Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.

BACKGROUND: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. OBJECTIVE: This study aims to visualize and measure patients' heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. METHODS: A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables' coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. RESULTS: A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis "accuracy" attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. CONCLUSIONS: Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People's preferences for the "accuracy" and "diagnostic expenses" attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study.

BACKGROUND: Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people's preferences for AI clinicians and traditional clinicians are worth exploring. OBJECTIVE: We aimed to quantify and compare people's preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people's preferences were affected by the pressure of pandemic. METHODS: We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people's preferences for different diagnosis methods. RESULTS: In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. CONCLUSIONS: Individuals' preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.

Association of Visceral Fat Area and Hyperuricemia in Non-Obese US Adults: A Cross-Sectional Study.

This study aimed to investigate the relationship between visceral fat area (VFA) and hyperuricemia (HUA) among non-obese adults. We extracted data from 6224 US adults aged 20−59 years from the National Health and Nutrition Examination Survey (NHANES) from 2011−2018. The VFA was divided into four quartiles (Q1−Q4). We used multivariable logistic regression models to control for known confounders. A generalized additive model (GAM) and restricted cubic spines were used to examine the association between VFA and HUA stratified by sex, and a two-piecewise linear regression model was used to calculate the threshold effect among males. The results revealed that the prevalence of HUA was 11.8% (men 15.8%, women 7.2%). In the fully adjusted model, there was a positive association between VFA and HUA [as a quartile variable, Q4 vs. Q1, odds ratio (OR): 3.77 and 95% confidence interval (CI): (2.47~5.75), p < 0.001, p for trend < 0.001; as a continuous variable, per 10 cm2 increment, OR (95%CI):1.10(1.07,1.14), p < 0.001]. Besides, this positive association remained significantly stratified by sex. Interestingly, we observed a nonlinear dose-response relationship between VFA and HUA in males (inflection point: 107.46 cm2). In conclusion, our study confirmed a significant positive relationship between VFA and HUA among non-obese adults and remained statistically significant when stratified by sex.

Cost-effectiveness analysis of autogenous arteriovenous fistula, arteriovenous graft, and tunneled-cuffed catheter for hemodialysis in patients with end-stage kidney disease in Southern China.

OBJECTIVES: To evaluate the cost-effectiveness of three permanent vascular accesses for maintenance hemodialysis patients from a hospital perspective throughout 5 years, which is the average life expectancy of patients with end-stage kidney disease. SUBJECTS AND METHODS: We conducted a EuroQol(EQ-5D) questionnaire survey between January 2021 and March 2021 with 250 patients to estimate the health utility of various states in patients under different hemodialysis vascular access. We designed a Markov model and conducted a cost-effectiveness analysis to compare the cost-effectiveness of three hemodialysis vascular access in Guangzhou throughout 5 years. RESULTS: The mean costs were US$44,481 with tunneled-cuffed catheter (TCC), and US$68,952 and US$59,247 with arteriovenous graft (AVG) and autogenous arteriovenous fistula (AVF), respectively. The mean quality-adjusted life-years (QALYs) was 1.41 with TCC, and 2.37 and 2.73 with AVG and AVF, respectively. AVG had an incremental cost-effectiveness ratio (ICER) of US$25,491 per QALY over TCC; AVF had an ICER of -US$26,958 per QALY over AVG. At a willingness to pay below US$10,633.8 per QALY, TCC is likely the most cost-effective vascular access. At any willingness to pay between US$10,633.8 and US$30,901.4 per QALY, AVF is likely the most cost-effective vascular access. CONCLUSION: These findings illustrate the value of AVF given its relative cost-effectiveness to other hemodialysis modalities. Although AVG costs much more than TCC for slightly higher QALYs than TCC, AVG still has a greater advantage over TCC for patients with longer life expectancy due to its lower probability of death.

Parent-Child Attachment and Social Adaptation Behavior in Chinese College Students: The Mediating Role of School Bonding.

