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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.
Taoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola Akinwunmi; Babatunde Akinwunmi; Casper Zhang; Yibo Wu; Wai-Kit Ming. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines 2021, 9, 649 .
AMA StyleTaoran Liu, Zonglin He, Jian Huang, Ni Yan, Qian Chen, Fengqiu Huang, Yuejia Zhang, Omolola Akinwunmi, Babatunde Akinwunmi, Casper Zhang, Yibo Wu, Wai-Kit Ming. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines. 2021; 9 (6):649.
Chicago/Turabian StyleTaoran Liu; Zonglin He; Jian Huang; Ni Yan; Qian Chen; Fengqiu Huang; Yuejia Zhang; Omolola Akinwunmi; Babatunde Akinwunmi; Casper Zhang; Yibo Wu; Wai-Kit Ming. 2021. "A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States." Vaccines 9, no. 6: 649.
Background: The role of low-carbohydrate ketogenic diet (LCKD) as an adjuvant therapy in antitumor treatment is not well established. This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to investigate the efficacy of LCKD as an adjuvant therapy in antitumor treatment compared to non-ketogenic diet in terms of lipid profile, body weight, fasting glucose level, insulin, and adverse effects; Methods: In this study, databases such as PubMed, Web of Science, Scopus, CINAHL, and Cochrane trials were searched. Only RCTs that involved cancer participants that were assigned to dietary interventions including a LCKD group and a control group (any non-ketogenic dietary intervention) were selected. Three reviewers independently extracted the data, and the meta-analysis was performed using a fixed effects model or random effects model depending on the I2 value or p-value; Results: A total of six articles met the inclusion/exclusion criteria. In the overall analysis, the post-intervention results = standard mean difference, SMD (95% CI) showed total cholesterol (TC) level = 0.25 (−0.17, 0.67), HDL-cholesterol = −0.07 (−0.50, 0.35), LDL-cholesterol = 0.21 (−0.21, 0.63), triglyceride (TG) = 0.09 (−0.33, 0.51), body weight (BW) = −0.34 (−1.33, 0.65), fasting blood glucose (FBG) = −0.40 (−1.23, 0.42) and insulin = 0.11 (−1.33, 1.55). There were three outcomes showing significant results in those in LCKD group: the tumor marker PSA, p = 0.03, the achievement of ketosis p = 0.010, and the level of satisfaction, p = 0.005; Conclusions: There was inadequate evidence to support the beneficial effects of LCKDs on antitumor therapy. More trials comparing LCKD and non-KD with a larger sample size are necessary to give a more conclusive result.
Ya-Feng Yang; Preety Mattamel; Tanya Joseph; Jian Huang; Qian Chen; Babatunde Akinwunmi; Casper Zhang; Wai-Kit Ming. Efficacy of Low-Carbohydrate Ketogenic Diet as an Adjuvant Cancer Therapy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients 2021, 13, 1388 .
AMA StyleYa-Feng Yang, Preety Mattamel, Tanya Joseph, Jian Huang, Qian Chen, Babatunde Akinwunmi, Casper Zhang, Wai-Kit Ming. Efficacy of Low-Carbohydrate Ketogenic Diet as an Adjuvant Cancer Therapy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients. 2021; 13 (5):1388.
Chicago/Turabian StyleYa-Feng Yang; Preety Mattamel; Tanya Joseph; Jian Huang; Qian Chen; Babatunde Akinwunmi; Casper Zhang; Wai-Kit Ming. 2021. "Efficacy of Low-Carbohydrate Ketogenic Diet as an Adjuvant Cancer Therapy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials." Nutrients 13, no. 5: 1388.
