An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.

Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC).

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.

The electronic medical record management systems may improve monitoring and control of disease activity in patients with ankylosing spondylitis.

To investigate the impact of an electronic medical record management system (EMRMS) on disease activity and the frequency of outpatient visits among patients with ankylosing spondylitis (AS). We identified 652 patients with AS who were followed up for at least 1 year before and after the first Ankylosing Spondylitis Disease Activity Score (ASDAS) assessment and compared the number of outpatient visits and average visit time within 1 year before and after the initial ASDAS assessment. Finally, we analyzed 201 patients with AS who had complete data and received ≥ 3 continuous ASDAS assessments at an interval of 3 months, and we compared the results of the second and third ASDAS assessments with those of the first. The number of annual outpatient visits increased after ASDAS assessment (4.0 (4.0, 7.0) vs. 4.0 (4.0, 8.0), p < 0.001), particularly among those with a high initial disease activity. The average visit time was reduced within 1 year after ASDAS assessment (6.4 (8.5, 11.2) vs. 6.3 (8.3, 10.8) min, p = 0.073), especially among patients whose with an inactive disease activity was < 1.3 (ASDAS C-reactive protein (CRP) 6.7 (8.8, 11.1) vs. 6.1 (8.0, 10.3) min, p = 0.033; ASDAS erythrocyte sedimentation rate (ESR) 6.4 (8.7, 11.1) vs. 6.1 (8.1, 10.0) min, p = 0.027). Among patients who received at least three ASDAS assessments, the third ASDAS-CRP tended to be lower than the first (1.5 (0.9, 2.1) vs. 1.4 (0.8, 1.9), p = 0.058). The use of an EMRMS increased the frequency of ambulatory visits among AS patients with high and very high disease activity and reduced the visit time among those with an inactive disease. Continual ASDAS assessments may help control the disease activity of patients with AS.

Sustaining Improvements of Surgical Site Infections by Six Sigma DMAIC Approach.

BACKGROUND: SSIs (surgical site infections) are associated with increased rates of morbidity and mortality. The traditional quality improvement strategies focusing on individual performance did not achieve sustainable improvement. This study aimed to implement the Six Sigma DMAIC method to reduce SSIs and to sustain improvements in surgical quality. The surgical procedures, clinical data, and surgical site infections were collected among 42,233 hospitalized surgical patients from 1 January 2019 to 31 December 2020. Following strengthening leadership and empowering a multidisciplinary SSI prevention team, DMAIC (Define, Measure, Analyze, Improve, and Control) was used as the performance improvement model. An evidence-based prevention bundle for reduction of SSI was adopted as performance measures. Environmental monitoring and antimicrobial stewardship programs were strengthened to prevent the transmission of multi-drug resistant microorganisms. Process change was integrated into a clinical pathway information system. Improvement cycles by corrective actions for the risk events of SSIs were implemented to ensure sustaining improvements. We have reached the targets of the prevention bundle elements in the post-intervention period in 2020. The carbapenem resistance rates of Enterobacteriaceae and P. aeruginosa were lower than 10%. A significant 22.2% decline in SSI rates has been achieved, from 0.9% for the pre-intervention period in 2019 to 0.7% for the post-intervention period in 2020 (p = 0.004). Application of the Six Sigma DMAIC approach could significantly reduce the SSI rates. It also could help hospital administrators and quality management personnel to create a culture of patient safety.

Accuracy of MRI derived cerebral aqueduct flow parameters in the diagnosis of idiopathic normal pressure hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) is a potentially reversible cause of dementia-like symptoms among the elderly. Current diagnostic guidelines for iNPH rely on clinical manifestations and ventricular morphology, which often lack accuracy. While magnetic resonance imaging (MRI) CSF flowmetry of the cerebral aqueduct provides a noninvasive aid to differential diagnosis, previous studies suffered from small sample sizes. This study compares the accuracy of different CSF flow parameters for iNPH diagnosis in a general patient population. From 2016 to 2018, a total of 216 subjects over 60 years of age were retrospectively enrolled, including 38 patients with iNPH and 178 patients with non-iNPH neurological conditions. All participants received phase-contrast MRI (PC-MRI) CSF flowmetry, with measurements performed independently by two radiologists. Flow parameters of iNPH and non-iNPH groups were compared along with their diagnostic accuracy. Absolute stroke volume (ABSV), forward flow, backward flow, mean flux and peak velocity were significantly higher in iNPH patients (P < 0.001, P < 0.001, P < 0.001, P = 0.008, P = 0.038, respectively). Backward flow had the highest diagnostic accuracy, followed by ABSV and forward flow. Net caudocranial aqueductal flow was observed in both groups, but with greater volume in the iNPH group. PC-MRI provides a non-invasive method of CSF flowmetry across the cerebral aqueduct and may aid in iNPH diagnosis. ABSV and its component flow values may provide better accuracy in identifying iNPH than other parameters.

