Road traffic mortalities (RTMs), a leading cause of death globally, mostly occur in low- and middle-income countries, and having sufficient healthcare resources could lower the number of these fatalities. Our study aimed to illustrate the incidence of RTMs per 100,000 population and to compare the distribution of healthcare resources from 2011 to 2021 with rates of RTMs in the 77 provinces of Thailand. We divided the population into adults (≥ 15 years) and children (0–14 years). Lorenz curve and Gini coefficient were used to measure the level of distribution and equality of hospital resources and in relation to RTMs across the country. The average number of deaths was 30.34 per 100,000 per year, with male predominance. The RTM rates for adults and children were 32.71 and 19.08 per 100,000 respectively, and motorcycle accidents accounted for the largest percentage of deaths across all age groups. The Gini coefficient showed that operating rooms (0.42) were the least equally distributed hospital resource, while physicians were the most equally distributed (0.34). Anomalies were identified between the distribution of RTMs and available hospital resources. We hope our study will be beneficial in reallocating these resources more fairly to reflect the different numbers of traffic accidents in each province with the aim of reducing lower traffic-related deaths.

Objective To evaluate the diagnostic performance of manual grading of anterior segment optical coherence tomography (AS-OCT) in detection of plateau iris configuration (PIC) based on the presence of standardised ultrasound biomicroscopy (UBM) criteria in at least two quadrants; namely, clinical diagnosis of PIC (DxPIC). Methods and analysis In this cross-sectional study, paired AS-OCT and UBM images were evaluated by three glaucoma specialists. AS-OCT was classified into two mechanisms, PIC versus non-PIC, of primary angle closure disease (PACD) and AS-OCT-PIC diagnostic performance was tested with DxPIC. Results One hundred and seventy-nine eyes of 142 patients were enrolled for analysis, and DxPIC was found in 85 eyes (47.49%). Intraobserver agreement rates of AS-OCT classification by the graders were 0.77, 0.701 and 0.742 (all p<0.001), and interobserver agreement rates, between a senior glaucoma specialist and the other two glaucoma specialists, were 0.68 and 0.702 (all p<0.001). Plateau iris was classified in AS-OCT images by the three graders, rated 32.96%–39.1% and 24.58%–34.08% in the horizontal and vertical axes, respectively. Diagnostic performance was analysed, yielding sensitivity ranging from 56.47% to 77.78%, and specificity of 48.94% to 64.29%. We applied disease prevalence of 30%, revealing positive predictive values varying from 32.16% to 44.44%, and negative predictive values of 72.4% to 85.71%. Accuracy ranged from 51.2% to 65%. Agreement between the two devices was fair, kappa range 0.31–0.351. Conclusion Performance of manual grading of AS-OCT in detection of DxPIC was relatively poor; therefore, unadjusted AS-OCT does not appear to be good for manual PIC screening in PACD patients and cannot serve as a substitute for UBM in PIC detection.

Background: Falls are the leading cause of injury among older adults, with vision impairment recognized as a significant risk factor. However, many existing studies have been limited by small sample sizes, retrospective designs, or insufficient adjustment for confounding factors. To overcome these limitations, we used data from the University of Michigan’s Health and Retirement Study (HRS) to analyze the association between self-reported vision and fall risk among older adults in a large, nationally representative sample. Objective: The objective of this study was to investigate the association between vision impairment and falls and assess whether subjective vision impairment predicts future falls in older adults. Methods: This cross-sectional and longitudinal analysis used data from the HRS (1996‐2020) to assess the relationship between self-reported vision, glaucoma history, and falls among US adults aged 65 years and older. HRS uses a biennial, multistage area probability sample survey design, collecting data with community-dwelling individuals followed up every 2 years until death, tracking health, economic, and social outcomes. Multivariate logistic regression was used to analyze associations between self-reported vision and self-reported falls in the past 2 years. Results: A total of 38,835 respondents contributed 117,834 observations. The weighted proportion of participants reporting falls was 37.9% (95% CI 37.7%‐40.1%). Significant risk factors for falls included overall eyesight impairment (adjusted odds ratio [aOR] 1.36, 95% CI 1.20‐1.56), distance vision impairment (aOR 1.37, 95% CI 1.32‐1.42), near vision impairment (aOR 1.33, 95% CI 1.27‐1.37), and glaucoma (aOR 1.15, 95% CI 1.07‐1.24). A similar association was observed for serious falls, where overall eyesight impairment (aOR 1.20, 95% CI 1.03‐1.44), distance vision impairment (aOR 1.14, 95% CI 1.07‐1.22), near vision impairment (aOR 1.12, 95% CI 1.05‐1.18), and glaucoma (aOR 1.15, 95% CI 1.05‐1.26) were significant. In longitudinal analyses, overall vision impairment (aOR 1.23, 95% CI 1.16‐1.29), distance vision impairment (aOR 1.27, 95% CI 1.20‐1.38), near vision impairment (aOR 1.23, 95% CI 1.19‐1.32), and glaucoma (aOR 1.25, 95% CI 1.13‐1.37) increased the risk of future falls. Reported overall vision was significantly associated with the number of falls in both the same (P<.001) and subsequent (P<.001) survey cycles. Conclusions: Both distance and near vision impairment, as well as glaucoma, are associated with a higher risk of falls in older adults and present possible areas for intervention and prevention.

Objectives: Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public’s practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK. Methods: After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis. Results: AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13–1.17, p<0.001). Conclusions: AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people’s mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.