The epidemiology of acute orthopedic dislocations is poorly understood. A nationwide database provides a valuable resource for examining this issue in the Taiwanese population. A 6-year retrospective cohort study of 1,000,000 randomly-sampled beneficiaries from the year 2005 was used as the original population. Based on the hospitalized and ambulatory data, the concomitant ICD9-CM diagnosis codes and treatment codes were evaluated and classified into 8 and 3 major categories, respectively. The cases matching both inclusive criteria of dislocation-related diagnosis codes and treatment codes were defined as incident cases. During 2000-2005, the estimated annual incidence (per 100,000 population) of total orthopedic dislocations in Taiwan was 42.1 (95%CI: 38.1-46.1). The major cause of these orthopedic dislocations was traffic accidents (57.4%), followed by accident falls (27.5%). The annual incidence dislocation by location was shoulder, 15.3; elbow, 7.7; wrist, 3.5; finger, 4.6; hip, 5.2; knee, 1.4; ankle, 2.0; and foot, 2.4. Approximately 16% of shoulder dislocations occurred with other concomitant fractures, compared with 17%, 53%, 16%, 76% and 52%, respectively, of dislocated elbow, wrist, hip, knee, and ankle cases. Including both simple and complex dislocated cases, the mean medical cost was US$612 for treatment of a shoulder dislocation, $504 for the elbow, $1,232 for the wrist, $1,103 for the hip, $1,888 for the knee, and $1,248 for the ankle. In Taiwan, three-quarters of all orthopedic dislocations were of the upper limbs. The most common complex fracture-dislocation was of the knee, followed by the wrist and the ankle. Those usually needed a treatment combined with open reduction of fractures and resulted in a higher direct medical expenditure.

This investigation determined the effects of air pollution on childhood asthma hospitalization in regions with differing air pollution levels in Taiwan over a long time period. Data of childhood hospital admissions for asthma in patients aged 0–18 years and air quality in eight regions for the period 2001–2012 in Taiwan were collected. Poisson generalized linear regression analysis was employed to identify the relative risks of hospitalization due to asthma in children associated with exposure to varying levels of air pollutants with a change in the interquartile range after adjusting for temperature and relative humidity. Particulate matter ≤2.5 μm (PM_2.5), particulate matter ≤10 μm (PM₁₀), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂), were positively associated with childhood asthma hospitalization, while O₃ was negatively associated with childhood asthma hospitalization. SO₂ was identified as the most significant risk factor. The relative risks for asthma hospitalization associated with air pollutants were higher among children aged 0–5 years than aged 6–18 years and were higher among males than females. The effects of air pollution on childhood asthma were greater in the higher-level air pollution regions, while no association was observed in the lower-level air pollution regions. These findings may prove important for policymakers involved in implementing policies to reduce air pollution.

Few studies have assessed the lagged effects of levels of different urban city air pollutants and seasons on asthma hospitalization in children. This study used big data analysis to explore the effects of daily changes in air pollution and season on childhood asthma hospitalization from 2001 to 2010 in Taipei and Kaohsiung City, Taiwan. A time-stratified case-crossover study and conditional logistic regression analysis were employed to identify associations between the risk of hospitalization due to asthma in children and the levels of air pollutants (PM_2.5, PM₁₀, O₃, SO₂, and NO₂) in the days preceding hospitalization. During the study period, 2900 children in Taipei and 1337 in Kaohsiung aged ≤15 years were hospitalized due to asthma for the first time. The results indicated that the levels of air pollutants were significantly associated with the risk of asthma hospitalization in children, and seasonal effects were observed. High levels of air pollution in Kaohsiung had greater effects than in Taipei after adjusting for seasonal variation. The most important factor was O₃ in spring in Taipei. In children aged 0–6 years, asthma was associated with O₃ in Taipei and SO₂ in Kaohsiung, after controlling for the daily mean temperature and relative humidity.

Objective: Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance Research Database (NHIRD) in order to predict sleep disorder in an asthma cohort from 2002–2010. Methods: The disease histories of the asthma patients were transferred to sequences and matrices for the prediction of sleep disorder by applying machine learning (ML) algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), and deep learning (DL) models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolution Neural Network (CNN). Results: Among 14,818 new asthma subjects in 2002, there were 4469 sleep disorder subjects from 2002 to 2010. The KNN, SVM, and RF algorithms were demonstrated to be successful sleep disorder prediction models, with accuracies of 0.798, 0.793, and 0.813, respectively (AUC: 0.737, 0.690, and 0.719, respectively). The results of the DL models showed the accuracies of the RNN, LSTM, GRU, and CNN to be 0.744, 0.815, 0.782, and 0.951, respectively (AUC: 0.658, 0.750, 0.732, and 0.934, respectively). Conclusions: The results showed that the CNN model had the best performance for sleep disorder prediction in the asthma cohort.