The BASDAI Cut-Off for Disease Activity Corresponding to the ASDAS Scores in a Taiwanese Cohort of Ankylosing Spondylitis.

Objectives: The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) has been widely utilized to evaluate disease activity in patients with ankylosing spondylitis (AS) by an arbitrary cut-off of ≥4 to indicate high disease activity and initiate biological therapy. The Ankylosing Spondylitis Disease Activity Score (ASDAS) is a new composite index to assess AS disease activity states that have been defined and validated. ASDAS ≥2.1 was selected as a criterion to start biological therapy. The purpose of this study was to estimate the corresponding BASDAI and ASDAS cut-off in a Taiwanese AS cohort. Methods: From November 2016 to October 2018, we assessed the ASDAS and the BASDAI regularly and recorded demographic data for 489 AS patients in Taichung Veterans General hospital (TCVGH) using an electronic patient-reported data system linked to electronic medical records. We used receiver operating characteristic curves with Youden's J statistic to determine the BASDAI values that correspond to ASDAS disease activity cut-offs (i.e., 1.3, 2.1, and 3.5). Results: In our population, the best trade-off BASDAI values corresponding to ASDAS -C-reactive protein (CRP) 1.3, 2.1, and 3.5 were 2.1, 3.1, and 3.7, respectively. The optimal BASDAI values corresponding to ASDAS-erythrocyte sedimentation rates 1.3, 2.1, and 3.5 were 2.0, 2.6, and 4.8, respectively. Conclusion: We propose a revised BASDAI cut-off based on our data, as BASDAI scores are commonly used globally. A more reasonable, lower BASDAI cut-off to initiate or change biological therapy will bring us closer to better decisions to treat AS patients.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation.

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current standard involving manual segmentation of aqueduct region of interest (ROI). Retrospective analysis was performed on 333 patients who underwent PC-MRI, totaling 353 imaging studies. Aqueduct flow measurements using manual ROI placement was performed independently by two radiologists. Two types of CNNs, MultiResUNet and UNet, were trained using ROI data from the senior radiologist, with PC-MRI studies being randomly divided into training (80%) and validation (20%) datasets. Segmentation performance was assessed using Dice similarity coefficient (DSC), and CSF flow parameters were calculated from both manual and CNN-derived ROIs. MultiResUNet, UNet and second radiologist (Rater 2) had DSCs of 0.933, 0.928, and 0.867, respectively, with p < 0.001 between CNNs and Rater 2. Comparison of CSF flow parameters showed excellent intraclass correlation coefficients (ICCs) for MultiResUNet, with lowest correlation being 0.67. For UNet, lower ICCs of -0.01 to 0.56 were observed. Only 3/353 (0.8%) studies failed to have appropriate ROIs placed by MultiResUNet, compared to 12/353 (3.4%) failed cases for UNet. In conclusion, CNNs were able to measure aqueductal CSF flow with similar performance to a senior neuroradiologist. MultiResUNet demonstrated fewer cases of segmentation failure and more consistent flow measurements compared to the widely adopted UNet.

The effect of weather, air pollution and seasonality on the number of patient visits for epileptic seizures: A population-based time-series study.

