LRIF, HRIF) × 3 (Part B, CDI schemes: Distinguished by free cancer drugs provided duration of 1, 2, or 3 years) factorial design on the primary outcome: subject's choices for CDI schemes; as well as secondary outcomes about time orientation (Part C) and perceived risk (Part D) related to CDI purchasing, along with selected demographic characteristics (Part E) of subjects. Consequently, subjects were divided into groups (r,j) randomly according to the questionnaire version they response to, which was extracted randomly from the six questionnaire versions generated based on RIF and CDI schemes. The details are as follows: Part A : Risk information frames (RIFs) We extracted LRIF from common online CDI advertising, which served as the control group and simulated the current online CDI risk exposition in the market. Correspondingly, HRIF as the treatment group was created with a more meticulous narrative of cancer itself and its derived financial risks mainly, based on CLT (Huang & Xu, 2024 ; Kim & Nan, 2019 ; Lange et al., 2012 ) in behavioral economics and relevant references(Block & Keller, 1995 ; Bonner et al., 2021 ; Kreuter et al., 2007 ; Lewis & Atad, 2023 ). The RIFs see Appendix B. Part B : CDI schemes Age serves as the primary determinant for CDI online pricing. We knew the 40-something age group was the body of CDI purchasing form executives at several large CDI companies. Thus, we performed age 40 as a parameter into the top 6 mainstream online CDI subscription systems, and found that the average premium for insuring 1 year was CNY 176.62. Then, the premium for just insuring 1 year was set at CNY 179 in our CDI schemes. After discounting according to the China's average inflation rate (2%-3%) over the past decade and the 3-year fixed deposit rate (2.20%) published by the Bank of China, the premium for insuring 3 years consecutively was priced at CNY 521 in our CDI schemes. The final premium determination in our study also considered the principle of anchoring effect in behavioral economics(Baillon et al., 2022 ), which was to increase the premium mental calculation complexity for weakening the interference of linear price growth on the subject's choices of CDI schemes, in this way, the decision results for the subjects were more reflected in the time orientation, rather than the premium change itself. The only difference between the 3 CDI schemes is the duration free cancer drugs provided, see Appendix C. Part C : Time orientation towards CDI purchasing To avoid order effect and propensity scoring of the subjects' responses, partial mirrored items were set and presented randomly with the wenjuan.com random question function, and the final scores of each item were displayed after the same trend processing. Time orientation was designated as the independent variable and categorized into present-oriented and future-oriented attitudes (Goldzahl, 2017 ), measured with psychometric scales is associated with current cancer risk and premium cost, future security, and long-term impact on life(Kees, 2010 ). The present-oriented dimension was assessed using two items: "I focus on my current NOT future cancer risk when I choose the CDI scheme" (mirrored item: "I focus on my future NOT current cancer risk when I choose the CDI scheme") and "I care more about current premium NOT future cost reduce when I choose the CDI scheme" (mirrored item: "I care more about future cost reduce NOT current premium when I choose the CDI scheme"). The future-oriented dimension was evaluated using two items: "I value more on the future security when I choose the CDI scheme" and "I consider its long-term impact on my future life seriously when I choose the CDI scheme". All four items were measured on five-point scales ranging from 1 (disagree) to 5 (agree). Accordingly, the present-oriented and future-oriented attitudes dimension both are scored on a scale of 2–10, with higher scores indicating a stronger corresponding time orientation. Part D : Perceived risk related to developing cancer The methodology for Part D was established following the same approach as for Part C. We set perceived risk as the mediating variable in this study that consists of cancer and financial by reference to the research(Molina et al., 2015 ; Persoskie et al., 2014 ; Zomerdijk et al., 2021 ). To assess the dimension of cancer, we employed two items focusing on the presence of carcinogenic factors and concerns about developing cancer: "Carcinogenic factors are ubiquitous in daily living and working conditions" and "I'm worried about developing cancer in my life" (mirrored item: "I'm NOT worried about developing cancer in my life"). To evaluate the financial dimension, we utilized 3 items concentrating on the risk of cancer drugs burden, unaffordable for family, and CDI avoids economic loss: "I'm worried about the price of cancer drugs is steep" (mirrored item: "I'm NOT worried about the price of cancer drugs is steep"), "If I get cancer, the cancer drugs expenses are unaffordable for my family" (mirrored item: "If I get cancer, the cancer drugs expenses are affordable for my family"), and "Cancer drugs insurance could avoid economic risk caused by cancer". All five items were measured on five-point scales ranging from 1 (disagree) to 5 (agree). Consequently, the perceived risk related to developing cancer was measured by summing the scores of the above two dimensions that ranging from 5 to 25. A higher score indicates a heightened level of perceived risk. Part E : Selected demographic characteristics Previous research has suggested that sociodemographic characteristics would potentially affect people's health insurance purchasing decisions. We then selected seven demographic characteristics (age, gender, marital, residence, education, health insurance type, and annual disposable income individually) as control variables. It should be noted that the reason why we did not include variables such as known about CDI and CDI purchasing experience is that factors like strong information endowment of these subjects may cause biased survey results, due to such population are more active online. Quality guarantee Preset survey experiment 200 undergraduates of Nanjing Medical University were selected for pre-test. Based on their questionnaire responses and subsequent interview results, we then further polished the LRIF and HRIF narratives, refined the time orientation and perceived risk scale items, and estimated the time required for the formal survey experiment. Formal survey experiment Firstly, the LRIF and HRIF interfaces were forced to stay at least 10 and 25 seconds, respectively, and subjects could only continue the questionnaire response by clicking the "Reading Complete" button. Secondly, limited one IP address one response to avoid duplicated data collection. Thirdly, eliminated questionnaires that answer less than 180 seconds. Fourthly, excluded low quality questionnaires with large area (exceed 10%) empty value and required items "0" value. Finally, in accordance with the above standards, carried out 3 waves of real-time quality control survey experiments(Carcioppolo et al., 2022 ), each round spaced 3 to 5 days with around 2000 samples. Post hoc analysis We conducted the reliability and validity for the scales of time orientation and perceived risk. In the mechanism analysis of CDI purchasing decisions, common method variance (CMV), and endogeneity issue mainly caused by missing or unknown omitted variables (e.g., CDI purchasing experience, etc.) might bias our findings, which were both tested as well. In addition, we placed the LRIF and HRIF at the bottom of the questionnaires, then took 15% sample size and carried out the same procedure as the formal survey experiment, to rule out the potential possibility of difference in results that due to the RIFs weakened or strengthened the original time orientation and perceived risk. All the above implemented procedures ensure the robustness of the results and conclusions in this study. Statistical analysis We utilized chi-square test to demonstrate the balance and comparability of valid samples across each study group, applied multiple chi-square test to disclose the systematic differences of subjects' CDI purchasing decisions, and wielded independent sample t test to reveal the systematic differences of subjects' time orientation and perceived risk between LRIF and HRIF frames. Then, based on the aforementioned results, we further tested the mechanism of RIFs (LRIF and HRIF) act on CDI purchasing decisions with the mediating role of perceived risk by using the bootstrap method. Cronbach's α coefficient (threshold is 0.6) was used to test the reliability of the scales, exploratory factor analysis (EFA, KMO value threshold is 0.6) to assess the validity of the scales. Harman's single-factor test (threshold is 50%) was applied for CMV evaluation, and the endogeneity issue was detected by Durbin-Wu-Hausman (DWH) test. All of the statistical analyses were performed using SPSS (version 25.0). The P value of 0.05 was considered to be significant. Ethics approval and consent to participate The Ethical Committee approved this study in the Nanjing Medical University Institutional Review Board Consent Letter No. (2023)576. All methods were carried out in accordance with relevant guidelines and regulations. Subjects were informed of the study's purpose and procedures. In additional, written informed consent to participate in this study was provided by the subjects. Results Subject samples Randomization into the six study groups yielded a total of 5583 valid samples, demonstrating balance across selected demographic characteristics (see Table 1). This balanced distribution suggests that the impact of risk information narratives (LRIF vs HRIF) can be examined by comparing unadjusted results across the study groups. Table 1 Selected demographic characteristics of subject for each study group Variable Group 1.1 Group 1.2 Group 1.3 Group 2.1 Group 2.2 Group 2.3 Total χ 2 P N (%) N (%) N (%) N (%) N (%) N (%) N (%) Age 16.381 0.357 18 to 29 years old 416 (44.4) 441 (47.3) 426 (44.6) 437 (47.7) 442 (47.9) 455 (49.6) 2617 (46.9) 30 to 39 years old 344 (36.8) 342 (36.7) 334 (34.9) 313 (34.1) 323 (35.0) 300 (32.7) 1956 (35.0) 40 to 49 years old 110 (11.8) 106 (11.4) 127 (13.3) 111 (12.1) 108 (11.7) 110 (12.0) 672 (12.0) 50 to 60 years old 66 (7.1) 44 (4.7) 69 (7.2) 57 (6.2) 49 (5.3) 53 (5.8) 338 (6.1) Gender 1.446 0.919 Male 482 (51.5) 499 (53.5) 489 (51.2) 475 (51.7) 482 (52.3) 485 (52.8) 2912 (52.2) Female 454 (48.5) 434 (46.5) 467 (48.8) 443 (48.3) 440 (47.7) 433 (47.2) 2671 (47.8) Marital 0.872 0.972 Married 515 (55.0) 503 (53.9) 532 (55.6) 496 (54.0) 500 (54.2) 504 (54.9) 3050 (54.6) Not married 421 (45.0) 430 (46.1) 424 (44.4) 422 (46.0) 422 (45.8) 414 (45.1) 2533 (45.4) Residence 7.540 0.183 Rural 285 (30.4) 320 (34.3) 319 (33.4) 288 (31.4) 319 (34.6) 323 (35.2) 1854 (33.2) Urban 651 (69.6) 613 (65.7) 637 (66.6) 630 (68.6) 603 (65.4) 595 (64.8) 3729 (66.8) Annual disposable income individually 13.239 0.584 Up to 19999 CNY 208 (22.2) 220 (23.6) 241 (25.2) 213 (23.2) 227 (24.6) 226 (24.6) 1335 (23.9) 20000 to 29999 CNY 106 (11.3) 107 (11.5) 87 (9.1) 89 (9.7) 79 (8.6) 89 (9.7) 557 (10.0) 30000 to 49999 CNY 400 (42.7) 386 (41.4) 394 (41.2) 416 (45.3) 403 (43.7) 385 (41.9) 2384 (42.7) At least 50000 CNY 222 (23.7) 220 (23.6) 234 (24.5) 200 (21.8) 213 (23.1) 218 (23.7) 1307 (23.4) Education 19.021 0.520 Junior 24 (2.6) 27 (2.9) 33 (3.5) 26 (2.8) 39 (4.2) 30 (3.3) 179 (3.2) Senior 128 (13.7) 123 (13.2) 143 (15.0) 127 (13.8) 107 (11.6) 140 (15.3) 768 (13.8) Associate 