The effects of topical anesthesia on outcomes and glucose control in diabetic patients treated with split-thickness skin graft surgery.

BACKGROUND: Using split-thickness skin grafting to treat diabetic foot and leg ulcers is common. Diabetic patients usually exhibit multiple comorbidities and high risks of adverse responses to general and spinal anesthesia. Topical anesthesia can be an alternative to avoid these risks. In this study, the clinical experience of split-thickness skin grafting under topical anesthesia was demonstrated, to evaluate its effectiveness and benefits in diabetic patients. METHODS: From 2018 to 2020, diabetic patients with foot or leg wounds undergoing split-thickness skin grafting were reviewed and categorized into two groups according to the anesthesia methods: topical anesthesia and general/spinal anesthesia. Patient demographics, wound characteristics and healing status, postoperative complications, and perioperative blood glucose levels were recorded and analyzed. RESULTS: During the study period, 28 patients underwent split-thickness skin grafting under topical anesthesia and 46 under general/spinal anesthesia. The rate of complete wound healing in 4 weeks was similar in both the groups. The topical anesthesia group suffered fewer postoperative infections (3.6% vs 21.7%, p = 0.044), required shorter postoperative hospitalization (8.3 ± 6.2 vs 11.1 ± 7.2 days, p = 0.048), and exhibited lower mean blood glucose levels and less glucose variability than the general/spinal anesthesia group. CONCLUSION: Conducting split-thickness skin grafting under topical anesthesia was shown to be a safe and effective means of treating leg and foot wounds in diabetic patients.

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data.

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

Big Data, Decision Models, and Public Health.

As the digital era unfolds, the volume and velocity of environmental, population, and public health data are rapidly increasing [...].

Association between weather and utilisation of physical therapy in patients with osteoarthritis: a case-crossover study.

BACKGROUND: During varied weather conditions, patients with osteoarthritis experience different severity of symptoms and signs. However, weather may also cause barriers or incentives for patients to seek medical services. These factors may result in changes in medical utilisation; however, no studies have investigated whether the probability of physical therapy utilisation among patients with osteoarthritis is associated with changes in meteorological factors. METHOD: By using a secondary data of NHID in Taiwan, we conducted a population-based, retrospective study with case-crossover design for patients initially diagnosed with osteoarthritis between 2000 and 2013. The meteorological factors of months with the lowest treatment rate were used as patients' own control periods and compared with the parameters of months with high treatment frequency. The risk of exposure to different meteorological factors, including mean temperature, daily highest temperature, daily minimum temperature, diurnal temperature range, relative humidity, and barometric pressure, was estimated and represented by odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: A total of 8,130 patients were recruited. Regardless of univariate or multivariable analysis, increased daily highest temperature enhanced the frequency of physical therapy (OR: 1.04; 95% CI: 1.02-1.05; p < 0.01; OR: 1.07; 95% CI: 1.04-1.10; p < 0.01). When the weather was hotter (> 23 °C), higher diurnal temperature range and humidity resulted in an increase in the utilisation of physical therapy. However, when the weather was colder (< 23 °C), reverse effects were observed. CONCLUSIONS: An increase in temperature increases the probability of physical therapy resource use. Therefore, temperature, along with other meteorological factors, may play a key role in the utilization of physical therapy among patients with osteoarthritis.

Decline in hospitalization for childhood asthma in different air pollution regions in Taiwan, 2001-2012.

This study aimed to investigate the trends in childhood asthma hospitalization in regions with differing levels of air pollution in Taiwan, 2001-2012. Joinpoint regression was used to identify significant trend changes. The hospitalization rate varied according to gender, geographic region, and age. The incidence of childhood asthma hospitalization decreased from 127.99 to 76.67 (/100,000 population), with an average annual percentage change of around -4.1%; in the Yilan region, the average air pollution concentrations were 19.92 µg/m3, 39.47 µg/m3, 25.99 ppb, 2.19 ppb, and 11.23 ppb for PM2.5, PM10, O3, SO2, and NO2, respectively, which were lower than Taiwan's average values; however, the childhood asthma hospitalization rate was the highest (179.75/100,000 population). The national trend in childhood asthma hospitalization exhibited a significant decrease. The effects of air pollution on childhood asthma were greater in the higher-level air pollution regions, while less association was observed in the lower-level air pollution regions.

Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance.

Using Dorsal Metacarpal Artery Perforator Flap in the Soft Tissue Reconstruction of Traumatic Finger Defects: A Single-Center Study.