Family and school are two main places for adolescents to develop socialization, which can be contributed by good parent-child attachment and school bonding. Earlier studies suggested that parent-child attachment played an important role in promoting the formation of high-level school bonding, which is also likely to influence social adaptation. This study aimed to explore the relationship between parent-child attachment and social adaptation, and the mediating role of school bonding. Using stratified cluster sampling, 1,440 college students were first randomly selected from four universities and then stratified by specialty with a balance between genders and grades. Participants voluntarily participated in this study and completed questionnaires including the Parent-Child Attachment Scale, School Bonding Scale, and Social Adaptation Scale. Finally, a total of 1,320 college students were included in the analysis (59.5% female; aged 18-24years, Mage=20.39±1.52years). Data analysis and structural equation modeling were conducted using SPSS 22.0 and AMOS 23.0. The results indicated that the overall level of parent-child attachment in females (M=75.72, SD=12.36) was significantly higher than that of males (M=73.71, SD=12.68; F=8.22, p<0.01). Difference was also found between sibling status (F=13.90, p<0.001), and the only-child (M=76.16, SD=12.72) scored significantly higher than their counterparts (non-only children, M=73.60, SD=12.19). Parent-child attachment was positively correlated with social adaptation (p<0.01) and school bonding (p<0.01), while school bonding was also positively correlated with social adaptation score (p<0.01). School bonding played a partial intermediate role in the relationship between parent-child attachment and social adaptation (ß=0.15). Our research identified a direct influence of parent-child attachment and an indirect influence via school bonding on social adaptation among college students.

Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries.

BACKGROUND: Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19-related health information during the pandemic. OBJECTIVE: The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage. METHODS: To achieve the study objectives, we analyzed the public's information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19-related "diseases," "treatments and medical resources," "symptoms and signs," and "public measures." The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage. RESULTS: Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19-related "diseases," in most countries, the RSV of the term "coronavirus" increased earlier than that of "covid-19"; however, around April 2020, the search volume of the term "covid-19" surpassed that of "coronavirus." Regarding the topic "treatments and medical resources," the most and least searched terms were "mask" and "ventilator," respectively. Regarding the topic "symptoms and signs," "fever" and "cough" were the most searched terms. The RSV for the term "lockdown" was significantly higher than that for "social distancing" under the topic "public health measures." In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic "treatments and medical resources" and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic "diseases" and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage. CONCLUSIONS: Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics.

A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States.

OBJECTIVES: To investigate the differences in vaccine hesitancy and preference of the currently available COVID-19 vaccines between two countries, namely, China and the United States (U.S.). METHOD: A cross-national survey was conducted in both China and the United States, and discrete choice experiments, as well as Likert scales, were utilized to assess vaccine preference and the underlying factors contributing to vaccination acceptance. Propensity score matching (PSM) was performed to enable a direct comparison between the two countries. RESULTS: A total of 9077 (5375 and 3702 from China and the United States, respectively) respondents completed the survey. After propensity score matching, over 82.0% of respondents from China positively accepted the COVID-19 vaccination, while 72.2% of respondents from the United States positively accepted it. Specifically, only 31.9% of Chinese respondents were recommended by a doctor to have COVID-19 vaccination, while more than half of the U.S. respondents were recommended by a doctor (50.2%), local health board (59.4%), or friends and families (64.8%). The discrete choice experiments revealed that respondents from the United States attached the greatest importance to the efficacy of COVID-19 vaccines (44.41%), followed by the cost of vaccination (29.57%), whereas those from China held a different viewpoint, that the cost of vaccination covered the largest proportion in their trade-off (30.66%), and efficacy ranked as the second most important attribute (26.34%). Additionally, respondents from China tended to be much more concerned about the adverse effect of vaccination (19.68% vs. 6.12%) and have a lower perceived severity of being infected with COVID-19. CONCLUSION: Although the overall acceptance and hesitancy of COVID-19 vaccination in both countries are high, underpinned distinctions between these countries were observed. Owing to the differences in COVID-19 incidence rates, cultural backgrounds, and the availability of specific COVID-19 vaccines in the two countries, vaccine rollout strategies should be nation-dependent.