In the current study, we sought to identify special needs and safe evacuation conditions for children with neurodevelopmental disorders (CNDs) along Japan’s tsunami-prone Pacific coast. A survey and spatial analysis were used to collect data of CNDs (n = 47) and their caregivers. Areas predicted to be flooded in a tsunami, as well as evacuation routes to emergency shelters for vulnerable people (ESVPs), were mapped using geographic information systems (GIS). Our results showed that five professional staff were needed to support 33 CNDs requiring 135.9 m² of ESVP space. Critical safety factors were altitude, vertical evacuation, accessibility to ESVPs, and nonexistence of estuaries in the direction of evacuation. GIS-based spatial analysis and evacuation modeling for disaster preparedness and training plans that involve nurses are essential.

The Nankai Trough in Japan has been identified as a geological feature that could cause extensive damage in the event of a major earthquake. In this study, we investigated the impact of effective guidance signs for hearing-impaired individuals requiring special care when escaping to a tsunami evacuation building (emergency evacuation location) using geographical information system (GIS) and viewshed analysis. We selected an area we considered would suffer severe damage following a major earthquake and tsunami; we identified difficulties in the provision of escape routes. Using GIS, we determined the time required for escape and tsunami arrival time if effective signs were installed; we undertook such analysis using the height data of buildings in the target area. With effectively installed guidance signs, the average required evacuation time was 36.88 minutes; without such signs (which is currently the case in the target area), the average time was 47.10 minutes: that would result in citizens getting caught by the tsunami. Installing effective guidance signs would allow hearing-impaired individuals to escape to an evacuation building before being hit by the tsunami.

The number of rubella cases has increased in Japan, especially among adults. Rubella infection in pregnant females can lead to congenital rubella syndrome (CRS). The Japanese government is promoting vaccination to prevent CRS. This study performs a cost-effectiveness analysis of the following four methods: (1) females who wished to become pregnant and partners, with an antibody-titer test; (2) females only, with an antibody-titer test; (3) females and males, without an antibody-titer test; (4) females only, without an antibody-titer test. A decision tree model with a hypothetical cohort of 500 males and 500 females was used for the analysis, and the parameters were obtained from previous studies. The number of avoidances of CRS was defined as the effect. Compared to the case where methods were not implemented, the number of CRS cases that can be prevented by implementing the methods was 0.0115589 by (1) and (3) and 0.0147891 by (2) and (4). The cost effectiveness of (1) to (4) was 287,413,677 JPY, 135,050,529 JPY, 388,524,974 JPY, and 197,744,219 JPY, respectively (1 JPY = 0.00963247 USD). Method (2) was the most cost-effective and did not change by sensitivity analysis. We conclude that the vaccination for females only with an antibody-titer test is recommended.

Background: Outdoor secondhand smoke (SHS) remains a public health concern, particularly around designated outdoor smoking areas where nonsmokers may pass through or remain nearby. Although prior studies have quantified outdoor SHS concentrations, fewer have examined how many people may be present within a plausible exposure setting. Estimating the exposure-opportunity level requires methods that are feasible, scalable, and minimally intrusive. Objective: This study aimed to evaluate the feasibility of using passive Wi-Fi packet sensing, calibrated with brief on-site observation, to estimate the number of smokers and passersby within a plausible SHS exposure range at a public outdoor smoking area in Japan. Methods: We conducted a formative field study at a designated outdoor smoking area at the Asia Pacific Trade Center in Osaka, Japan. A passive Wi-Fi packet sensor collected timestamps, anonymized device identifiers, organizationally unique identifiers, and received signal strength indicator (RSSI) values from October 13 to 29, 2023. The main analysis focused on October 28, 2023, a high-footfall event day selected for direct calibration. Episodes were classified using empirically derived RSSI thresholds, and class-specific calibration ratios were applied to estimate day-level counts. Results: Of 128,313 anonymized detections recorded on October 28, 90.3% (115,950/128,313) occurred during business hours. Among these, 8.6% (n=11,068) identifiers were detected more than once. Dwell time could be calculated for 1.4% (n=1817) of the identifiers, and 0.5% (n=659) eligible presence episodes remained after preprocessing. During a 30-minute validation window, smokers and passersby were counted manually within a 25-m radius. During the validation window, 6230 signal records formed 104 stays, with a mean stay duration of 9.89 (SD 7.89) minutes. During the validation window, direct observation recorded 14 smokers and 207 passersby within the 25-m radius. Applying the rule-based classification and calibration ratios to business hours data yielded estimated day totals of 262 smokers and 3907 passersby within the plausible SHS exposure range. Estimated smoker counts showed 2 peaks, around noon and 4 PM, whereas passerby volume peaked around midday. In an exploratory analysis, a random forest model using stay duration, mean RSSI, and RSSI variability achieved an accuracy of 0.95, sensitivity of 0.75, specificity of 0.97, and area under the receiver operating characteristic curve of 0.99. Conclusions: This formative field study suggests that passive Wi-Fi packet sensing, combined with brief on-site observation, can be used to estimate population-level exposure opportunity around an outdoor smoking area. The method identified substantial numbers of potentially exposed passersby in a high-footfall public setting. Although the findings are site specific and preliminary, they indicate that exposure-count metrics may complement concentration-based and survey-based SHS research. Further studies incorporating repeated validation, direct pollutant monitoring, and multiple sites are needed to refine the method and strengthen its usefulness for tobacco control and public health decision-making.

Background: Community-based child-rearing support services are essential for promoting equitable access to maternal and childcare. However, spatial inequities in accessibility may persist in municipalities that encompass both urban and rural areas. The expansion of open government data enables transparent evaluation of spatial accessibility in local health planning. Purpose: This study aimed to evaluate the spatial accessibility of community-based child-rearing health consultation services in Kanazawa City and to demonstrate a reproducible open-data GIS framework applicable to municipal-level child health planning. Methods: A cross-sectional spatial analysis was conducted using consultation site locations and population data in 250-m grid-cells for children aged 0–2 years. Network-based service areas were generated using predefined walking (900 m and 1,800 m) and driving (7.5 km and 15 km) distances. Both area-based coverage and population-weighted coverage within these service areas were calculated. Results: Forty-nine consultation sites were identified. At the 1,800-m walking-distance threshold, service areas covered 76.1% of child-inhabited residential grid areas and 86.5% of the population aged 0–2 years, indicating higher population-weighted coverage than area-based coverage. In contrast, driving-distance service areas encompassed nearly all residential grid areas and virtually all children within the municipality. Conclusions: Pedestrian accessibility to child-rearing consultation services varies within this mixed urban–rural municipality, leaving geographically dispersed areas underserved despite relatively high population-weighted coverage in urban districts. Open-data-driven spatial analysis using both area-based and population-weighted indicators provides a scalable framework for identifying spatial inequities and supporting evidence-based municipal child health planning