INTRODUCTION: The reconstruction of soft tissue defects of fingers is a challenge due to the limitations of local tissue availability. The dorsal metacarpal artery perforator (DMAP) flap is a vascular island flap raised on the dorsum of the hand, and it is a good option for finger reconstruction by replacing similar-for-similar in a single operation. In this study, we would like to share our experience of using the DMAP flap in cases of various traumatic finger defects. MATERIALS AND METHODS: From November 2016 to May 2019, patients who had traumatic finger injuries and had undergone DMAP flap for soft tissue reconstruction were examined. The patients' demographic data, injury and flap characteristics, wound healing status, and complications were collected and studied. The functional and aesthetic outcomes were evaluated using the Michigan Hand Outcomes Questionnaire. RESULTS: There was a total of 10 patients included in this study, of which 9 were male and 1 was female. The average age was 43 years (17-66 years). Seven patients were administered general anesthesia, and 3 others wide-awake local anesthesia. The average flap size was 4.9 × 2.0 cm, and all the donor sites were primarily closed. Nearly half of the patients had temporary venous congestion, but most of the flaps survived well ultimately. Only 1 patient had a partial flap necrosis, which required an additional skin graft. CONCLUSIONS: The DMAP flap offers a thin and pliable skin to reconstruct finger defects within 1-stage surgery. It is easy to harvest with reliable and constant circulation. With adequate design, the DMAP flap can be used to resurface both volar and dorsal finger defects and also can reach the tip of the little finger. The DMAP flap is the ideal flap for reconstruction of traumatic finger defect with either local or general anesthesia.

Deep learning approaches for sleep disorder prediction in an asthma cohort.

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.

Big Data, Decision Models, and Public Health.

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

Liver cancer prediction in a viral hepatitis cohort: A deep learning approach.

Viral hepatitis is the primary cause of liver diseases, among which liver cancer is the leading cause of death from cancer. However, this cancer is often diagnosed in the later stages, which makes treatment difficult or even impossible. This study applied deep learning (DL) models for the early prediction of liver cancer in a hepatitis cohort. In this study, we surveyed 1 million random samples from the National Health Insurance Research Database (NHIRD) to analyze viral hepatitis patients from 2002 to 2010. Then, we used DL models to predict liver cancer cases based on the history of diseases of the hepatitis cohort. Our results revealed the annual prevalence of hepatitis in Taiwan increased from 2002 to 2010, with an average annual percentage change (AAPC) of 5.8% (95% CI: 4.2-7.4). However, young people (aged 16-30 years) exhibited a decreasing trend, with an AAPC of -5.6 (95% CI: -8.1 to -2.9). The results of applying DL models showed that the convolution neural network (CNN) model yielded the best performance in terms of predicting liver cancer cases, with an accuracy of 0.980 (AUC: 0.886). In conclusion, this study showed an increasing trend in the annual prevalence of hepatitis, but a decreasing trend in young people from 2002 to 2010 in Taiwan. The CNN model may be applied to predict liver cancer in a hepatitis cohort with high accuracy.

Cost Effectiveness Analysis and Payment Policy Recommendation-Population-Based Survey with Big Data Methodology for Readmission Prevention of Patients with Paroxysmal Supraventricular Tachycardia treated with Radiofrequency Catheter Ablation.

Recurrence of paroxysmal supraventricular tachycardia (PSVT) has been reported to be lower in patients treated with radiofrequency catheter ablation (RFCA) than in those who are not. Few population-based surveys have stated the cost-effectiveness related to this treatment. We, therefore, performed a nationwide retrospective study using National Health Insurance Research Database (NHIRD) data from 2001-2012 in Taiwan. The incidence of PSVT-related admissions was computed from patients' first admission for a primary PSVT diagnosis. There were 21,086 patients hospitalized due to first-time PSVT, of whom 13,075 underwent RFCA, with 374 recurrences (2.86%). In contrast, 1751 (21.86%) of the remaining 8011 patients who did not receive RFCA, most of whom had financial concerns, experienced PSVT recurrence. The relative PSVT recurrence risk in those who did not receive RFCA was 7.6 times (95%CI: 6.67-8.33) that of those who did undergo RFCA. In conclusion, the PSVT recurrence rate was much higher in patients who did not receive RFCA at their first admission. Furthermore, RFCA proved cost-effective, with the ratio of the incremental cost-effectiveness ratio (ICER) and gross domestic product (GDP) being only 1.15. To prevent readmission and avoid incremental cost, the authority could provide a financial supplement for every patient so that the procedure is performed, reducing the PSVT-recurrence life-years (disease-specific DALY).

Systemic lupus erythematosus is associated with poor outcome after acute myocardial infarction.