BACKGROUND Previous researches didn’t explore the influence of smoking before pregnancy on the health-related quality of life (HRQoL)of Chinese pregnant women, which is a big population in the largest developing country in the world and cannot be neglected. OBJECTIVE To evaluate the HRQoL of pregnant women in China with different smoking status and further to estimate the association between pre-pregnancy smoking and HRQoL. METHODS A nationwide-based cross-sectional study was conducted to determine the association between different smoking status (smoking currently, quit smoking, never smoking) and HRQoL in pregnant women across China. A web-based questionnaire was administered during prenatal examinations. EuroQoL Group’s five-dimension (EQ-5D-5L) scale with EuroQoL Group’s visual analog scale (EQ-5D VAS) scale were used for measuring HRQoL. RESULTS A total of 16,811 participants were included in the study. Significant difference in EQ-5D VAS was detected between non-smokers and ex-smokers (P<.001). Among ex-smokers, the proportion of pregnant women who suffer from depression/anxiety is higher compared with non-smokers (P<.001). We found that the increased cigarette consumption before pregnancy could result in lower EQ-5D VAS (P=.04) and EQ-5D index (P=.005) of pregnant women in China. CONCLUSIONS Chinese pregnant women with smoking history tend to have lower HRQoL. Smoking cessation during pregnancy doesn’t not significantly improve the HRQoL of Chinese pregnant women compared to smokers. Compared to non-smokers, ex-smokers are more likely to suffer from depression/anxiety. Among ex-smokers, the more cigarettes the Chinese pregnant women smoked, the lower their HRQoL. We suggest that the Chinese government should strengthen the education of stopping smoking and avoiding second-hand smoke for women who have pregnancy plan and their family members.
Kadi Hu; Shiqian Zou; Casper J. P. Zhang; Huailiang Wu; Babatunde Akinwunmi; Zilian Wang; Wai-Kit Ming. Health-Related Quality of Life Among Pregnant Women with Pre-pregnancy Smoking in China: A National Cross-sectional Study (Preprint). 2021, 1 .
AMA StyleKadi Hu, Shiqian Zou, Casper J. P. Zhang, Huailiang Wu, Babatunde Akinwunmi, Zilian Wang, Wai-Kit Ming. Health-Related Quality of Life Among Pregnant Women with Pre-pregnancy Smoking in China: A National Cross-sectional Study (Preprint). . 2021; ():1.
Chicago/Turabian StyleKadi Hu; Shiqian Zou; Casper J. P. Zhang; Huailiang Wu; Babatunde Akinwunmi; Zilian Wang; Wai-Kit Ming. 2021. "Health-Related Quality of Life Among Pregnant Women with Pre-pregnancy Smoking in China: A National Cross-sectional Study (Preprint)." , no. : 1.
Qian Liu; Zequan Zheng; Jingsen Chen; Winghei Tsang; Jin Shan; Yimin Zhang; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. Health Communication for Hospice Care through Chinese Media: Digital Topic Modeling Approach (Preprint). JMIR Public Health and Surveillance 2021, 1 .
AMA StyleQian Liu, Zequan Zheng, Jingsen Chen, Winghei Tsang, Jin Shan, Yimin Zhang, Babatunde Akinwunmi, Casper Jp Zhang, Wai-Kit Ming. Health Communication for Hospice Care through Chinese Media: Digital Topic Modeling Approach (Preprint). JMIR Public Health and Surveillance. 2021; ():1.
Chicago/Turabian StyleQian Liu; Zequan Zheng; Jingsen Chen; Winghei Tsang; Jin Shan; Yimin Zhang; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. 2021. "Health Communication for Hospice Care through Chinese Media: Digital Topic Modeling Approach (Preprint)." JMIR Public Health and Surveillance , no. : 1.
