Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City

Abstract

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007–2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0–15 years old). Middle-aged people (16–65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8–1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place.

An association between air pollution and daily most frequently visits of eighteen outpatient diseases in an industrial city

“…Except for rainfall and wind directions, the environmental data resembles the patterns of normal distribution 29 . Test of the normal distribution of medical data also exhibit patterns of normal distribution 29,30 (shown also in Supplementary Materials). All environmental data that were used as model variables were normalized by the maximum values of each variable to ensure that the computation were on the same unitless ground when making the comparison 33 .…”

“…A disease-air pollution model. A multivariate disease-air pollution model used in this work were used in previous studies in finding an association between hospital outpatient visits for respiratory diseases and environmental factors 29 , and between hospital outpatient visits for accidents and environmental factors 30 . In this section, we describe detailed computational steps used in this work with the same multivariate model.…”

“…Notice that time-series data of daily outpatient number M i j , of a disease, and 12 environmental factors are known from input data. The holidays and weekends were removed from the time-series data to avoid the artificial effect of low outpatients on these public holidays 29,30 . Except for the removal of medical and environmental data from public holidays, no other processes were applied to the input time-series data.…”

“…PM 2 5 , O 3 , CO, NO, NO 2 , SO 2 , T, P, WD, WS, and RH. The daily outpatient visits were computed according to the 3 age groups of outpatients (0-15, 16-65, and higher than 65 years old as in Wang and Chau 29 and Chau and Wang 30 . A spectrum of 18 diseases was sorted based on the International Classification of Diseases, 9th Revision (ICD-9) as shown in Table 1.…”

“…1 requires the input of daily outpatient visits for each of the 18 diseases. The data of daily outpatient visits for the 18 diseases from the Taiwan Landseed Hospital were used in this work 29,30 . Each medical record contains the residential location of the outpatient.…”