School-Based Stroke Education Through On-Demand E-learning During Coronavirus Disease 2019 Pandemic: Itoigawa Stroke Awareness Campaign.

Introduction Raising stroke awareness is important to shorten the interval from onset to consultation. We performed a school-based stroke education by on-demand e-learning during the coronavirus disease 2019 pandemic. Methods We performed on-demand e-learning and distributed the online- and paper-based manga about stroke for students and parental guardians in August 2021. We carried out this in a manner similar to the prior effective online stroke awareness initiatives in Japan. An online post-educational survey in October 2021 was conducted to evaluate the awareness effects by asking participants about their knowledge. We also investigated the modified Rankin Scale (mRS) at the discharge of stroke patients who were treated in our hospital during the before- and after-campaign periods, respectively. Results We distributed the paper-based manga and asked to work on this campaign to all 2,429 students (1,545 elementary school and 884 junior high school students) who lived in Itoigawa. We acquired 261 (10.7%) online responses from the students and 211 (8.7%) responses from their parental guardians. The number of students who chose all correct answers in the survey significantly increased after the campaign (205/261, 78.5%) compared to that before the campaign (135/261, 51.7%) and those of parental guardians showed similar trends (before campaign 93/211, 44.1%; after campaign 198/211, 93.8%). We investigated 282 stroke patients (90 patients before and 192 patients after-campaign period), and their mRS at discharge after-campaign seemed to be improved. Conclusion Only 10.7% of students and 8.7% of the parental guardians worked on the online survey. However, the number of those who chose correct answers about stroke increased after the campaign. After this campaign, the mRS of stroke patients at discharge improved although it was unclear if this is a direct result of this activity.

Headache education by leaflet distribution during COVID-19 vaccination and school-based on-demand e-learning: Itoigawa Geopark Headache Awareness Campaign.

OBJECTIVE: We prospectively performed the Itoigawa Headache Awareness Campaign from August 2021 to June 2022, with two main interventions, and evaluated its effectiveness. BACKGROUND: Headache is a common public health problem, but its burden could be reduced by raising awareness about headache and the appropriate use of acute and prophylactic medication. However, few studies on raising headache awareness in the general public have been reported. METHODS: The target group was the general public aged 15-64. We performed two main interventions synergistically supported by other small interventions. Intervention 1 included leaflet distribution and a paper-based questionnaire about headache during COVID-19 vaccination, and intervention 2 included on-demand e-learning and online survey through schools. In these interventions, we emphasize the six important topics for the general public that were described in the Clinical Practice Guideline for Headache Disorders 2021. Each response among the two interventions' cohorts was collected on pre and post occasions. The awareness of the six topics before and after the campaign was evaluated. RESULTS: We obtained 4016 valid responses from 6382 individuals who underwent vaccination in intervention 1 and 2577 from 594 students and 1983 parents in intervention 2; thus, 6593 of 20,458 (32.2%) of the overall working-age population in Itoigawa city experienced these interventions. The percentage of individuals' aware of the six topics significantly increased after the two main interventions ranging from 6.6% (39/594)-40.0% (1606/4016) to 64.1% (381/594)-92.6% (1836/1983) (p < 0.001, all). CONCLUSIONS: We conducted this campaign through two main interventions with an improved percentage of individuals who know about headache. The two methods of community-based interventions could raise headache awareness effectively. Furthermore, we can achieve outstanding results by doing something to raise disease awareness during mass vaccination, when almost all residents gather in a certain place, and school-based e-learning without face-to-face instruction due to the COVID-19 pandemic.

School-based online survey on chronic headache, migraine, and medication-overuse headache prevalence among children and adolescents in Japanese one city - Itoigawa Benizuwaigani study.