BACKGROUND Hospice care, a type of end-of-life care provided for dying patients and their families, has been rooted in China since the 1980s. It can improve receivers’ quality of life as well as ease their economic burden. The Chinese mass media have continued to actively dispel misconceptions of hospice care and deliver the latest information to citizens. OBJECTIVE This study aimed to retrieve and analyze news reports on hospice care to gain insight into whether any differences exist in delivered heath information as time went by and the role the mass media played in health communication in recent years. METHODS We searched the Huike (WiseSearch) database for related news from Chinese mass media between 2014 and 2019. We set January 1, 2014 to December 31, 2016 as the first time period and January 1, 2017 to December 31, 2019 as the second time period. Python was used to complete the data cleaning process. We determined appropriate topic numbers for these two periods based on coherence score and applied the latent Dirichlet allocation topic modeling. Keywords of each topic and corresponding topics’ names were then generated. The topics were plotted into different circles and their distances on the two-dimensional plane was represented by multidimensional scaling. RESULTS After removing the duplicated and irrelevant news articles, we obtained a total of 2227 articles. We chose eight as the suitable topic number for both time periods and generated topics’ name and their keywords. The top three most reported topics in the first period were patient treatment, hospice care stories, and development of health care services and health insurance, accounting for 18.68% (n = 178), 16.58% (n = 158), and 14.17% (n = 135) of the collected reports, respectively. The top three most reported topics in the second period were hospice care stories, patient treatment, and development of health care services, accounting for 15.62% (n = 199), 15.38 (n = 15.38), and 14.27% (n = 182), respectively. CONCLUSIONS Topic modeling of news reports gives us a better understanding of patterns of health communication about hospice care by mass media. Chinese mass media frequently reported on hospice care in April due to a traditional Chinese festival. An increase in coverage in the second period was observed. These two periods share six similar topics, among which patient treatment outstrips hospice care stories as the most-reported topic in the second period, showing the humanistic spirit behind the reports. We suggest stakeholders cooperate with the mass media when planning to update policies.
Qian Liu; Zequan Zheng; Jingsen Chen; Winghei Tsang; Jin Shan; Yimin Zhang; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. Health Communication for Hospice Care through Chinese media: Digital Topic Modeling Approach (Preprint). 2021, 1 .
AMA StyleQian Liu, Zequan Zheng, Jingsen Chen, Winghei Tsang, Jin Shan, Yimin Zhang, Babatunde Akinwunmi, Casper Jp Zhang, Wai-Kit Ming. Health Communication for Hospice Care through Chinese media: Digital Topic Modeling Approach (Preprint). . 2021; ():1.
Chicago/Turabian StyleQian Liu; Zequan Zheng; Jingsen Chen; Winghei Tsang; Jin Shan; Yimin Zhang; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. 2021. "Health Communication for Hospice Care through Chinese media: Digital Topic Modeling Approach (Preprint)." , no. : 1.
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.
Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study. Journal of Medical Internet Research 2021, 23, e26997 .
AMA StyleTaoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study. Journal of Medical Internet Research. 2021; 23 (3):e26997.
Chicago/Turabian StyleTaoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2021. "Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study." Journal of Medical Internet Research 23, no. 3: e26997.
Objectives A novel long-acting regimen (LAR) of cabotegravir and rilpivirine for HIV treatment requires dosing every 2 months instead of daily. We assessed what proportion of people living with HIV and physicians would be interested in trying and offering LAR respectively and why. Methods 688 people living with HIV on treatment, and 120 HIV physicians completed web-based surveys in Germany, Italy, the UK and France during 2019. Balanced description of a hypothetical LAR regarding efficacy, administration and possible side effects were provided. The hypothetical long-acting injections were assumed to be cost-neutral to current daily oral antiretrovirals. Interest of people living with HIV in trying (‘very’/’highly’) and physicians’ willingness to offer (‘definitely’/’probably’) this LAR in different situations, with perceived benefits/concerns was measured. Results Of people living with HIV, 65.8% were interested in trying LAR. The majority (~80%–90%) of those with unmet needs felt LAR would help, including those with strong medical needs (malabsorption and interfering gastrointestinal conditions), suboptimal adherence, confidentiality/privacy concerns and emotional burden of daily dosing. Of physicians, percentage willing to offer LAR varied situationally: strong medical need (dysphagia, 93.3%; malabsorption, 91.6%; interfering gastrointestinal issues, 90.0%; central nervous system disorders, 87.5%); suboptimal adherence (84.2%); confidentiality/privacy concerns (hiding medications, 86.6%) and convenience/lifestyle (84.2%). People living with HIV liked LAR for not having to carry pills when travelling (56.3%); physicians liked the increased patient contact (54.2%). Furthermore, 50.0% of people living with HIV perceived LAR would minimise transmission risk and improve their sexual health. The most disliked attribute was scheduling appointments (37.2%) and resource constraints (57.5%) for people living with HIV and physicians, respectively. Physicians estimated 25.7% of their patients would actually switch. Conclusion Providers and people living with HIV viewed the described LAR as addressing several unmet needs. Alternative treatment routes and especially LAR may improve adherence and quality of life.