OBJECTIVE: The objective of the study was to explore the influences of seasonality, meteorological conditions, and air pollution exposure on the number of patients who visit the hospital due to seizures. METHODS: Outpatient and inpatient data from the National Health Insurance Database of Taiwan from 2009 to 2013, meteorological data from the Meteorological Bureau, and air pollution exposure data from the Taiwan Air Quality Monitoring Stations were collected and integrated into daily time series data. The following data processing and analysis results are based on the mean of the 7 days' lag data of the 18 meteorological condition/air pollution exploratory factors to identify the critical meteorological conditions and air pollution exposure factors by executing univariate analysis. The average hospital visits for seizure per day by month were used as an index of observation. The effect of seasonality has also been examined. RESULTS: The average visits per day by month had a significant association with 10 variables. Overall, the number of visits due to these factors has been estimated to be 71.529 (13.7%). The most obvious factors affecting the estimated number of visits include ambient temperature, CH4, and NO. Six air pollutants, namely CH4, NO, CO, NO2, PM2.5, and NMHC had a significantly positive correlation with hospital visits due to seizures. Moreover, the average daily number of hospital visits was significantly high in January and February (winter season in Taiwan) than in other months (R2 = 0.422). CONCLUSION: The prediction model obtained in this study indicates the necessity of rigorous monitoring and early warning of these air pollutants and climate changes by governments. Additionally, the study provided a firm basis for establishing prediction models to be used by other countries or for other diseases.

Gender difference in ASAS HI among patients with ankylosing spondylitis.

OBJECTIVE: To assess the associations of the Assessment of Spondyloarthritis International Society Health Index (ASAS HI) with gender and other factors in patients with ankylosing spondylitis (AS). METHODS: From November 2017 to October 2018, we measured the Ankylosing Spondylitis Disease Activity Score (ASDAS), the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) and the ASAS HI score for AS patients at the Taichung Veterans General Hospital. After adjusting for disease activity (ASDAS-erythrocyte sedimentation rate [ESR], ASDAS- C-reactive protein [CRP], BASDAI+ESR or BASDAI+CRP), mSASSS and other potential confounders including medications, comorbidities, and laboratory data, any associations between gender and the sum score of ASDAS HI were assessed using multiple linear regression analysis, as well as any associations between gender and an ASAS HI score >5 using multivariable logistic regression analysis. RESULTS: A total of 307 AS patients (62 [20.2%] females, mean age 46.4 years [S.D. 13.3], mean symptom duration 20.6 years [S.D. 12.1]) were included. Multiple linear regression analysis showed that the male gender was significantly associated with a lower ASAS HI (B = -1. 91, 95% confidence interval [CI], -2.82--1.00, p <0.001). Multivariable logistic regression analysis revealed that males also had a lower risk of achieving scores of ASAS HI > 5 than females (odds ratio = 0.15, 95% CI, 0.07-0.36, p <0.001). Disease activity measures, including ASDAS-ESR, ASDAS-CRP and BASDAI, had positive correlations with ASAS HI. CONCLUSION: This single-center, cross-sectional study revealed that a higher ASAS HI score was significantly associated with female gender and higher disease activity measures.

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome.

PURPOSE: The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy. METHODS: We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies. RESULTS: Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis. CONCLUSION: We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.

Prolonged length of stay for acute hospital admissions as the increasing of age: A nationwide population study for Taiwan's patients with cerebral palsy.

BACKGROUND: Studies investigating reasons for the admission and the associated lengths of stay (LOSs) among cerebral palsy (CP) patients are limited. This study determined common reasons for acute hospitalizations and the LOSs among children, adolescents, and young adults with CP. METHODS: We performed a secondary analysis of data. CP patients aged 4-32.9 years were identified by CP registry in the catastrophic illness patient registry of the 2010 Taiwan National Health Insurance Research Database. Data of admission claims from 2010 to 2011 were analyzed. Reasons for admissions were identified according to International Classification of Diseases codes. Common reasons, frequencies of admissions for each reason, and LOSs were reported. RESULTS: Pneumonia, other respiratory problems, and epilepsy were the top three reasons for admissions in all groups. Other common reasons in all groups were sepsis, other respiratory infections, and gastrointestinal problems. The reasons specific to children included orthopedic issues; ear, nose, and throat problems; and urinary tract infections (UTIs). In youths, scoliosis, and contractures, were unique reasons. In young adults, UTIs, blood problems, and mental illness, were special reasons. Most admission reasons appeared to prolong LOS, and the LOS exhibited an increasing trend as age increased. CONCLUSION: The results implied that patients with CP are more susceptible to most disease invasions. Our results also suggest that the current care system in Taiwan is unsuitable for patients with CP. These results can be used as guidance for planning effective multidisciplinary assessments in the future.

Association of epilepsy and asthma: a population-based retrospective cohort study.