BACKGROUND: Systemic lupus erythematosus (SLE) is associated with a higher risk of cardiovascular disease. However, it is not clear whether or not SLE is associated with poor outcomes after acute myocardial infarction (AMI). METHODS AND RESULTS: Using the Taiwan National Health Insurance Database, we identified the SLE group as patients with AMI who have a concurrent discharge diagnosis of SLE. We also selected an age-, sex-, hospital level-, and admission calendar year-matched non-SLE group at a ratio of 1:3 from the total non-SLE group. One hundred fifty-one patients with SLE, 113,791 patients without SLE, and 453 matched patients without SLE were admitted with a diagnosis of AMI. Patients with SLE were significantly younger, predominantly female, and more likely to have chronic kidney disease than those without SLE. The in-hospital mortality rates were 12.6%, 9.0%, and 4.2% in the SLE, total non-SLE, and matched non-SLE groups, respectively. The in-hospital mortality was significantly higher in the SLE group than in the total non-SLE group (OR = 1.98; 95% CI = 1.2-3.26) and the matched non-SLE group (mortality OR = 2.20; 95% CI = 1.06-4.58). In addition, the SLE group was associated with a borderline significant risk of prolonged hospitalization when compared with the non-SLE group. CONCLUSION: SLE is associated with a higher risk of in-hospital mortality and a borderline significantly higher risk of prolonged hospitalization after AMI.

The Short-Term Effects of Ambient Air Pollutants on Childhood Asthma Hospitalization in Taiwan: A National Study.

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 (PM2.5), 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.

A survey of ambulatory-treated asthma and correlation with weather and air pollution conditions within Taiwan during 2001-2010.

Objective: This study of asthma was performed to evaluate annual trends in emergency department (ED) for 10 years. Weather and air pollution factors affecting asthma were also studied in order to identify the important factors and alert the public in advance. Methods: A survey of ambulatory-treated asthma patients was performed and the correlations with weather and air pollution factors examined in a cohort of one million patients in 2010. The fixed-cohort study analyzed trends, medical costs, and annual prevalence grouped by age and gender. Results: The number of asthma patients visiting EDs and non-emergency (non-ED) clinics significantly increased, with average annual percentage changes (AAPCs) of 2.3 and 4.6%, respectively. The average direct medical cost for EDs was increased significantly as compared with that of non-ED visits. Classification of asthma visits by hospital level indicated that local hospitals and others exhibited a significantly increasing trend (AAPC =15.3% [95% CI: 14.3-16.2]). The annual prevalence of asthma in males, females, and children was significantly increased (AAPCs of 1.5, 1.8, and 3.9%, respectively). Asthma patient hospitalizations were significantly correlated with temperature, humidity, and air pollution factors. Conclusions: The number of non-ED visits due to asthma increased, and the average direct medical cost for ED admissions also increased. Asthma patients tended to visit local hospitals primarily. Asthma visits by children increased, but a decrease was observed in adults. The number of hospitalized asthma patients was negatively correlated with temperature and humidity but positively correlated with the levels of PM2.5, PM10, and NO2.

Investigating the effect of daily sleep on memory capacity in college students.

BACKGROUND: Sleep is a natural periodic state of rest for body and mind and daily sleep affects physical and mental health. However, it is essential to address intensity of sleep characteristics affecting the memory capacity of humans positively or negatively. OBJECTIVE: Using wearable devices to observe and assess the effect of daily sleep on memory capacity of college students. METHODS: This study assessed the daily sleep characteristics and memory capacity of 39 college students who used wrist-worn devices. The spatial span test (SST) was used to evaluate the memory capacity. RESULTS: The study indicated a negative correlation between memory capacity and awake count on the test date and during the week before the test date (r=-0.153 (95% CI: -0.032, -0.282), r=-0.391 (95% CI: -0.520, -0.235), respectively). However, the minutes asleep on the test date and during the week before the test date positively affected memory capacity (r= 0.127 (95% CI: 0.220, 0.025), r= 0.370 (95% CI: 0.208, 0.500), respectively). In addition, spending ⩾ 6 hours and 42 minutes asleep on the test date or ⩾ 6 hours and 37 minutes asleep per day on average during the week before the test date resulted in a better memory capacity. CONCLUSIONS: A lower awake count led to a higher memory capacity in college students, as did more minutes asleep.

A Study of the Effects of Daily Physical Activity on Memory and Attention Capacities in College Students.