Babatunde Akinwunmi; Daniel Buchenberger; Jenny Scherzer; Martina Bode; Paolo Rizzini; Fabio Vecchio; Laetitia Roustand; Gaelle Nachbaur; Laurent Finkielsztejn; Vasiliki Chounta; Nicolas Van de Velde. Factors associated with interest in a long-acting HIV regimen: perspectives of people living with HIV and healthcare providers in four European countries. Sexually Transmitted Infections 2021, 1 .
AMA StyleBabatunde Akinwunmi, Daniel Buchenberger, Jenny Scherzer, Martina Bode, Paolo Rizzini, Fabio Vecchio, Laetitia Roustand, Gaelle Nachbaur, Laurent Finkielsztejn, Vasiliki Chounta, Nicolas Van de Velde. Factors associated with interest in a long-acting HIV regimen: perspectives of people living with HIV and healthcare providers in four European countries. Sexually Transmitted Infections. 2021; ():1.
Chicago/Turabian StyleBabatunde Akinwunmi; Daniel Buchenberger; Jenny Scherzer; Martina Bode; Paolo Rizzini; Fabio Vecchio; Laetitia Roustand; Gaelle Nachbaur; Laurent Finkielsztejn; Vasiliki Chounta; Nicolas Van de Velde. 2021. "Factors associated with interest in a long-acting HIV regimen: perspectives of people living with HIV and healthcare providers in four European countries." Sexually Transmitted Infections , no. : 1.
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.
Taoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment. Journal of Medical Internet Research 2021, 23, e22841 .
AMA StyleTaoran Liu, Winghei Tsang, Fengqiu Huang, Oi Ying Lau, Yanhui Chen, Jie Sheng, Yiwei Guo, Babatunde Akinwunmi, Casper Jp Zhang, Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment. Journal of Medical Internet Research. 2021; 23 (2):e22841.
Chicago/Turabian StyleTaoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. 2021. "Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment." Journal of Medical Internet Research 23, no. 2: e22841.
Background The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. Objective This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. Methods Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. Results The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. Conclusions The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.
Zonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis. JMIR Public Health and Surveillance 2021, 7, e20495 .
AMA StyleZonglin He, Yiqiao Chin, Shinning Yu, Jian Huang, Casper J P Zhang, Ke Zhu, Nima Azarakhsh, Jie Sheng, Yi He, Pallavi Jayavanth, Qian Liu, Babatunde O Akinwunmi, Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis. JMIR Public Health and Surveillance. 2021; 7 (1):e20495.
Chicago/Turabian StyleZonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. 2021. "The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis." JMIR Public Health and Surveillance 7, no. 1: e20495.
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.
Taoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint). 2021, 1 .
AMA StyleTaoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming. Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint). . 2021; ():1.