BACKGROUND: Epidemiologic data supporting the epilepsy-asthma association are insufficient. Therefore, we examined this association in this study. METHODS: By using claims data from the National Health Insurance Research Database (Taiwan), we executed a retrospective cohort analysis. Analysis 1 entailed comparing 150,827 patients diagnosed as having incident asthma during 1996-2013 with disease-free controls who were selected randomly during the same period, frequency matched in terms of age and sex. Similarly, analysis 2 entailed comparing 25,274 patients newly diagnosed as having epilepsy with sex- and age-matched controls who were selected randomly. At the end of 2013, we evaluated in analysis 1 the epilepsy incidence and risk and evaluated in analysis 2 the asthma incidence and risk. We applied Kaplan-Meier analysis to derive plots of the proportion of asthma-free seizures. RESULTS: In analysis 1, the asthma group exhibited a higher epilepsy incidence than did the control group (3.05 versus 2.26 per 1,000 person-years; adjusted hazard ratio: 1.39, 95% CI [1.33-1.45]). We also noted a greater risk of subsequent epilepsy in women and girls. In analysis 2, we determined that the asthma incidence between the control and epilepsy groups did not differ significantly; however, some age subgroups including children and individuals in their 30s had an increased risk. A negative association was found in adolescents. The Kaplan-Meier analysis revealed epilepsy to be positively associated with subsequent onset of asthma within seven years of epilepsy diagnosis. DISCUSSION: Asthma may be associated with high epilepsy risk, and epilepsy may be associated with high asthma risk among children and individuals in their 30s. Nevertheless, people with epilepsy in other age subgroups should be aware of the possibility of developing asthma within seven years of epilepsy diagnosis.

Comparison of risks factors for unplanned ICU transfer after ED admission in patients with infections and those without infections.

BACKGROUND: The objectives of this study were to compare the risk factors for unplanned intensive care unit (ICU) transfer after emergency department (ED) admission in patients with infections and those without infections and to explore the feasibility of using risk stratification tools for sepsis to derive a prediction system for such unplanned transfer. METHODS: The ICU transfer group included 313 patients, while the control group included 736 patients randomly selected from those who were not transferred to the ICU. Candidate variables were analyzed for association with unplanned ICU transfer in the 1049 study patients. RESULTS: Twenty-four variables were associated with unplanned ICU transfer. Sixteen (66.7%) of these variables displayed association in patients with infections and those without infections. These common risk factors included specific comorbidities, physiological responses, organ dysfunctions, and other serious symptoms and signs. Several common risk factors were statistically independent. CONCLUSIONS: The risk factors for unplanned ICU transfer in patients with infections were comparable to those in patients without infections. The risk factors for unplanned ICU transfer included variables from multiple dimensions that could be organized according to the PIRO (predisposition, insult/infection, physiological response, and organ dysfunction) model, providing the basis for the development of a predictive system.

Feasibility of using the predisposition, insult/infection, physiological response, and organ dysfunction concept of sepsis to predict the risk of deterioration and unplanned intensive care unit transfer after emergency department admission.

BACKGROUND: Recognizing patients at risk for deterioration and in need of critical care after emergency department (ED) admission may prevent unplanned intensive care unit (ICU) transfers and decrease the number of deaths in the hospital. The objective of this research was to study if the predisposition, insult, response, and organ dysfunction (PIRO) concept of sepsis can be used to predict the risk of unplanned ICU transfer after ED admission. METHODS: The ICU transfer group included 313 patients with unplanned transfer to the ICU within 48 hours of ED admission, and the control (non-transfer) group included 736 randomly sampled patients who were not transferred to the ICU. Two-thirds of the total 1049 patients in this study were randomly assigned to a derivation group, which was used to develop the PIRO model, and the remaining patients were assigned to a validation group. RESULTS: Independent predictors of deterioration within 48 hours after ED admission were identified by the PIRO concept. PIRO scores were higher in the ICU transfer group than in the non-transfer group, both in the derivation group [median (mean ± SD), 5 (5.7 ± 3.7) vs. 2 (2.5 ± 2.5); p < 0.001], and in the validation group [median (mean ± SD), 6 (6.0 ± 3.4) vs. 2 (2.4 ± 2.6); p < 0.001]. The proportion of ICU transfer patients with a PIRO score of 0-3, 4-6, 7-9, and ≥10 was 14.1%, 46.5%, 57.3%, and 83.8% in the derivation group (p < 0.001) and 12.8%, 37.3%, 68.2%, and 70.0% in the validation group (p < 0.001), respectively. The proportion of inpatient mortality in patients with a PIRO score of 0-3, 4-6, 7-9, and ≥10 was 2.6%, 10.1%, 23.2%, and 45.9% in the derivation group (p < 0.001) and 3.3%, 12.0%, 18.2%, and 20.5% in the validation group (p < 0.001), respectively. CONCLUSION: The PIRO concept of sepsis may be used in undifferentiated medical ED patients as a prediction system for unplanned ICU transfer after admission.

