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.

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.