National seroepidemiological study of COVID-19 after the initial rollout of vaccines: Before and at the peak of the Omicron-dominant period in Japan

BACKGROUND: Based on routine surveillance data, Japan has been affected much less by COVID-19 compared with other countries. To validate this, we aimed to estimate SARS-CoV-2 seroprevalence and examine sociodemographic factors associated with cumulative infection in Japan. METHODS: A population-based serial cross-sectional seroepidemiological investigation was conducted in five prefectures in December 2021 (pre-Omicron) and February-March 2022 (Omicron [BA.1/BA.2]-peak). Anti-nucleocapsid and anti-spike antibodies were measured to detect infection-induced and vaccine/infection-induced antibodies, respectively. Logistic regression was used to identify associations between various factors and past infection. RESULTS: Among 16 296 participants (median age: 53 [43-64] years), overall prevalence of infection-induced antibodies was 2.2% (95% CI: 1.9-2.5%) in December 2021 and 3.5% (95% CI: 3.1-3.9%) in February-March 2022. Factors associated with past infection included those residing in urban prefectures (Tokyo: aOR 3.37 [95% CI: 2.31-4.91], Osaka: aOR 3.23 [95% CI: 2.17-4.80]), older age groups (60s: aOR 0.47 [95% CI 0.29-0.74], 70s: aOR 0.41 [95% CI 0.24-0.70]), being vaccinated (twice: aOR 0.41 [95% CI: 0.28-0.61], three times: aOR 0.21 [95% CI: 0.12-0.36]), individuals engaged in occupations such as long-term care workers (aOR: 3.13 [95% CI: 1.47-6.66]), childcare workers (aOR: 3.63 [95% CI: 1.60-8.24]), food service workers (aOR: 3.09 [95% CI: 1.50-6.35]), and history of household contact (aOR: 26.4 [95% CI: 20.0-34.8]) or non-household contact (aOR: 5.21 [95% CI:3.80-7.14]) in February-March 2022. Almost all vaccinated individuals (15 670/15 681) acquired binding antibodies with higher titers among booster dose recipients. CONCLUSIONS: Before Omicron, the cumulative burden was >10 times lower in Japan (2.2%) compared with the US (33%), the UK (25%), or global estimates (45%), but most developed antibodies owing to vaccination

Corowa-kun: A messenger app chatbot delivers COVID-19 vaccine information, Japan 2021.

BACKGROUND: There is a long history in Japan of public concerns about vaccine adverse events. Few studies have assessed how mobile messenger apps affect COVID-19 vaccine hesitancy. METHODS: Corowa-kun, a free chatbot, was created on February 6, 2021 in LINE, the most popular messenger app in Japan. Corowa-kun provides instant, automated answers to 70 frequently asked COVID-19 vaccine questions. A cross-sectional survey with 21 questions was performed within Corowa-kun during April 5-12, 2021. RESULTS: A total of 59,676 persons used Corowa-kun during February-April 2021. Of them, 10,192 users (17%) participated in the survey. Median age was 55 years (range 16-97), and most were female (74%). COVID-19 vaccine hesitancy reported by survey respondents decreased from 41% to 20% after using Corowa-kun. Of the 20% who remained hesitant, 16% (1,675) were unsure, and 4% (364) did not intend to be vaccinated. Factors associated with vaccine hesitancy were: age 16-34 (odds ratio [OR] = 3.7; 95% confidential interval [CI]: 3.0-4.6, compared to age ≥ 65), female sex (OR = 2.4; Cl: 2.1-2.8), and history of a previous vaccine side-effect (OR = 2.5; Cl: 2.2-2.9). Being a physician (OR = 0.2; Cl: 0.1-0.4) and having received a flu vaccine the prior season (OR = 0.4; Cl: 0.3-0.4) were protective. CONCLUSIONS: A substantial number of people used the chabot in a short period. Mobile messenger apps could be leveraged to provide accurate vaccine information and to investigate vaccine intention and risk factors for vaccine hesitancy

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research

Abstract BackgroundThe persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. ObjectiveThis study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model’s reliance on patient history during the diagnostic process. MethodsWe used 25 clinical vignettes from the Journal of Generalist Medicine ResultsChatGPT’s diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2χ1Pχ1P ConclusionsChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings