RESUMO
Numerous studies have examined the effects of weather on emergency ambulance service (EAS) demand. Given Taipei's unique physical and social environments, empirical evidence collected from other regions may not be applicable. Collecting more information about the characteristics of vulnerable groups and the effects of weather could help the EAS managing authority in formulating cost-effective EAS policies. This study aims to look at the effects of weather on EAS demand in Taipei and to make a comparison with Hong Kong, which is also an Asian city and has a similar cultural context. The study analyzed over 370,000 EAS usage records from the Taipei City Fire Department. These records were aggregated into time series data according to patients' characteristics and then regressed on meteorological data via multivariate forward regression. The effect size differences of the variance explained by different groups of EAS users' regression models were compared. Afterward, the results of the regression analysis from Taipei were compared with those from a Hong Kong study. Elderly and critical patients in both cities showed significantly more sensitivity to weather than other patients. Further analysis showed that non-trauma cases were related to weather in Taipei. Although both cities had similar results, the Taipei study clearly showed that elderly and critical patients were more sensitive to weather than other patient subgroups. Health education programs should focus on the vulnerable groups identified in this study in order to increase their awareness and help them protect themselves before the onset of adverse weather conditions. By generating results that are directly applicable to Taipei, the formulation of inappropriate EAS policies can be prevented.
RESUMO
OBJECTIVE: This study aims to assess rural-urban differences in the predictors of emergency ambulance service (EAS) demand and misuse in New Taipei City. Identifying the predictors of EAS demand will help the EAS service managing authority in formulating focused policies to maintain service quality. METHODS: Over 160,000 electronic EAS usage records were used with a negative binomial regression model to assess rural-urban differences in the predictors of EAS demand and misuse. RESULTS: The factors of 1) ln-transformed population density, 2) percentage of residents who completed up to junior high school education, 3) accessibility of hospitals without an emergency room, and 4) accessibility of EAS were found to be predictors of EAS demand in rural areas, whereas only the factor of percentage of people aged above 65 was found to predict EAS demand in urban areas. For EAS misuse, only the factor of percentage of low-income households was found to be a predictor in rural areas, whereas no predictor was found in the urban areas. CONCLUSION: Results showed that the factors predicting EAS demand and misuse in rural areas were more complicated compared to urban areas and, therefore, formulating EAS policies for rural areas based on the results of urban studies may not be appropriate.