This study evaluated the relationship between daily physical activity (DPA) and memory capacity, as well as the association between daily activity and attention capacity, in college students in Taiwan. Participants (mean age = 20.79) wore wearable trackers for 106 days in order to collect DPA. These data were analyzed in association with their memory and attention capacities, as assessed using the spatial span test (SST) and the trail making test (TMT). The study showed significant negative correlations between memory capacity, time spent on the attention test (TSAT), calories burnt, and very active time duration (VATD) on the day before testing (r = -0.272, r = -0.176, r = 0.289, r = 0.254, resp.) and during the week prior to testing (r = -0.364, r = -0.395, r = 0.268, r = 0.241, resp.). The calories burnt and the VATD per day thresholds, which at best discriminated between normal-to-good and low attention capacity, were ≥2283 calories day-1, ≥20 minutes day-1 of very high activity (VHA) on the day before testing, or ≥13,640 calories week-1, ≥76 minutes week-1 of VHA during the week prior to testing. Findings indicated the short-term effects that VATD and calories burnt on the day before or during the week before testing significantly and negatively associated with memory and attention capacities of college students.

Application of a Time-Stratified Case-Crossover Design to Explore the Effects of Air Pollution and Season on Childhood Asthma Hospitalization in Cities of Differing Urban Patterns: Big Data Analytics of Government Open Data.

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 (PM2.5, 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.

Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms.

OBJECTIVES: The aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion. METHODS: A data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1. RESULTS: Five hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. CONCLUSIONS: Our study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.

Effect of socioeconomic inequalities on cholecystectomy outcomes: a 10-year population-based analysis.

BACKGROUND: Although numerous epidemiological studies on cholecystectomy have been conducted worldwide, only a few have considered the effect of socioeconomic inequalities on cholecystectomy outcomes. Specifically, few studies have focused on the low-income population (LIP). METHODS: A nationwide prospective study based on the Taiwan National Health Insurance dataset was conducted during 2003-2012. The International Classification of ICD-9-CM procedure codes 51.2 and 51.21-51.24 were identified as the inclusion criteria for cholecystectomy. Temporal trends were analyzed using a joinpoint regression, and the hierarchical linear modeling (HLM) method was used as an analytical strategy to evaluate the group-level and individual-level factors. Interactions between age, gender and SES were also tested in HLM model. RESULTS: Analyses were conducted on 225,558 patients. The incidence rates were 167.81 (95% CI: 159.78-175.83) per 100,000 individuals per year for the LIP and 123.24 (95% CI: 116.37-130.12) per 100,000 individuals per year for the general population (GP). After cholecystectomy, LIP patients showed higher rates of 30-day mortality, in-hospital complications, and readmission for complications, but a lower rate of routine discharge than GP patients. The hospital costs and length of stay for LIP patients were higher than those for GP patients. The multilevel analysis using HLM revealed that adverse socioeconomic status significantly negatively affects the outcomes of patients undergoing cholecystectomy. Additionally, male sex, advanced age, and high Charlson Comorbidity Index (CCI) scores were associated with higher rates of in-hospital complications and 30-day mortality. We also observed that the 30-day mortality rates for patients who underwent cholecystectomy in regional hospitals and district hospitals were significantly higher than those of patients receiving care in a medical center. CONCLUSION: Patients with a disadvantaged finance status appeared to be more vulnerable to cholecystectomy surgery. This result suggested that further interventions in the health care system are necessary to reduce this disparity.

The incidence and factors of hip fractures and subsequent morbidity in Taiwan: An 11-year population-based cohort study.

Hip fractures are a major problem to elder population, but subsequent morbidity is unclear about environmental factors and socioeconomic conditions. The study aims to investigate the incidence of hip fractures treated by the surgery; to compare the sequelae and temporal trends of hip fractures; to evaluate the seasonal effects in the subsequent short-term and long-term morbidities after hip fractures. A cohort study design is conducted using national health research datasets between 2000 and 2010. The ICD-9-CM diagnostic codes were utilized to investigate the incidence of hip fractures and the corresponding treatments. Hierarchical modeling was used to analyze the factors associated with various types of hip fractures. The results indicated that females had a lower incidence in the 30-44 age group, but a significantly higher incidence than males among those aged 60 years or older (adjusted rates 232.1 vs. 100.3 per 100,000 persons, p<0.001). The incidence of hip fractures in the low-income group showed no significant difference compared to that in the general population. There was a temporal trend of a 8.6% increase in the incidence of all types of hip fractures over the period of 2000-2010. A summer-winter variation is observed among the elderly. Hip fractures and subsequent morbidity are increasing in Taiwan's aging society. Older age, female gender, and time periods were independent risk factors for subsequent morbidities after surgical treatment. The result of this study is useful to the healthcare policy makers and to raise the public awareness of hip fractures.