Chicago/Turabian StyleTaoran Liu; Winghei Tsang; Yifei Xie; Kang Tian; Fengqiu Huang; Yanhui Chen; Oiying Lau; Guanrui Feng; Jianhao Du; Bojia Chu; Tingyu Shi; Junjie Zhao; Yiming Cai; Xueyan Hu; Babatunde Akinwunmi; Jian Huang; Casper J P Zhang; Wai-Kit Ming. 2021. "Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study (Preprint)." , no. : 1.
BACKGROUND Due to the COVID-19 pandemic, health information related to COVID-19 has spread across the news media worldwide. Google is among the most used Internet search engines, and the Google Trends tool can reflect how the public seek COVID-related health information during this period. OBJECTIVE To understand health communication through Google Trends and news coverage and to explore their relationship with early prevention and control of COVID-19 in 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 for eight countries (United States, United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1, 2020 and April 29, 2020, and depicted the trend of news coverage on COVID-19 over time, as well as search trends on the topics of COVID-19 related diseases, treatments and medical resources, symptoms and signs, and public measures. The characteristics of various trends in different countries were described and analyzed. RESULTS Across all search trends in eight countries, search peaks were formed almost between March and April 2020, and declines occurred in April 2020. Regarding COVID-19 related diseases, the searched peak for all terms occurred near or after March 11, 2020 across all countries, except Singapore. For treatments and medical resources, the term “mask” formed multiple search peaks while “ventilator” fluctuated modestly. In the topic of symptoms and signs, “fever” and “cough” were the most searched terms. The topic of public measures was the least searched. Besides, when combing the search trends with news coverage, there were mainly three patterns: the American pattern, the Singapore pattern, and the other-countries pattern. The Singapore pattern mainly saw two search peaks, while the trends of news coverage and search queries in the American pattern were in opposite directions. CONCLUSIONS Our findings reveal public concern about facemasks, disease control, and public health measures. As a source of information, news media can influence the search behaviors of the public. According to public concerns, news media can be used to spread more valuable information, and thus achieve more effective health communication. Also, because public concerns varied in different countries and periods, news media can deliver more effective health communication based on the change of public interest. Governments and health care systems can recognize Google Trends as public needs in the early stages of a COVID-19 crisis, and can translate public needs into practices to better control the spread of COVID-19. Therefore, news coverage trends and Google search trends also contribute to the prevention and control of epidemics in the early epidemic stage.
Qian Liu; Wai-Kit Ming; Fengqiu Huang; Qiuyi Chen; AoAo Jiao; Taoran Liu; Huailiang Wu; Babatunde Akinwunmi; Jia Li; Guan Liu; Casper Jp Zhang; Jian Huang. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint). 2020, 1 .
AMA StyleQian Liu, Wai-Kit Ming, Fengqiu Huang, Qiuyi Chen, AoAo Jiao, Taoran Liu, Huailiang Wu, Babatunde Akinwunmi, Jia Li, Guan Liu, Casper Jp Zhang, Jian Huang. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint). . 2020; ():1.
Chicago/Turabian StyleQian Liu; Wai-Kit Ming; Fengqiu Huang; Qiuyi Chen; AoAo Jiao; Taoran Liu; Huailiang Wu; Babatunde Akinwunmi; Jia Li; Guan Liu; Casper Jp Zhang; Jian Huang. 2020. "Understanding Health Communication Through Google Trends and News Coverage for COVID-19: A Multinational Study in Eight Countries (Preprint)." , no. : 1.
A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.
Zonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China. Journal of Medical Internet Research 2020, 22, e21685 .
AMA StyleZonglin He, Casper J P Zhang, Jian Huang, Jingyan Zhai, Shuang Zhou, Joyce Wai-Ting Chiu, Jie Sheng, Winghei Tsang, Babatunde O Akinwunmi, Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China. Journal of Medical Internet Research. 2020; 22 (9):e21685.
Chicago/Turabian StyleZonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. 2020. "A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China." Journal of Medical Internet Research 22, no. 9: e21685.
Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.
Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research 2020, 22, e21573 .