252 (26.9) 240 (25.7) 251 (26.3) 227 (24.7) 215 (23.3) 235 (25.6) 1420 (25.4) Undergraduate 488 (52.1) 492 (52.7) 491 (51.4) 494 (53.8) 517 (56.1) 472 (51.4) 2954 (52.9) Postgraduate 44 (4.7) 51 (5.5) 38 (4.0) 44 (4.8) 44 (4.8) 41 (4.5) 262 (4.7) Health insurance type 14.703 0.793 Free 75 (8.0) 85 (9.1) 89 (9.3) 84 (9.2) 79 (8.6) 98 (10.7) 510 (9.1) Urban employees 408 (43.6) 379 (40.6) 396 (41.4) 375 (40.8) 367 (39.8) 366 (39.9) 2291 (41.0) Urban and rural residents 397 (42.4) 409 (43.8) 410 (42.9) 408 (44.4) 419 (45.4) 389 (42.4) 2432 (43.6) Commercial 41 (4.4) 35 (3.8) 42 (4.4) 30 (3.3) 37 (4.0) 38 (4.1) 223 (4.0) Without 15 (1.6) 25 (2.7) 19 (2.0) 21 (2.3) 20 (2.2) 27 (2.9) 127 (2.3) Sample size 936 933 956 918 922 918 5583 [Insert table 1 here] Purchasing decisions for CDI schemes Table 2 presents numbers (frequency) and their statistically significant differences of the HRIF treatment effect on CDI purchasing decisions of subjects compared with LRIF control group. Under LRIF, as the period of free cancer drugs provided increased, the proportion of option 0 (not insured) and Option 1 (just insured 1 year, 179 CNY premium) decreased from 18.5% to 7.5%, and 40.5% to 28.2%, respectively, while the proportion of option 2 (insured 3 years consecutively, 179 CNY premium) increased from 41.0% to 64.2%. Under HRIF, correspondingly, the proportion of option 0 and option 1 reduced from 5.3% to 1.6%, and 28.0% to 15.5%, respectively, while the proportion of option 2 raised from 66.7% to 82.9%. The proportion of option 0 in groups 1.1, 1.2, and 1.3 was higher than that in groups 2.1 (18.5% vs 5.3%), 2.2 (12.8% vs 4.2%), and 2.3 (7.5% vs 1.6%). The proportion of option 1 in groups 1.1, 1.2, and 1.3 was higher than that in groups 2.1 (40.5% vs 28.0%), 2.2 (32.7% vs 24.7%), and 2.3 (28.2% vs 15.5%). Conversely, the proportion of option 2 in groups 1.1, 1.2, and 1.3 was lower than that in groups 2.1 (41.0% vs 66.7%), 2.2 (54.6 vs 71.0%), and 2.3 (64.2% vs 82.9%). Thus, H1 was verified. Table 2 Results of purchasing decisions for CDI schemes Framework Group Option 0 Option 1 Option 2 χ 2 P Number (%) Number (%) Number (%) LRIF 1.1 173 (18.5) 379 (40.5) 384 (41.0) 284.736 <0.001 1.2 119 (12.8) 305 (32.7) 509 (54.6) 46.952 <0.001 1.3 72 (7.5) 270 (28.2) 614 (64.2) 1.110 0.574 HRIF 2.1 49 (5.3) 257 (28.0) 612 (66.7) 14.002 0.001 2.2 35 (4.2) 228 (24.7) 655 (71.0) 38.002 <0.001 2.3 15 (1.6) 142 (15.5) 761 (82.9) 190.085 <0.001 χ 2 235.135 157.690 411.077 P <0.001 <0.001 <0.001 Abbreviations: LRIF=low risk information frame; HRIF=high risk information frame. Notes: Option 0=not insured; Option 1=just insured 1 year, 179 CNY premium; Option 2=insured 3 years consecutively, 179 CNY premium. [Insert table 2 here] Time orientation towards CDI purchasing Table 3 displays the variance of agreement degree related to present and future-oriented attitude items expressed in LRIF and HRIF. Subjects under LRIF scored on present-oriented attitude items regarding current cancer risk and premium cost were 4.49 and 4.55, respectively, which were significantly higher than that of the subjects under HRIF (2.79 and 2.83). Inversely, The subjects under LRIF scored on future-oriented attitude items related to security and long-term impact on life were 3.00 and 3.14, respectively, which were significantly lower that of the subjects under HRIF (4.69 and 4.78). Therefore, H2 was verified. Table 3 Results of time orientation towards CDI purchasing Dimensions Items LRIF (N=2825) HRIF (N=2758) Comparison M (SD) M (SD) t (5581) P Present-oriented I focus on my current NOT future cancer risk when I choose the CDI scheme 4.49 (0.807) 2.79 (0.986) 70.545 <0.001 I care more about current premium NOT future cost reduce when I choose the CDI scheme 4.55 (0.717) 2.83 (0.952) 76.430 <0.001 Future-oriented I value more on the future security when I choose the CDI scheme 3.00 (0.886) 4.69 (0.645) 81.155 <0.001 I consider its long-term impact on my future life seriously when I choose the CDI scheme 3.14 (0.808) 4.78 (0.539) 88.905 <0.001 Abbreviations: LRIF=low risk information frame; HRIF=high risk information frame. [Insert table 3 here] Perceived risk related to developing cancer Table 4 shows the difference in scores on items about perceived risk measurement for subjects under LRIF and HRIF. Subjects under LRIF scored points 3.18, 2.90, 2.43, 3.07, and 3.03 on the perceived risk measurement items of worrying about carcinogenic factors, developing cancer, cancer drugs burden, unaffordable for family, and CDI avoids economic loss, respectively, which were statistically significantly lower than that under the HRIF (scored in order of points 4.76, 4.58, 3.98, 4.66 and 4.67, respectively). The above results also indicated that the HRIF developed in this study was effective. Thereby, H3 was verified. Table 4 Results of perceived risk related to developing cancer Dimensions Items LRIF (N=2825) HRIF (N=2758) Comparison M (SD) M (SD) t (5581) P Cancer Carcinogenic factors are ubiquitous in daily living and working conditions 3.18 (0.872) 4.76 (0.563) 79.888 <0.001 I'm worried about developing cancer in my life 2.90 (0.952) 4.58 (0.726) 73.944 <0.001 Financial I'm worried about the price of cancer drugs is steep 2.43 (1.225) 3.98 (1.138) 49.017 <0.001 If I get cancer, the cancer drugs expenses are unaffordable for my family 3.07 (0.949) 4.66 (0.688) 71.675 <0.001 Cancer drugs insurance could avoid economic risk caused by cancer 3.03 (0.855) 4.67 (0.626) 81.836 <0.001 Abbreviations: LRIF=low risk information frame; HRIF=high risk information frame. [Insert table 4 here] Mediating mechanism of RIFs intervene on CDI purchasing decisions Under LRIF, the mediating effect coefficient of present-oriented attitude (POA) on cancer drugs insurance choice (CDIC) was -0.03 [95%CI: -0.072, -0.033], the mediating effect coefficient of future-oriented attitude (FOA) on CDIC was 0.09 [95%CI: 0.164, 0.200]. While under HRIF, the mediating effect coefficient of POA on CDIC was -0.01 [95%CI: -0.024, -0.009], the mediating effect coefficient of FOA on CDIC was 0.02 [95%CI: 0.031, 0.054]. Thus, in general, perceived risk plays a mediating role in the relationship between time orientation and CDI purchasing decisions. With the transition from LRIF to HRIF, the mediating effect proportion of perceived risk increased from 59.4% to 100% in POA acting on CDIC, while decreased from 73.4% to 26.6% in FOA acting on CDIC, RIF shifting effect (from LRIF to HRIF) was about 43.7% (arithmetic average for the mediating effect proportions of POA and FOA). Overall, H4 was validated to some extent. Table 5 shows the results of testing the RIF shifting effect (LRIF transfers to HRIF) with the perceived risk intermediary role by using the bootstrap method. Table 5 Results of mediating mechanism of RIF intervene on CDI purchasing decisions Variables LRIF HRIF POA=>PR=>CDIC FOA=>PR=>CDIC POA=>PR=>CDIC FOA=>PR=>CDIC Coefficient 95% CI Coefficient 95% CI Coefficient 95% CI Coefficient 95% CI Direct effect -0.021 [-0.039, -0.002] 0.033 [0.016, 0.050] -0.005 [-0.018, -0.007] 0.062 [0.041, 0.083] Mediating effect -0.030 [-0.072, -0.033] 0.091 [0.164, 0.200] -0.006 [-0.024, -0.009] 0.022 [0.031, 0.054] Total effect -0.051 [-0.072, -0.029] 0.123 [0.104, 0.142] -0.011 [-0.024, -0.002] 0.085 [0.063, 0.106] Mediating effect proportion (%) 59.4 73.4 100.0 26.6 Abbreviations: LRIF=low risk information frame; HRIF=high risk information frame; POA=present-oriented attitude; FOA=future-oriented attitude; PR=perceived risk; CDIC=cancer drugs insurance choice; CI=confidence intervals. [Insert table 5 here] Post hoc analyses Firstly, Cronbach's α coefficient and KMO values of the time-orientation scale were 0.813 and 0.679 ( P <0.001), the corresponding results of the perceived risk scale were 0.849 and 0.865 ( P <0.001), indicating that both the above two scales meet our study needs. Secondly, results of the Harman's single-factor test showed that the largest (lowest) single factor accounted for 31.7% (18.1%) of the variation, suggesting that the CMV was not a valid threat in this study. Thirdly, the DWH test results provided evidence of endogeneity may be a potential trouble for the regression models in present study, but these endogenically induced bias would be mitigated with random block design, thus not threatening our key conclusions of RIF shifting effect that we were focused. Last but not least, Post-test results with LRIF and HRIF placed at the bottom of the questionnaires showed that no statistically significant systemic differences in CDI purchasing decisions, perceived risk, and time orientation for subjects between treatment and control groups. Appendices D, E, F, and G for details. Discussion This study contributes to the existing literature by employing an online survey experiment to explore the relative contributions of RIFs to the regularity disparities of perceived risk, time orientation, and their interaction mechanism in purchasing decisions for emerging CDI in China. The primary contribution of the present article is to propose that the mechanism by which the HRIF could stimulate CDI purchasing decisions is that it weakens individuals' POA while strengthens their FOA with the mediating role of perceived risk. Speculating on the policy implications of RIF's impact on CDI development, marketing strategies for CDI should try to make advertising appear comprehensive and detailed risk information narratives. For instance, emphasizing the high risk of incidence rate and financial burden associated with cancer meticulously in CDI advertising may reduce the perceived immediate costs and enhance perceived delayed benefits associated with CDI purchasing. Emerging CDI is full of great significance for relieving Chinese cancer burden, one of the most populous countries with the largest population of cancer patients globally (Feng et al., 2019). However, as far as the risk information narrative is concerned, our experimental results suggest that the advertising being executed (examples see Appendix A) is insufficient for CDI marketing expansion that based on online sales. The differentiated results of choosing CDI schemes under LRIF and HRIF show that the powerful capacity of episodic narrative with meticulous risk information in significantly promoting the possibility of subjects to insure long-term CDI schemes, and inhibiting the possibility of subjects to not insure or insure short-term CDI schemes. Much of the health behavior interventions that have focused on how to inform people about health risks (e.g., cancer, smoking, drinking, and unhealthy eating etc.) and then nudge them to take actions for avoiding these risks, with less or more achieved desirable experimental or empirical outcomes (Block & Keller, 1995; Pechmann & Catlin, 2016; You et al., 2023). These possess important policy enlightenment and practical application value for raising the level of CDI financing and further achieving sustainable development. Partial scholars argued that framing risk information may be not helpful to shape individuals' time orientation (Kees, 2010). However, we found that HRIF could simultaneously restrain POA and reinforce FOA. According to CLT, temporal distance will influence individuals to view distant future options abstractly and near future options concretely, with remote events having less psychological impact on people than immediate events due to their abstractness (Trope & Liberman, 2000). Consequently, possible reasons may be that for CDI purchasing decisions of the subjects, poor risk information perhaps limit them to focus on immediate