Trends in the use of antiepileptic drugs in Taiwan from 2003 to 2007: a population-based national health insurance study.

PURPOSE: The aim of this study was to evaluate the prevalence of prescription and use of antiepileptic drugs (AEDs) for the treatment of epilepsy and other indications in a nationwide population using a prescription database. MATERIALS AND METHODS: AED prescription data were collected from the National Health Insurance Research Database (NHIRD) in Taiwan for a 5-year period (2003-2007). Patients prescribed AEDs at least two times from 2003 to 2007 were selected for the study from a random sample that included approximately 600,000 people. RESULTS: The prevalence of AED use (per 1000 inhabitants) increased from 12.6 in 2003 to 13.8 in 2007. The prevalence of newer AED use increased progressively from 1.0 in 2003 to 3.8 in 2007, but the prevalence of older AED use decreased during this time (11.6-10). Carbamazepine and valproic acid were the most common AEDs used. Among the newer generation of AEDs, gabapentin was the most frequently used. Newer AEDs were used primarily to treat pain disorders. The primary class of drugs used to treat epileptic disorders was older AEDs. CONCLUSION: An increase in the use of AEDs was observed over a 5-year period in data collected from NHIRD. This might implicate the use of newer compounds at clinical practice not only increased in the treatment of epilepsy, but also in the conditions other than epilepsy especially pain disorders.

Antiepileptic drug utilization in Taiwan: analysis of prescription using National Health Insurance database.

OBJECTIVE: The objective of this study was to analyze and evaluate antiepileptic drug (AED) utilization among adults in Taiwan. METHODS: A random sample of 167,377 patients from the National Health Insurance (NHI) reference database was used. Prescription records were retrieved for all patients prescribed AEDs during 2004. The prescribed daily dose/defined daily dose (PDD/DDD) ratio was used to assess the adequacy of AED dosing. RESULTS: Seventy-one percent (n=518) of patients used only one AED, while 29% (n=212) used more than two AEDs in 2004. For monotherapy, the most frequent regimens included carbamazepine (41.9%), followed by phenytoin (27.3%) and valproic acid (17.8%). For polytherapy, the most commonly used combination was valproic acid and carbamazepine. For adults, the mean PDD/DDD ratio for each AED used for either monotherapy or polytherapy was less than 1.00. Additionally, adult patients treated with more than one AED during 2004 in Taiwan took each drug in higher dose than patients using the same AED in monotherapy. CONCLUSION: In Taiwan antiepileptic drug therapies appear to be still dominated by the first generation drugs. The mean dosages of most antiepileptic drugs were lower than that of WHO suggested.

Prevalence of treated epilepsy in western medicine among the adult population in Taiwan: a study conducted using antiepileptic drug prescription data.

OBJECTIVE: To determine the prevalence of treated epilepsy in the Taiwan in 2004 using prescription data from the National Health Insurance (NHI) research database and to compare this rate with estimates from other prevalence studies. METHODS: A cohort was extracted from a group of antiepileptic drug (AED) users who met two inclusion criteria: (1) use of AED more than once and International Classification of Disease (ICD) codes indicating epilepsy or (2) use of AEDs except clonazepam for more than 90 days without ICD codes of epilepsy. RESULTS: In total, 559 AED users were included. The estimated crude prevalence of AED use was 0.42% of the adult population (0.4% for women and 0.45% for men), and 72% of the cohort (n=400) used only one AED. CONCLUSION: Our estimate of prevalence of treated epilepsy through the NHI system has much larger coverage of population with more credible registration system, and appears to be closer to the truth than two previous small-scaled community-based studies in Taiwan.