AMA StyleJiayi Shen, Jiebin Chen, Zequan Zheng, Jiabin Zheng, Zherui Liu, Jian Song, Sum Yi Wong, Xiaoling Wang, Mengqi Huang, Po-Han Fang, Bangsheng Jiang, Winghei Tsang, Zonglin He, Taoran Liu, Babatunde Akinwunmi, Chi Chiu Wang, Casper J P Zhang, Jian Huang, Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research. 2020; 22 (9):e21573.
Chicago/Turabian StyleJiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. 2020. "An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study." Journal of Medical Internet Research 22, no. 9: e21573.
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.
Taoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint). 2020, 1 .
AMA StyleTaoran Liu, Winghei Tsang, Fengqiu Huang, Oi Ying Lau, Yanhui Chen, Jie Sheng, Yiwei Guo, Babatunde Akinwunmi, Casper Jp Zhang, Wai-Kit Ming. Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint). . 2020; ():1.
Chicago/Turabian StyleTaoran Liu; Winghei Tsang; Fengqiu Huang; Oi Ying Lau; Yanhui Chen; Jie Sheng; Yiwei Guo; Babatunde Akinwunmi; Casper Jp Zhang; Wai-Kit Ming. 2020. "Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint)." , no. : 1.
People across the world have been greatly affected by the ongoing coronavirus disease (COVID-19) pandemic. The high infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospitals is particularly problematic for recently delivered mothers and currently pregnant women who require professional antenatal care. Online antenatal care would be a preferable alternative for these women since it can provide pregnancy-related information and remote clinic consultations. In addition, online antenatal care may help to provide relatively economical medical services and diminish health care inequality due to its convenience and cost-effectiveness, especially in developing countries or regions. However, some pregnant women will doubt the reliability of such online information. Therefore, it is important to ensure the quality and safety of online services and establish a stable, mutual trust between the pregnant women, the obstetric care providers and the technology vis-a-vis the online programs. Here, we report how the COVID-19 pandemic brings not only opportunities for the development and popularization of online antenatal care programs but also challenges.
Huailiang Wu; Weiwei Sun; Xinyu Huang; Shinning Yu; Hao Wang; Xiaoyu Bi; Jie Sheng; Sihan Chen; Babatunde Akinwunmi; Casper J P Zhang; Wai-Kit Ming. Online Antenatal Care During the COVID-19 Pandemic: Opportunities and Challenges. Journal of Medical Internet Research 2020, 22, e19916 .
AMA StyleHuailiang Wu, Weiwei Sun, Xinyu Huang, Shinning Yu, Hao Wang, Xiaoyu Bi, Jie Sheng, Sihan Chen, Babatunde Akinwunmi, Casper J P Zhang, Wai-Kit Ming. Online Antenatal Care During the COVID-19 Pandemic: Opportunities and Challenges. Journal of Medical Internet Research. 2020; 22 (7):e19916.
Chicago/Turabian StyleHuailiang Wu; Weiwei Sun; Xinyu Huang; Shinning Yu; Hao Wang; Xiaoyu Bi; Jie Sheng; Sihan Chen; Babatunde Akinwunmi; Casper J P Zhang; Wai-Kit Ming. 2020. "Online Antenatal Care During the COVID-19 Pandemic: Opportunities and Challenges." Journal of Medical Internet Research 22, no. 7: e19916.