costs, meticulous risk information may let them pay more attention to delayed benefits. In addition, to our surprise, the post-test results with removed RIF effect look better than that in LRIF (lower uninsured rates and POA levels, higher FOA and perceived risk levels), probably on account of the crude risk information dulls individuals' sensitivity to risk. This is a reminder of the fact that the risk information being presented by sellers currently may run counter to their desire to motivate consumers to purchase CDI via advertising in online marketplaces. Our findings demonstrate that perceived risk, introduced as a mediating variable, could partially identify the influencing mechanism of HRIF on CDI purchasing decisions. We did acknowledge that endogenous troubles would cause biased estimates for regression models themselves, but not for RIF shifting effect with random block design. Moreover, the RIF shift effect size of 43.7% makes it impossible for us to deny the utility of HRIF. Previous research indicates that proximal framing linked health risk significantly increases risk perception of those who normally do not consider future consequences of their behaviors(Kees, 2010), the aforementioned research evidences and theories demonstrate the strong relationship between time orientation and perceived risk. Therefore, the principle may be that when LHIF converted to HRIF, rich and full risk information narratives significantly restrain POA and strengthen FOA at the same time with the mediating role of perceived risk, finally making subjects more inclined to focus on purchasing decisions for insuring and insuring long-term CDI schemes. In sum, the advertising currently being implemented is poor to spur CDI's online sales growth, HRIF is worth considering for intervening individuals' CDI purchasing decisions. Limitations Several limitations mostly related to the online survey experiment used in our study must be acknowledged. First, inherent defects of self-report method for assessing CDI purchase decision. Second, there exist shortcomings in the measurements of perceived risk and time orientation due to limited availability of the online survey data. Third, important control variables, such as CDI purchasing experience, were excluded because of online data bias restrictions. Fourth, despite we sampled representatively in a country that has over 20% of the world's population, it still remains an open question whether RIF shapes CDI decision-making to the same extent in Eastern and Western societies with diverse cultural contexts. However, these limitations provide directions for future research endeavors. Conclusion In recent years, attention to impact of information frames on population health behavior are becoming international concern. Yet, we are unaware of any published research that have explicitly explored the causal role of HRIF in shaping CDI purchasing decisions and its action mechanism. This study demonstrates that risk information narrative in current advertising is ineffective, and HRIF stimulates individuals' CDI purchasing decisions by weakening POA and strengthening FOA with the mediating role of perceived risk. In a world where risk information is influencing decision-making in every aspect of human society, many people remain trapped in unhealthy behavior patterns due to misled by wrong or false risk information related to health communication advertising. Our findings provide a reliable and practical basis for relevant authorities formulate RIF as health interventions for CDI purchasing decisions to reduce health damage and financial burden of cancer. Declarations Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (72374110), the State Scholarship Fund of China Scholarship Council (202308320379), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_1900). Author Contribution Z.S. and D.Q. conceived the research design. Z.S. and Z.Z. conducted the statistical analysis and wrote the main manuscript text. X.C. and D.Q. supervised the manuscript writing. 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B MC Public Health , 23(1) http://doi.org/10.1186/s12889-023-16814-1 Zomerdijk, N., Jongenelis, M., Short, C. E., Smith, A., Turner, J., & Huntley, K. (2021). Prevalence and correlates of psychological distress, unmet supportive care needs, and fear of cancer recurrence among haematological cancer patients during the COVID-19 pandemic. Supportive Care in Cancer , 29(12), 7755-7764. http://doi.org/10.1007/s00520-021-06369-5 Additional Declarations No competing interests reported. Supplementary Files Appendices.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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In 2020 alone, approximately 2.4\u0026nbsp;million Chinese residents succumbed to cancer (Qi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with the economic cost estimated to reach USD 6.1 trillion nationally from 2020 to 2050 (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While specific anticancer drugs can significantly reduce mortality and tumor progression, their exorbitant prices pose a significant barrier (Michaeli \u0026amp; Michaeli, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Allied Market Research (AMR), global spending on cancer drugs was approximately USD 135.5\u0026nbsp;billion in 2020, projected to reach USD 274.4\u0026nbsp;billion by 2030 (AMR, 2021). In China, domestic targeted anticancer drugs often exceed USD 3786 per month (Luo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), rendering them unaffordable for most families (Prasad et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe escalating costs of anticancer drugs have garnered international attention (Yasaitis et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the public health insurance system does not adequately shield individual cancer patients from financial toxicity (Chambers et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Desai \u0026amp; Gyawali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Incentivizing specialty drugs coverage in commercial health plans may increase tumor treatments access for patients (Chambers et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while cancer drug insurance (CDI) commercial coverage in China remains inadequate. Disparities in health insurance participation were ongoing concerns, with social psychologists and behavioral economists positing that perceptions and attitudes toward time and risk may explain these disparities (Loewenstein et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rice, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and leveraging the frame effect of information presentation in promotional strategies is considered an effective tool to influence health insurance behavior (Aizawa \u0026amp; Kim, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of China's burgeoning \"Internet plus\" development, online sales are catalyzing the rise of CDI, garnering increased attention in recent years. Nevertheless, as our observation for nowaday common online CDI advertisements (examples see Appendix A), we found that rough narrative of cancer-related risk information, and same risk information frame (RIF) for different CDI schemes is an universal phenomenon in CDI advertising. We refer to rough narratives as low risk information frame (LRIF) and meticulous narratives as high risk information frame (HRIF). Considering these factors together, it is reasonable to speculate that inefficiently linking advertising to risk information is a significant factor contributing to weakened CDI sales status in China, although we have not found studies confirming this.\u003c/p\u003e \u003cp\u003eAlso, despite existing studies offer initial insights into the correlation between perceived risk and time orientation and the use of RIF in promoting health behaviors, the majority were conducted using retrospective data or laboratory experiments with students as subjects, providing inconclusive evidence of the potential efficacy of RIF in health policy making. Undoubtedly, effective debiasing strategies could be practically applied to gain causal understanding of factors contributing to bias, and online survey experiments may counter biased data associated with health-related decision-making (Cao \u0026amp; Li, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, this study provides a contextualized simulation aligned with current emerging CDI online sales advertising, aiming to understand the unique ability of RIF in shaping individuals' time orientation, perceived risk, and focusing specifically on CDI purchasing decision mechanisms. Such research has powerful implications for making health policies to promote CDI, which as an emerging and prominent insurance product in China. Unless specified otherwise, the CDI discussed in present study is commercial.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eInformation framing\u003c/h2\u003e \u003cp\u003eInformation framing refers to how individuals respond differently based on the positive or negative description of information (Ferguson \u0026amp; Gallagher, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Two prominent models (risk, and gain-loss framed) propose that the titer effects of information frame are moderated by framing methods(Kwasny et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and perceived risk(Pakseresht et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Risky and temporal framing are the two common formats for framing information or advertising in health domain (Kees, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There is evidence that the way information is framed influences decision-making with respect to a variety of health behaviors(Aizawa \u0026amp; Kim, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim \u0026amp; Nan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lewis \u0026amp; Atad, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, results from a controlled experiment on temporal framing effects in HPV vaccination indicated that a present-oriented message presented in a narrative format led to more favorable attitudes, stronger intentions, and perceived efficacy towards the vaccine (Kim \u0026amp; Nan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), exposure to narrative messages was also found to be positively associated with intentions to discuss influenza vaccination (Lewis \u0026amp; Atad, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany theories could be used to interpret the effectiveness of information frames and how they work. Prospect theory (PT), temporal discounting theory (TDT), and construal level theory (CLT) mainly in this regard. The framing postulate of PT indicates that health-relevant information presented in terms of value and risk can shape individual perceptions to motivate healthy behavior (Rothman \u0026amp; Salovey, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). TDT treats the reduction in the value of an intensifier as a function of its receiver delay, revealing the psychological phenomenon that an individual's value assessment for events decreases over time (Bickel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CLT proposes the concept of temporal construal, suggesting that the time-distance effect arises from an active network of mental representations towards future objects, predicting that feasibility concerns should receive less weight as time distance increases (Lange et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consequently, we observe a close relationship between information, time, risk, and value evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePerceived risk\u003c/h2\u003e \u003cp\u003eThe weighting of benefits and costs in insurance decision-making may also depend on individuals' perceptions and attitudes toward the risks associated with cancer (Soane et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Perceived risk arises from uncertainty about carcinogenic factors and the economic burden prior to cancer diagnosis (Sum \u0026amp; Nordin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Individual subjective assessments of their risk and recovery probabilities from health and financial trauma caused by cancer development seem pivotal in enrolling in CDI. Theoretical models examining the correlation between perceived risk and medical insurance purchasing yield mixed predictions. There is evidence that perceived risk clearly pushes the average willingness to pay for health insurance below the fair price (Baillon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there is also evidence that perceived risk related to the chances of getting cancer is not associated with health behaviors like cancer exams (Bowen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers are committed to deepening our understanding of the driving mechanism of perceived risk, and exploring how to improve the public's risk identification ability and decision-making quality through efficient risk communication. Existing research indicates that message content framing in combination with health-related risk communications is able to have significant and measurable effects on consumer cognition, emotion, and behavior (Pechmann \u0026amp; Catlin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, some analyses have shown that perceived risk plays a mediating role in the influence of online food safety information acquisition on food risk prevention behaviors (You et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While, this potentially important framing technique that has not received enough attention is the differentiated risk narratives in the context of online sales advertising for CDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTime orientation\u003c/h2\u003e \u003cp\u003eTime orientation refers to how individuals value distant outcomes relative to present ones (Goldzahl, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Evidence drawn from CLT suggests that health messages linked temporal framing effects could influence individuals' time orientation towards hazards and losses in health, and temporal framing effects in health advertising have also been found to affect future-orientation on perceived risk and behavioral intentions (Kees, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The latest findings from a multilevel meta-analysis on temporal framing effects showed that gain (versus loss) framing was a significant moderator for temporal influence on promoting healthy eating and anti-smoking/drinking behaviors, and proximal frames were more effective at increasing risk perception than distal frames (Huang \u0026amp; Xu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, although numerous literature indicated that a close connection between perceived risk and time orientation, some scholars do not believe that framing risk information would systematically change time orientation (Kees, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTime orientation with systematic differences distinguishes individuals who stress-emphasize immediate versus delayed consequences (present-orientation versus future-orientation) of their actions, and individuals with different time orientations show heterogeneity in health-related behaviors (T\u0026oacute;rtora \u0026amp; Ares, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). An online experiment revealed that stronger present-orientation predicted greater smoking intentions, while future-orientation predicted greater quit intentions, and in addition, future (versus present) thinking significantly improved intentions to give up smoking by enhancing perceived self-efficacy for cessation (Nan \u0026amp; Qin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given that CDI purchasing decisions involve trade-offs between delayed benefits (relieve financial burden if found cancer, or give security sense during guarantee period) and immediate costs (premium cost, monetary loss aversion), individuals' weighting of long-term versus short-term consequences may influence their decisions. Therefore, future-oriented individuals may place higher value on health insurance benefits than present-oriented individuals (Han, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Literature review mentioned as above, the influence of risky framing on time orientation is not well understood, especially regarding whether existing risk advertising is effective that has been applied in practice for CDI promotion, whether framing differentiated risk narratives would drive individuals' consumption on CDI options that dependent on time orientation and its action mechanism. Thereupon, we proposed the following basic hypotheses regarding RIF, perceived risk, and time orientation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: A much more meticulous risk information narrative would facilitate individual's CDI purchasing decisions for insuring or insuring long-term schemes.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: A much more meticulous risk information narrative would strengthen individual's future-orientation and weaken present-orientation regarding CDI purchasing.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH3: A much more meticulous risk information narrative would increase individual's perceived risk related to developing cancer.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH4: The perceived risk would play a mediating role in the influence of time orientation on CDI purchasing decisions.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003e This study utilized an online survey experiment, which was approved by Nanjing Medical University's Institutional Review Board in October 2023 and was implemented by wenjuan.com in November 2023. Wenjuan.com recruited contributing subjects willing to engage in the experiment from its sample bank of 8\u0026nbsp;million people. For making the sample more nationally representative, we required provincial and gender distribution characteristics of the samples that wenjuan.com collected were similar to the data published by the National Bureau of Statistics of China in 2022. The experiment was self-administered and accessible at any time during the designated period. Upon completion of each experiment, wenjuan.com randomly awarded subjects with 200 to 500 website points and CNY 1 to 3 cash rewards. Subjects were allowed to complete the experiment only once and could leave at any time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample\u003c/h2\u003e \u003cp\u003eThis study initially included a random sample of 6044 adults aged from 18 to 60 years old, who were members of the wenjuan.com online research panel. Upon initiation of the study, participants were unaware of whether they were in the treatment (HRIF) or control (LRIF) group. Subjects were informed that the survey pertained to consumption on CDI, and no precise information on the survey's topic was provided during the recruitment process (Goldzahl, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). After excluding invalid samples based on criteria such as minimum answer time, empty value limitation, and repeated responses, a total of 5583 valid samples were included for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOnline survey experiment design\u003c/h2\u003e \u003cp\u003eWe conducted a randomized online survey experiment nationally to explore a 2 (Part A, RIF: LRIF, HRIF) \u0026times; 3 (Part B, CDI schemes: Distinguished by free cancer drugs provided duration of 1, 2, or 3 years) factorial design on the primary outcome: subject's choices for CDI schemes; as well as secondary outcomes about time orientation (Part C) and perceived risk (Part D) related to CDI purchasing, along with selected demographic characteristics (Part E) of subjects. Consequently, subjects were divided into groups (r,j) randomly according to the questionnaire version they response to, which was extracted randomly from the six questionnaire versions generated based on RIF and CDI schemes. The details are as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003ePart A\u003c/b\u003e: Risk information frames (RIFs)\u003c/p\u003e \u003cp\u003eWe extracted LRIF from common online CDI advertising, which served as the control group and simulated the current online CDI risk exposition in the market. Correspondingly, HRIF as the treatment group was created with a more meticulous narrative of cancer itself and its derived financial risks mainly, based on CLT (Huang \u0026amp; Xu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kim \u0026amp; Nan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lange et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in behavioral economics and relevant references(Block \u0026amp; Keller, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Bonner et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kreuter et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lewis \u0026amp; Atad, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The RIFs see Appendix B.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePart B\u003c/b\u003e: CDI schemes\u003c/p\u003e \u003cp\u003eAge serves as the primary determinant for CDI online pricing. We knew the 40-something age group was the body of CDI purchasing form executives at several large CDI companies. Thus, we performed age 40 as a parameter into the top 6 mainstream online CDI subscription systems, and found that the average premium for insuring 1 year was CNY 176.62. Then, the premium for just insuring 1 year was set at CNY 179 in our CDI schemes. After discounting according to the China's average inflation rate (2%-3%) over the past decade and the 3-year fixed deposit rate (2.20%) published by the Bank of China, the premium for insuring 3 years consecutively was priced at CNY 521 in our CDI schemes.\u003c/p\u003e \u003cp\u003eThe final premium determination in our study also considered the principle of anchoring effect in behavioral economics(Baillon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which was to increase the premium mental calculation complexity for weakening the interference of linear price growth on the subject's choices of CDI schemes, in this way, the decision results for the subjects were more reflected in the time orientation, rather than the premium change itself. The only difference between the 3 CDI schemes is the duration free cancer drugs provided, see Appendix C.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePart C\u003c/b\u003e: Time orientation towards CDI purchasing\u003c/p\u003e \u003cp\u003eTo avoid order effect and propensity scoring of the subjects' responses, partial mirrored items were set and presented randomly with the wenjuan.com random question function, and the final scores of each item were displayed after the same trend processing. Time orientation was designated as the independent variable and categorized into present-oriented and future-oriented attitudes (Goldzahl, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), measured with psychometric scales is associated with current cancer risk and premium cost, future security, and long-term impact on life(Kees, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present-oriented dimension was assessed using two items: \"I focus on my current NOT future cancer risk when I choose the CDI scheme\" (mirrored item: \"I focus on my future NOT current cancer risk when I choose the CDI scheme\") and \"I care more about current premium NOT future cost reduce when I choose the CDI scheme\" (mirrored item: \"I care more about future cost reduce NOT current premium when I choose the CDI scheme\").