BACKGROUND: We investigated the prevalence of headache, migraine, and medication-overuse headache (MOH) among children and adolescents through a school-based online questionnaire. We also investigated the triggers for migraine among them and the effect of the COVID-19 pandemic on headache frequency. METHODS: Children and adolescents aged 6-17 y.o. completed an online questionnaire. Migraine, MOH was defined as The International Classification of Headache Disorders Third edition. Factor and clustering analyses were performed for migraine triggers. The effect of the coronavirus disease 2019 (COVID-19) pandemic on headache frequency was also asked. RESULTS: Of the 2489 respondents, the prevalence of headache, migraine, and MOH were 36.44%, 9.48%, and 0.44%, respectively. Up to 70% of the respondents with headaches complained of the disturbance to daily life, but about 30% consulted doctors. The migraine triggers were grouped into 5 factors by factor analysis. The sensitivities of the migraineurs against the factors were divided into 3 clusters. Cluster 1 had stronger sensitivity for several triggers. Cluster 2 was sensitive to weather, smartphones, and video games. Cluster 3 had less sensitivity for triggers. Cluster 2 less consulted doctors even though the burden of migraine was enormous. During the COVID-19 pandemic, 10.25% of respondents increased headache attacks, while 3.97% decreased. CONCLUSIONS: This is the first detailed study on headache prevalence in Japanese students from elementary school to high school in one region. The burden of headaches is large among children and adolescents, and the unmet needs of its clinical practice should be corrected.

The First Case Series From Japan of Primary Headache Patients Treated by Completely Online Telemedicine.

Background Since March 2020, the coronavirus disease 2019 pandemic has increased the need for telemedicine to avoid in-person consultations. Online clinics for most diseases officially started in Japan in April 2022. Here, we report the cases of eight Japanese headache patients treated by completely online telemedicine for three months from the first visit. Methodology From the medical records between July 2022 and October 2022, we retrospectively investigated eight consecutive first-visit primary headache patients who consulted our online headache clinic via telemedicine and continued to see us via telemedicine only. The Headache Impact Test-6 (HIT-6) score, monthly headache days (MHD), and monthly acute medication intake days (AMD) were investigated over the observation period. Results A total of eight women were included, and the median (interquartile range) age was 30 (24-51) years. The median HIT-6 scores before, one, and three months after treatment were 63 (58-64), 54 (53-62), and 52 (49-54), respectively. MHD before, one, and three months after treatment were 15 (9-28), 12 (3-17), and 2 (2-8), respectively. AMD before, one, and three months after treatment were 10 (3-13), 3 (1-8), and 2 (0-3), respectively. Significant reductions in HIT-6 and MDH were observed three months after the initial consultation (p = 0.007 and p = 0.042, respectively). AMD was not significantly decreased at three months (p = 0.447). Conclusions This is the first report of Japanese patients treated by completely online telemedicine for three months from the first visit. HIT-6 and MDH can be significantly decreased at three months by only telemedicine. Online telemedicine is expected to be widely used to resolve unmet needs in headache treatment.

Development of an artificial intelligence-based diagnostic model for Alzheimer's disease.

Introduction: The diagnosis of Alzheimer's disease (AD) is sometimes difficult for nonspecialists, resulting in misdiagnosis. A missed diagnosis can lead to improper management and poor outcomes. Moreover, nonspecialists lack a simple diagnostic model with high accuracy for AD diagnosis. Methods: Randomly assigned data, including training data, of 6000 patients and test data of 1932 from 7932 patients who visited our memory clinic between 2009 and 2021 were introduced into the artificial intelligence (AI)-based AD diagnostic model, which we had developed. Results: The AI-based AD diagnostic model used age, sex, Hasegawa's Dementia Scale-Revised, the Mini-Mental State Examination, the educational level, and the voxel-based specific regional analysis system for Alzheimer's disease (VSRAD) score. It had a sensitivity, specificity, and c-static value of 0.954, 0.453, and 0.819, respectively. The other AI-based model that did not use the VSRAD had a sensitivity, specificity, and c-static value of 0.940, 0.504, and 0.817, respectively. Discussion: We created an AD diagnostic model with high sensitivity for AD diagnosis using only data acquired in daily clinical practice. By using these AI-based models, nonspecialists could reduce missed diagnoses and contribute to the appropriate use of medical resources.

Transient Global Amnesia Related to the Third Coronavirus Disease-19 (COVID-19) Vaccination.

We present a case report of transient global amnesia (TGA) after vaccinating the third dose of coronavirus disease-19 (COVID-19). A 65-year-old Japanese woman presented with TGA after her third vaccination, which spontaneously resolved. This article aims to facilitate the clinicians' understanding that TGA could occur after the COVID-19 vaccination in the COVID-19 global outbreak.

Questionnaire-Based Survey during COVID-19 Vaccination on the Prevalence of Elderly's Migraine, Chronic Daily Headache, and Medication-Overuse Headache in One Japanese City-Itoigawa Hisui Study.