BACKGROUND Most educators affected by the COVID-19 epidemic have had to find the most appropriate teaching approach to deal with this emergency teaching situation, i.e., an approach that can make good use of various teaching resources to achieve high adaptability. According to the characteristics of medical students and the course content of clinical clerkship, we adopted the problem-based learning (PBL) method to redesign the pedagogy of clerkship. At the end of the semester, the feasibility of the PBL teaching model in emergency remote teaching (ERT), which has an impact on students' learning experience and preference, was evaluated. Compared with other countries affected by the COVID-19 epidemic, the outbreak time and recovery time in China are both earlier; thus, the evaluation and feedback of ERT can provide the referential evidence to global educators and institutions. OBJECTIVE The objective of this study was to explore medical students' learning experience in ERT to understand how these positive and negative perceptions influence their learning preferences in different teaching modes and how the PBL model combined digital technology to influence teaching quality. METHODS Among 123 medical students from Jinan University, China, who participated in the questionnaire at the end of the ERT in the clinical clerkship course, 25 volunteered to have a further in-depth interview. We randomly select five veterinary students to participate in the one-on-one in-depth online interview, which was conducted within 30 minutes. After coding of the transcription by the NVivo 12.0 software, the collected qualitative data would undergo a thematic analysis. RESULTS The thematic analysis indicated two main themes. One is that the adoption of PBL is the crucial for overseas medical students to evaluate ERT positively, and is depicted by one sub-theme: positive comment contributions. The other theme is that clinical practice as the core of medical education has a decisive influence on the teaching mode preference of medical students, as depicted by two sub-themes: negative comment contributions; and preference in different teaching methods. CONCLUSIONS Although medical students preferred an offline teaching mode due to practical requirements, they generally gave positive comments on this ERT because of the PBL method pedagogy. This indicated the feasibility of the online PBL teaching method in medical education. Moreover, medical students' preferences in the combination of the online and offline teaching mode revealed a revolutionary new direction of revolution in medical education.
Meiling Chen; Siyi Wang; Babatunde Akinwunmi; Wai-Kit Ming. Evaluation of problem-based learning innovation in medical education during the COVID-19 pandemic: a qualitative study among medical students (Preprint). 2020, 1 .
AMA StyleMeiling Chen, Siyi Wang, Babatunde Akinwunmi, Wai-Kit Ming. Evaluation of problem-based learning innovation in medical education during the COVID-19 pandemic: a qualitative study among medical students (Preprint). . 2020; ():1.
Chicago/Turabian StyleMeiling Chen; Siyi Wang; Babatunde Akinwunmi; Wai-Kit Ming. 2020. "Evaluation of problem-based learning innovation in medical education during the COVID-19 pandemic: a qualitative study among medical students (Preprint)." , no. : 1.
UNSTRUCTURED A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.
Zonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China (Preprint). 2020, 1 .
AMA StyleZonglin He, Casper J P Zhang, Jian Huang, Jingyan Zhai, Shuang Zhou, Joyce Wai-Ting Chiu, Jie Sheng, Winghei Tsang, Babatunde O Akinwunmi, Wai-Kit Ming. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China (Preprint). . 2020; ():1.
Chicago/Turabian StyleZonglin He; Casper J P Zhang; Jian Huang; Jingyan Zhai; Shuang Zhou; Joyce Wai-Ting Chiu; Jie Sheng; Winghei Tsang; Babatunde O Akinwunmi; Wai-Kit Ming. 2020. "A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China (Preprint)." , no. : 1.
BACKGROUND Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.
Jiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint). 2020, 1 .
AMA StyleJiayi Shen, Jiebin Chen, Zequan Zheng, Jiabin Zheng, Zherui Liu, Jian Song, Sum Yi Wong, Xiaoling Wang, Mengqi Huang, Po-Han Fang, Bangsheng Jiang, Winghei Tsang, Zonglin He, Taoran Liu, Babatunde Akinwunmi, Chi Chiu Wang, Casper J P Zhang, Jian Huang, Wai-Kit Ming. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint). . 2020; ():1.
Chicago/Turabian StyleJiayi Shen; Jiebin Chen; Zequan Zheng; Jiabin Zheng; Zherui Liu; Jian Song; Sum Yi Wong; Xiaoling Wang; Mengqi Huang; Po-Han Fang; Bangsheng Jiang; Winghei Tsang; Zonglin He; Taoran Liu; Babatunde Akinwunmi; Chi Chiu Wang; Casper J P Zhang; Jian Huang; Wai-Kit Ming. 2020. "An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint)." , no. : 1.