\u003c/p\u003e \u003cp\u003eThe future-oriented dimension was evaluated using two items: \"I value more on the future security when I choose the CDI scheme\" and \"I consider its long-term impact on my future life seriously when I choose the CDI scheme\". All four items were measured on five-point scales ranging from 1 (disagree) to 5 (agree). Accordingly, the present-oriented and future-oriented attitudes dimension both are scored on a scale of 2\u0026ndash;10, with higher scores indicating a stronger corresponding time orientation.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePart D\u003c/b\u003e: Perceived risk related to developing cancer\u003c/p\u003e \u003cp\u003eThe methodology for Part D was established following the same approach as for Part C. We set perceived risk as the mediating variable in this study that consists of cancer and financial by reference to the research(Molina et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Persoskie et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zomerdijk et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To assess the dimension of cancer, we employed two items focusing on the presence of carcinogenic factors and concerns about developing cancer: \"Carcinogenic factors are ubiquitous in daily living and working conditions\" and \"I'm worried about developing cancer in my life\" (mirrored item: \"I'm NOT worried about developing cancer in my life\").\u003c/p\u003e \u003cp\u003eTo evaluate the financial dimension, we utilized 3 items concentrating on the risk of cancer drugs burden, unaffordable for family, and CDI avoids economic loss: \"I'm worried about the price of cancer drugs is steep\" (mirrored item: \"I'm NOT worried about the price of cancer drugs is steep\"), \"If I get cancer, the cancer drugs expenses are unaffordable for my family\" (mirrored item: \"If I get cancer, the cancer drugs expenses are affordable for my family\"), and \"Cancer drugs insurance could avoid economic risk caused by cancer\".\u003c/p\u003e \u003cp\u003eAll five items were measured on five-point scales ranging from 1 (disagree) to 5 (agree). Consequently, the perceived risk related to developing cancer was measured by summing the scores of the above two dimensions that ranging from 5 to 25. A higher score indicates a heightened level of perceived risk.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePart E\u003c/b\u003e: Selected demographic characteristics\u003c/p\u003e \u003cp\u003ePrevious research has suggested that sociodemographic characteristics would potentially affect people's health insurance purchasing decisions. We then selected seven demographic characteristics (age, gender, marital, residence, education, health insurance type, and annual disposable income individually) as control variables. It should be noted that the reason why we did not include variables such as known about CDI and CDI purchasing experience is that factors like strong information endowment of these subjects may cause biased survey results, due to such population are more active online.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQuality guarantee\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003ePreset survey experiment\u003c/h2\u003e \u003cp\u003e200 undergraduates of Nanjing Medical University were selected for pre-test. Based on their questionnaire responses and subsequent interview results, we then further polished the LRIF and HRIF narratives, refined the time orientation and perceived risk scale items, and estimated the time required for the formal survey experiment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFormal survey experiment\u003c/h2\u003e \u003cp\u003eFirstly, the LRIF and HRIF interfaces were forced to stay at least 10 and 25 seconds, respectively, and subjects could only continue the questionnaire response by clicking the \"Reading Complete\" button. Secondly, limited one IP address one response to avoid duplicated data collection. Thirdly, eliminated questionnaires that answer less than 180 seconds. Fourthly, excluded low quality questionnaires with large area (exceed 10%) empty value and required items \"0\" value. Finally, in accordance with the above standards, carried out 3 waves of real-time quality control survey experiments(Carcioppolo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), each round spaced 3 to 5 days with around 2000 samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePost hoc analysis\u003c/h2\u003e \u003cp\u003eWe conducted the reliability and validity for the scales of time orientation and perceived risk. In the mechanism analysis of CDI purchasing decisions, common method variance (CMV), and endogeneity issue mainly caused by missing or unknown omitted variables (e.g., CDI purchasing experience, etc.) might bias our findings, which were both tested as well. In addition, we placed the LRIF and HRIF at the bottom of the questionnaires, then took 15% sample size and carried out the same procedure as the formal survey experiment, to rule out the potential possibility of difference in results that due to the RIFs weakened or strengthened the original time orientation and perceived risk. All the above implemented procedures ensure the robustness of the results and conclusions in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe utilized chi-square test to demonstrate the balance and comparability of valid samples across each study group, applied multiple chi-square test to disclose the systematic differences of subjects' CDI purchasing decisions, and wielded independent sample t test to reveal the systematic differences of subjects' time orientation and perceived risk between LRIF and HRIF frames. Then, based on the aforementioned results, we further tested the mechanism of RIFs (LRIF and HRIF) act on CDI purchasing decisions with the mediating role of perceived risk by using the bootstrap method. Cronbach's α coefficient (threshold is 0.6) was used to test the reliability of the scales, exploratory factor analysis (EFA, KMO value threshold is 0.6) to assess the validity of the scales. Harman's single-factor test (threshold is 50%) was applied for CMV evaluation, and the endogeneity issue was detected by Durbin-Wu-Hausman (DWH) test. All of the statistical analyses were performed using SPSS (version 25.0). The \u003cem\u003eP\u003c/em\u003e value of 0.05 was considered to be significant.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethical Committee approved this study in the Nanjing Medical University Institutional Review Board Consent Letter No. (2023)576. All methods were carried out in accordance with relevant guidelines and regulations. Subjects were informed of the study\u0026apos;s purpose and procedures. In additional, written informed consent to participate in this study was provided by the subjects.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSubject samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRandomization into the six study groups yielded a total of 5583 valid samples, demonstrating balance across selected demographic characteristics (see Table 1). This balanced distribution suggests that the impact of risk information narratives (LRIF vs HRIF) can be examined by comparing unadjusted results across the study groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelected demographic characteristics of subject for each study group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.114832535885167%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.114832535885167%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.114832535885167%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.114832535885167%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.114832535885167%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.114832535885167%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.311004784688995%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e16.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003e18 to 29 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e416 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e441 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e426 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e437 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e442 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e455 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2617 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003e30 to 39 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e344 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e342 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e334 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e313 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e323 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e300 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e1956 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003e40 to 49 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e110 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e106 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e127 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e111 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e108 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e110 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e672 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003e50 to 60 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e66 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e44 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e69 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e57 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e49 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e53 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e338 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e1.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e482 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e499 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e489 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e475 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e482 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e485 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2912 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e454 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e434 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e467 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e443 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e440 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e433 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2671 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eMarital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e515 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e503 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e532 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e496 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e500 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e504 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e3050 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eNot married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e421 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e430 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e424 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e422 (46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e422 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e414 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2533 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e7.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e285 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e320 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e319 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e288 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e319 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e323 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e1854 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e651 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e613 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e637 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e630 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e603 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e595 (64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e3729 (66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eAnnual disposable income individually\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e13.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eUp to 19999 CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e208 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e220 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e241 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e213 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e227 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e226 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e1335 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003e20000 to 29999 CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e106 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e107 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e87 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e89 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e79 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e89 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e557 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003e30000 to 49999 CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e400 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e386 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e394 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e416 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e403 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e385 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2384 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eAt least 50000 CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e222 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e220 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e234 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e200 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e213 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e218 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e1307 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e19.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e24 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e27 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e33 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e26 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e39 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e30 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e179 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e128 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e123 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e143 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e127 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e107 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e140 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e768 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eAssociate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e252 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e240 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e251 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e227 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e215 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e235 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e1420 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e488 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e492 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e491 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e494 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e517 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e472 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2954 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e44 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e51 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e38 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e44 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e44 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e41 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e262 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eHealth insurance type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e14.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eFree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e75 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e85 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e89 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e84 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e79 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e98 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e510 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eUrban employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e408 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e379 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e396 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e375 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e367 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e366 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2291 (41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eUrban and rural residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e397 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e409 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e410 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e408 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e419 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e389 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2432 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eCommercial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e41 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e35 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e42 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e30 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e37 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e38 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e223 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eWithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e15 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e25 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e19 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e21 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e20 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e27 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e127 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.88607594936709%\" valign=\"top\"\u003e\n \u003cp\u003eSample size\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e936\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.335443037974683%\" valign=\"top\"\u003e\n \u003cp\u003e918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e5583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.962025316455696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.012658227848101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e[Insert\u0026nbsp;table 1\u0026nbsp;here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurchasing decisions for CDI schemes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents numbers (frequency) and their statistically significant differences of the HRIF treatment effect on CDI purchasing decisions of subjects compared with LRIF control group. Under LRIF, as the period of free cancer drugs provided increased, the proportion of option 0 (not insured) and Option 1 (just insured 1 year, 179 CNY premium) decreased from 18.5% to 7.5%, and 40.5% to 28.2%, respectively, while the proportion of option 2 (insured 3 years consecutively, 179 CNY premium) increased from 41.0% to 64.2%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder HRIF, correspondingly, the proportion of option 0 and option 1 reduced from 5.3% to 1.6%, and 28.0% to 15.5%, respectively, while the proportion of option 2 raised from 66.7% to 82.9%. The proportion of option 0 in groups 1.1, 1.2, and 1.3 was higher than that in groups 2.1 (18.5% vs 5.3%), 2.2 (12.8% vs 4.2%), and 2.3 (7.5% vs 1.6%). The proportion of option 1 in groups 1.1, 1.2, and 1.3 was higher than that in groups 2.1 (40.5% vs 28.0%), 2.2 (32.7% vs 24.7%), and 2.3 (28.2% vs 15.5%). Conversely, the proportion of option 2 in groups 1.1, 1.2, and 1.3 was lower than that in groups 2.1 (41.0% vs 66.7%), 2.2 (54.6 vs 71.0%), and 2.3 (64.2% vs 82.9%). Thus, H1 was verified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of purchasing decisions for CDI schemes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFramework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOption 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOption 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOption 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e173 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e379 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e384 (41.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e284.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e305 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e509 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e270 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e614 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e257 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e612 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e228 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e655 (71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e761 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e235.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e157.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e411.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LRIF=low risk information frame; HRIF=high risk information frame.