BACKGROUND: The prevalence of headache disorders, migraine, chronic daily headache (CDH), and medication-overuse headache (MOH) among the elderly in Japan has not been sufficiently investigated. We performed a questionnaire-based survey and revealed 3-month headache prevalence and headaches' characteristics. METHODS: The population aged over 64 was investigated in Itoigawa during their third coronavirus disease 2019 vaccination. Migraine, MOH was defined as The International Classification of Headache Disorders Third edition. CDH was defined as a headache occurring at least 15 days per month. K-means++ were used to perform clustering. RESULTS: Among 2858 valid responses, headache disorders, migraine, CDH, and MOH prevalence was 11.97%, 0.91%, 1.57%, and 0.70%, respectively. Combined-analgesic and non-opioid analgesic were widely used. Only one migraineur used prophylactic medication. We performed k-means++ to group the 332 MOH patients into four clusters. Cluster 1 seemed to have tension-type headache-like headache characteristics, cluster 2 seemed to have MOH-like headache characteristics, cluster 3 seemed to have severe headaches with comorbidities such as dyslipidemia, stroke, and depression, and cluster 4 seemed to have migraine-like headache characteristics with photophobia and phonophobia. CONCLUSIONS: This is the largest prevalence survey in the Japanese elderly. Headache disorders are still the elderly's burden. Clustering suggested that severe headaches associated with some comorbidities may be unique to the elderly.

Questionnaire-based survey on the prevalence of medication-overuse headache in Japanese one city-Itoigawa study.

OBJECTIVE: The medication-overuse headache (MOH) prevalence has not been investigated in a general Japanese population. We performed questionnaire-based survey and revealed MOH prevalence and its characteristics. We also performed clustering to obtain insight for MOH subgrouping. METHODS: In this cross-sectional study, the 15-64-year-old population was investigated in Itoigawa during their COVID-19 vaccination under the national policy. MOH was defined as ≥ 15 days/month plus self-report of use of pain medications ≥ 10 or 15 days/month in the last 3 months. Ward method and k-means + + were used to perform clustering MOH patients. RESULTS: Among 5865 valid responses, MOH prevalence was 2.32%. MOH was common among females and the middle-aged. Combination-analgesic is the most overused as 50%. MOH had aggravation by routine physical activity, moderate or severe pain, and migraine-like, compared to non-MOH. The 136 MOH patients could be grouped into 3 clusters. Age and frequency of acute medication use were essential factors for clustering. CONCLUSIONS: This is the first study of MOH prevalence in Japan. Most MOH characteristics were similar to previous reports worldwide. Public awareness of proper headache treatment knowledge is still needed. Clustering results may be important for subtype grouping from a social perspective apart from existing clinical subtypes.

Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, "I Think I Have Influenza," Contributes to the Positivity Rate.

Introduction Rapid influenza diagnostic tests (RIDTs) are considered essential for determining when to start influenza treatment using anti-influenza drugs, but their accuracy is about 70%. Under the COVID-19 pandemic, we hope to refrain from performing unnecessary RIDTs considering droplet infection of COVID-19 and influenza. We re-examined the medical questionnaire's importance and its relationship to the positivity of RIDTs. Then we built a positivity prediction model for RIDTs using automated artificial intelligence (AI). Methods We retrospectively investigated 96 patients who underwent RIDTs at the outpatient department from December 2019 to March 2020. We used a questionnaire sheet with 24 items before conducting RIDTs. The factors associated with the positivity of RIDTs were statistically analyzed. We then used an automated AI framework to produce the positivity prediction model using the 24 items, sex, and age, with five-fold cross-validation. Results Of the 47 women and 49 men (median age was 39 years), 56 patients were RIDT positive with influenza A. The AI-based model using 26 variables had an area under the curve (AUC) of 0.980. The stronger variables are subjective pretest probability, which is a numerically described score ranging from 0% to 100% of "I think I have influenza," cough, past hours after the onset, muscle pain, and maximum body temperature in order. Conclusion We easily built the RIDT positivity prediction model using automated AI. Its AUC was satisfactory, and it suggested the importance of a detailed medical interview. Both the univariate analysis and AI-based model suggested that subjective pretest probability, "I think I have influenza," might be useful.