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.
Background: COVID-19 has created health and socioeconomic damage worldwide, and face masks are a low-cost but effective method of preventing transmission of this disease. Artificial intelligence (AI)-assisted systems can come into play to help visualize the public’s awareness of mask wearing and gain a better picture of whether there is adequate practice of protection during the outbreak. We reported the rate of face mask wearing by the general public using the artificial intelligence-assisted face mask detector, AiMASK.Methods: This cross-sectional study was conducted between January 23 and April 22, 2021 in over 32 districts in Bangkok, Thailand. After the introduction of AiMASK, development and internal validation were performed, and average accuracy of 97.8% was found. Data were classified into a protected group (correct face mask wearing) and an unprotected group (incorrect or non-mask wearing). We analyzed the association between factors affecting the unprotected group using univariate logistic regression analysis.Results: No significant difference was found between results from human graders and those of AiMASK using two proportion Z test (p=0.74). AiMASK detected a total of 1,124,524 people, the majority of whom were in the protected group (95.98%). The unprotected group consisted of 2.06% who practised incorrect mask-wearing, and the other 1.96% were those who did not wear masks. A 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 in the unprotected group 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). Districts in the city center were 1.31 times more likely to have higher proportions of unprotected individuals than suburban districts (OR=1.31, 95% CI 1.28-1.34, 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, and half of the unprotected group were those who wore masks incorrectly. Public policies should communicate the importance of wearing masks consistently throughout the day and during holidays as well as providing instructions for effective mask wearing to prevent virus transmission.
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