\u003c/p\u003e\n\u003cp\u003eNotes: Option 0=not insured; Option 1=just insured 1 year, 179 CNY premium; Option 2=insured 3 years consecutively, 179 CNY premium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert\u0026nbsp;table 2\u0026nbsp;here]\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime orientation towards CDI purchasing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 displays the variance of agreement degree related to present and future-oriented attitude items expressed in LRIF and HRIF. Subjects under LRIF scored on present-oriented attitude items regarding current cancer risk and premium cost were 4.49 and 4.55, respectively, which were significantly higher than that of the subjects under HRIF (2.79 and 2.83). Inversely, The subjects under LRIF scored on future-oriented attitude items related to security and long-term impact on life were 3.00 and 3.14, respectively, which were significantly lower that of the subjects under HRIF (4.69 and 4.78). Therefore, H2 was verified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of time orientation towards CDI purchasing\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.15520282186949%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.638447971781305%\" valign=\"top\"\u003e\n \u003cp\u003eLRIF (N=2825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.109347442680775%\" valign=\"top\"\u003e\n \u003cp\u003eHRIF (N=2758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.086142322097377%\" valign=\"top\"\u003e\n \u003cp\u003eM (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.9625468164794%\" valign=\"top\"\u003e\n \u003cp\u003eM (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.726591760299627%\" valign=\"top\"\u003e\n \u003cp\u003et (5581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.901408450704224%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePresent-oriented\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.09154929577465%\" valign=\"top\"\u003e\n \u003cp\u003eI focus on my current NOT future cancer risk when I choose the CDI scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e4.49 (0.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e2.79 (0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" valign=\"top\"\u003e\n \u003cp\u003e70.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.507042253521126%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.432203389830505%\" valign=\"top\"\u003e\n \u003cp\u003eI care more about current premium NOT future cost reduce when I choose the CDI scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e4.55 (0.717)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.949152542372882%\" valign=\"top\"\u003e\n \u003cp\u003e2.83 (0.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.59322033898305%\" valign=\"top\"\u003e\n \u003cp\u003e76.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.440677966101696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.901408450704224%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFuture-oriented\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.09154929577465%\" valign=\"top\"\u003e\n \u003cp\u003eI value more on the future security when I choose the CDI scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e3.00 (0.886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e4.69 (0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" valign=\"top\"\u003e\n \u003cp\u003e81.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.507042253521126%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.432203389830505%\" valign=\"top\"\u003e\n \u003cp\u003eI consider its long-term impact on my future life seriously when I choose the CDI scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e3.14 (0.808)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.949152542372882%\" valign=\"top\"\u003e\n \u003cp\u003e4.78 (0.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.59322033898305%\" valign=\"top\"\u003e\n \u003cp\u003e88.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.440677966101696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LRIF=low risk information frame; HRIF=high risk information frame.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert table 3 here]\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived risk related to developing cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shows the difference in scores on items about perceived risk measurement for subjects under LRIF and HRIF. Subjects under LRIF scored points 3.18, 2.90, 2.43, 3.07, and 3.03 on the perceived risk measurement items of worrying about carcinogenic factors, developing cancer, cancer drugs burden, unaffordable for family, and CDI avoids economic loss, respectively, which were statistically significantly lower than that under the HRIF (scored in order of points 4.76, 4.58, 3.98, 4.66 and 4.67, respectively). The above results also indicated that the HRIF developed in this study was effective. Thereby, H3 was verified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of perceived risk related to developing cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"106%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.309278350515465%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.577319587628864%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003eLRIF (N=2825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003eHRIF (N=2758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eM (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eM (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003et (5581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.416666666666666%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eCarcinogenic factors are ubiquitous in daily living and working conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3.18 (0.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e4.76 (0.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e79.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.30232558139535%\" valign=\"top\"\u003e\n \u003cp\u003eI\u0026apos;m worried about developing cancer in my life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.790697674418604%\" valign=\"top\"\u003e\n \u003cp\u003e2.90 (0.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e4.58 (0.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" valign=\"top\"\u003e\n \u003cp\u003e73.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.416666666666666%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFinancial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eI\u0026apos;m worried about the price of cancer drugs is steep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.43 (1.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e3.98 (1.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e49.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.30232558139535%\" valign=\"top\"\u003e\n \u003cp\u003eIf I get cancer, the cancer drugs expenses are unaffordable for my family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.790697674418604%\" valign=\"top\"\u003e\n \u003cp\u003e3.07 (0.949)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e4.66 (0.688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" valign=\"top\"\u003e\n \u003cp\u003e71.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.30232558139535%\" valign=\"top\"\u003e\n \u003cp\u003eCancer drugs insurance could avoid economic risk caused by cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.790697674418604%\" valign=\"top\"\u003e\n \u003cp\u003e3.03 (0.855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e4.67 (0.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" valign=\"top\"\u003e\n \u003cp\u003e81.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.13953488372093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LRIF=low risk information frame; HRIF=high risk information frame.\u003c/p\u003e\n\u003cp\u003e[Insert\u0026nbsp;table 4\u0026nbsp;here]\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediating mechanism of RIFs intervene on CDI purchasing decisions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder LRIF, the mediating effect coefficient of present-oriented attitude (POA) on cancer drugs insurance choice (CDIC) was -0.03 [95%CI: -0.072, -0.033], the mediating effect coefficient of future-oriented attitude (FOA) on CDIC was 0.09 [95%CI: 0.164, 0.200]. While under HRIF, the mediating effect coefficient of POA on CDIC was -0.01 [95%CI: -0.024, -0.009], the mediating effect coefficient of FOA on CDIC was 0.02 [95%CI: 0.031, 0.054]. Thus, in general, perceived risk plays a mediating role in the relationship between time orientation and CDI purchasing decisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith the transition from LRIF to HRIF, the mediating effect proportion of perceived risk increased from 59.4% to 100% in POA acting on CDIC, while decreased from 73.4% to 26.6% in FOA acting on CDIC, RIF shifting effect (from LRIF to HRIF) was about 43.7% (arithmetic average for the mediating effect proportions of POA and FOA). Overall, H4 was validated to some extent. Table 5 shows the results of testing the RIF shifting effect (LRIF transfers to HRIF) with the perceived risk intermediary role by using the bootstrap method. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of mediating mechanism of RIF intervene on CDI purchasing decisions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eLRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHRIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePOA=\u0026gt;PR=\u0026gt;CDIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFOA=\u0026gt;PR=\u0026gt;CDIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePOA=\u0026gt;PR=\u0026gt;CDIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFOA=\u0026gt;PR=\u0026gt;CDIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.039, -0.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.016, 0.050]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.018, -0.007]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.041, 0.083]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMediating effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.072, -0.033]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.164, 0.200]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.024, -0.009]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.031, 0.054]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.072, -0.029]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.104, 0.142]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.024, -0.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.063, 0.106]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMediating effect proportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LRIF=low risk information frame; HRIF=high risk information frame; POA=present-oriented attitude; FOA=future-oriented attitude; PR=perceived risk; CDIC=cancer drugs insurance choice; CI=confidence intervals.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[Insert\u0026nbsp;table 5\u0026nbsp;here]\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost hoc analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, Cronbach\u0026apos;s \u0026alpha; coefficient and KMO values of the time-orientation scale were 0.813 and 0.679 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), the corresponding results of the perceived risk scale were 0.849 and 0.865 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), indicating that both the above two scales meet our study needs. Secondly, results of the Harman\u0026apos;s single-factor test showed that the largest (lowest) single factor accounted for 31.7% (18.1%) of the variation, suggesting that the CMV was not a valid threat in this study. Thirdly, the DWH test results provided evidence of endogeneity may be a potential trouble for the regression models in present study, but these endogenically induced bias would be mitigated with random block design, thus not threatening our key conclusions of RIF shifting effect that we were focused. Last but not least, Post-test results with LRIF and HRIF placed at the bottom of the questionnaires showed that no statistically significant systemic differences in CDI purchasing decisions, perceived risk, and time orientation for subjects between treatment and control groups. Appendices D, E, F, and G for details.