BACKGROUND The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. OBJECTIVE This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. METHODS Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. RESULTS The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. CONCLUSIONS The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.
Zonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint). 2020, 1 .
AMA StyleZonglin He, Yiqiao Chin, Shinning Yu, Jian Huang, Casper J P Zhang, Ke Zhu, Nima Azarakhsh, Jie Sheng, Yi He, Pallavi Jayavanth, Qian Liu, Babatunde O Akinwunmi, Wai-Kit Ming. The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint). . 2020; ():1.
Chicago/Turabian StyleZonglin He; Yiqiao Chin; Shinning Yu; Jian Huang; Casper J P Zhang; Ke Zhu; Nima Azarakhsh; Jie Sheng; Yi He; Pallavi Jayavanth; Qian Liu; Babatunde O Akinwunmi; Wai-Kit Ming. 2020. "The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint)." , no. : 1.
BACKGROUND By middle April 2020, the novel coronavirus disease (COVID-19) pandemic has spread globally and caused more than 2 million confirmed cases and 140 thousand deaths. During this ongoing outbreak, the psychological demands of pregnant women needed to be acknowledged because they are likely to present symptoms of depression, stress or other mental discomfort during pregnancy. OBJECTIVE By middle April 2020, the novel coronavirus disease (COVID-19) pandemic has spread globally and caused more than 2 million confirmed cases and 140 thousand deaths. During this ongoing outbreak, the psychological demands of pregnant women needed to be acknowledged because they are likely to present symptoms of depression, stress or other mental discomfort during pregnancy. METHODS A population-based cross-sectional survey was carried out to collect the data of sociodemographic and other psychological assessments and responses through a national online platform. A total of 1901 pregnant women were included in this study. Each participant finished standardized rating scales on stress, depression, and responses to COVID-19. Independent t-test and chi-squared tests were used to compare outcomes between epicenter and non-epicenters in China. RESULTS Pregnant women in the epicenter appeared to have a significantly higher prevalence rate of suspected PSTD than the non-epicentral region. A slightly higher proportion of pregnant women in epicenter worried about infectious risks and outcomes related to their fetus. However, no significant difference was found between the probable PPD levels of the two regions. Pregnant women in both epicenter and non-epicentral regions have adapted their behaviors to mitigate the infection risks. CONCLUSIONS Epidemic situations could result in higher risks of psychological problems during pregnancy. Even outside of the epicenter, the depressive symptoms of pregnant women were more severe than during regular times. Several implications in antenatal care are also yielded for clinical application especially for countries in the early COVID-19 outbreak due to pandemic.
Huailiang Wu; Nga-Kwo Chan; Casper C.J. Zhang; Jian Huang; Huiyun Wang; Zongzhi Yin; Babatunde Akinwunmi; Wai-Kit Ming. Psychological and behavioral responses of pregnant women during the early COVID-19 outbreak: Epicenter and non-epicenter regions (Preprint). 2020, 1 .
AMA StyleHuailiang Wu, Nga-Kwo Chan, Casper C.J. Zhang, Jian Huang, Huiyun Wang, Zongzhi Yin, Babatunde Akinwunmi, Wai-Kit Ming. Psychological and behavioral responses of pregnant women during the early COVID-19 outbreak: Epicenter and non-epicenter regions (Preprint). . 2020; ():1.
Chicago/Turabian StyleHuailiang Wu; Nga-Kwo Chan; Casper C.J. Zhang; Jian Huang; Huiyun Wang; Zongzhi Yin; Babatunde Akinwunmi; Wai-Kit Ming. 2020. "Psychological and behavioral responses of pregnant women during the early COVID-19 outbreak: Epicenter and non-epicenter regions (Preprint)." , no. : 1.