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study contributes to the existing literature by employing an online survey experiment to explore the relative contributions of RIFs to the regularity disparities of perceived risk, time orientation, and their interaction mechanism in purchasing decisions for emerging CDI in China. The primary contribution of the present article is to propose that the mechanism by which the HRIF could stimulate CDI purchasing decisions is that it weakens individuals\u0026apos; POA while strengthens their FOA with the mediating role of perceived risk. Speculating on the policy implications of RIF\u0026apos;s impact on CDI development, marketing strategies for CDI should try to make advertising appear comprehensive and detailed risk information narratives. For instance, emphasizing the high risk of incidence rate and financial burden associated with cancer meticulously in CDI advertising may reduce the perceived immediate costs and enhance perceived delayed benefits associated with CDI purchasing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmerging CDI is full of great significance for relieving Chinese cancer burden, one of the most populous countries with the largest population of cancer patients globally\u0026nbsp;(Feng et al., 2019). However, as far as the risk information narrative is concerned, our experimental results suggest that the advertising being executed (examples see Appendix A) is insufficient for CDI marketing expansion that based on online sales. The differentiated results of choosing CDI schemes under LRIF and HRIF show that the powerful capacity of episodic narrative with meticulous risk information in significantly promoting the possibility of subjects to insure long-term CDI schemes, and inhibiting the possibility of subjects to not insure or insure short-term CDI schemes. Much of the health behavior interventions that have focused on how to inform people about health risks (e.g., cancer, smoking, drinking, and unhealthy eating etc.) and then nudge them to take actions for avoiding these risks, with less or more achieved desirable experimental or empirical outcomes\u0026nbsp;(Block \u0026amp; Keller, 1995; Pechmann \u0026amp; Catlin, 2016; You et al., 2023). These possess important policy enlightenment and practical application value for raising the level of CDI financing and further achieving sustainable development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePartial scholars argued that framing risk information may be not helpful to shape individuals\u0026apos; time orientation\u0026nbsp;(Kees, 2010). However, we found that HRIF could simultaneously restrain POA and reinforce FOA. According to CLT, temporal distance will influence individuals to view distant future options abstractly and near future options concretely, with remote events having less psychological impact on people than immediate events due to their abstractness\u0026nbsp;(Trope \u0026amp; Liberman, 2000). Consequently, possible reasons may be that for CDI purchasing decisions of the subjects, poor risk information perhaps limit them to focus on immediate costs, meticulous risk information may let them pay more attention to delayed benefits. In addition, to our surprise, the post-test results with removed RIF effect look better than that in LRIF (lower uninsured rates and POA levels, higher FOA and perceived risk levels), probably on account of the crude risk information dulls individuals\u0026apos; sensitivity to risk. This is a reminder of the fact that the risk information being presented by sellers currently may run counter to their desire to motivate consumers to purchase CDI via advertising in online marketplaces.\u003c/p\u003e\n\u003cp\u003eOur findings demonstrate that perceived risk, introduced as a mediating variable, could partially identify the influencing mechanism of HRIF on CDI purchasing decisions. We did acknowledge that endogenous troubles would cause biased estimates for regression models themselves, but not for RIF shifting effect with random block design. Moreover, the RIF shift effect size of 43.7% makes it impossible for us to deny the utility of HRIF. Previous research indicates that proximal framing linked health risk significantly increases risk perception of those who normally do not consider future consequences of their behaviors(Kees, 2010), the aforementioned research evidences and theories demonstrate the strong relationship between time orientation and perceived risk. Therefore, the principle may be that when LHIF converted to HRIF, rich and full risk information narratives significantly restrain POA and strengthen FOA at the same time with the mediating role of perceived risk, finally making subjects more inclined to focus on purchasing decisions for insuring and insuring long-term CDI schemes. In sum, the advertising currently being implemented is poor to spur CDI\u0026apos;s online sales growth, HRIF is worth considering for intervening individuals\u0026apos; CDI purchasing decisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations mostly related to the online survey experiment used in our study must be acknowledged. First, inherent defects of self-report method for assessing CDI purchase decision. Second, there exist shortcomings in the measurements of perceived risk and time orientation due to limited availability of the online survey data. Third, important control variables, such as CDI purchasing experience, were excluded because of online data bias restrictions. Fourth, despite we sampled representatively in a country that has over 20% of the world\u0026apos;s population, it still remains an open question whether RIF shapes CDI decision-making to the same extent in Eastern and Western societies with diverse cultural contexts. However, these limitations provide directions for future research endeavors.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn recent years, attention to impact of information frames on population health behavior are becoming international concern. Yet, we are unaware of any published research that have explicitly explored the causal role of HRIF in shaping CDI purchasing decisions and its action mechanism. This study demonstrates that risk information narrative in current advertising is ineffective, and HRIF stimulates individuals\u0026apos; CDI purchasing decisions by weakening POA and strengthening FOA with the mediating role of perceived risk. In a world where risk information is influencing decision-making in every aspect of human society, many people remain trapped in unhealthy behavior patterns due to misled by wrong or false risk information related to health communication advertising. Our findings provide a reliable and practical basis for relevant authorities formulate RIF as health interventions for CDI purchasing decisions to reduce health damage and financial burden of cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (72374110), the State Scholarship Fund of China Scholarship Council (202308320379), and the Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province (KYCX23_1900).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZ.S. and D.Q. conceived the research design. Z.S. and Z.Z. conducted the statistical analysis and wrote the main manuscript text. X.C. and D.Q. supervised the manuscript writing. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe greatly appreciate all the authors for their endeavors.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAizawa, N., \u0026amp; Kim, Y. S. (2015). Advertising and Risk Selection in Health Insurance Markets. \u003cem\u003eFinance and Economics Discussion Series\u003c/em\u003e, 2015(101), 1-50. http://doi.org/10.17016/FEDS.2015.101\u003c/li\u003e\n \u003cli\u003eAllied Market Research.\u0026nbsp;(2021). \u003cem\u003eOncology/Cancer Drugs Market Growth Overview\u003c/em\u003e https://www.alliedmarketresearch.com/oncology-cancer-drugs-market\u003c/li\u003e\n \u003cli\u003eBaillon, A., Capuno, J., O\u0026apos;Donnell, O., Tan, C. 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Online information acquisition affects food risk prevention behaviours: the roles of topic concern, information credibility and risk perception. \u003cem\u003eB\u003c/em\u003e\u003cem\u003eMC\u003c/em\u003e\u003cem\u003e\u0026nbsp;Public Health\u003c/em\u003e, 23(1) http://doi.org/10.1186/s12889-023-16814-1\u003c/li\u003e\n \u003cli\u003eZomerdijk, N., Jongenelis, M., Short, C. E., Smith, A., Turner, J., \u0026amp; Huntley, K. (2021). Prevalence and correlates of psychological distress, unmet supportive care needs, and fear of cancer recurrence among haematological cancer patients during the COVID-19 pandemic. \u003cem\u003eSupportive Care in Cancer\u003c/em\u003e, 29(12), 7755-7764. http://doi.org/10.1007/s00520-021-06369-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"risk information frame, perceived risk, time orientation, cancer drugs insurance, China","lastPublishedDoi":"10.21203/rs.3.rs-4570011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4570011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs one of the most populous countries with the highest number of cancer patients worldwide, China is actively promoting emerging commercial cancer drugs insurance (CDI) to address the increasingly serious cancer burden. However, providers are uncertain whether the risk information they design in their promotional advertising is effective for expanding CDI that primarily sold online. In this paper, we present a randomized online survey experiment nationally, to understand the unique ability of low/high risk information frame (LRIF/HRIF) in shaping individuals' CDI purchasing decisions. The results reveal that the LRIF which being used by providers is ineffective, the effect of shifting LRIF to HRIF in advertising increasing 43.7% for stimulating purchasing decisions. A possible mechanism may depend on HRIF restraining present-oriented attitude and reinforcing future-oriented attitude with the mediating role of perceived risk. These results suggest that further employing HRIF to develop advertising toolkits effectively would critical for promoting CDI expansion.\u003c/p\u003e","manuscriptTitle":"Contributions of risk information frame to perceived risk, time orientation, and cancer drugs insurance purchasing decisions: based on a nationwide online survey experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-04 06:17:24","doi":"10.21203/rs.3.rs-4570011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80f4f64c-65d1-4d9b-8ead-5ba0b6269ee1","owner":[],"postedDate":"July 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33609665,"name":"Health sciences/Health care"},{"id":33609666,"name":"Health sciences/Health care/Health policy"}],"tags":[],"updatedAt":"2024-11-21T04:53:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-04 06:17:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4570011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4570011","identity":"rs-4570011","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}