The epidemiology and natural history of depressive disorders in Hong Kong's primary care

Background: Depressive disorders are commonly managed in primary care and family physicians are ideally placed to serve as central providers to these patients. Around the world, the prevalence of depressive disorders in patients presenting to primary care is between 10-20%, of which around 50% remain undiagnosed. In Hong Kong, many barriers exist preventing the optimal treatment and management of patients with depressive disorders. The pathways of care, the long term outcomes and the factors affecting prognosis of these patients requires closer examination. Methods/Design. The aim of this study is to examine the prevalence, incidence and natural history of depressive disorders in primary care and the factors influencing diagnosis, management and outcomes using a cross-sectional study followed by a longitudinal cohort study. Doctors working in primary care settings across Hong Kong have been invited to participate in this study. On one day each month over twelve months, patients in the doctor's waiting room are invited to complete a questionnaire containing items on socio-demography, co-morbidity, family history, previous doctor-diagnosed mental illness, recent mental and other health care utilization, symptoms of depression and health-related quality of life. Following the consultation, the doctors provide information regarding presenting problem, whether they think the patient has depression, and if so, whether the diagnosis is new or old, and the duration of the depressive illness if not a new diagnosis. If the doctor detects a depressive disorder, they are asked to provide information regarding patient management. Patients who consent are followed up by telephone at 2, 12, 26 and 52 weeks. Discussion. The study will provide information regarding cross-sectional prevalence, 12 month incidence, remission rate, outcomes and factors affecting outcomes of patients with depressive disorders in primary care. The epidemiology, outcomes, pathways of care, predictors for prognosis and service needs for primary care patients with depressive disorders will be described and recommendations made for policy and service planning. © 2011 Chin et al; licensee BioMed Central Ltd.published_or_final_versio

THE DEVELOPMENT OF WEB-BASED INTEGRATING MANAGEMENT INFORMATION SYSTEM IN CONSTRUCTION LAB

The Thirteenth East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-13), September 11-13, 2013, Sapporo, Japan

A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies

In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications

A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies

In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications

Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus – a population-based retrospective cohort study

Abstract Background Since diabetes mellitus (DM) is the leading cause of end stage renal disease (ESRD), this study aimed to develop a 5-year ESRD risk prediction model among Chinese patients with Type 2 DM (T2DM) in primary care. Methods A retrospective cohort study was conducted on 149,333 Chinese adult T2DM primary care patients without ESRD in 2010. Using the derivation cohort over a median of 5 years follow-up, the gender-specific models including the interaction effect between predictors and age were derived using Cox regression with a forward stepwise approach. Harrell’s C-statistic and calibration plot were applied to the validation cohort to assess discrimination and calibration of the models. Results Prediction models showed better discrimination with Harrell’s C-statistics of 0.866 (males) and 0.862 (females) and calibration power from the plots than other established models. The predictors included age, usages of anti-hypertensive drugs, anti-glucose drugs, and Hemogloblin A1c, blood pressure, urine albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Specific predictors for male were smoking and presence of sight threatening diabetic retinopathy while additional predictors for female included longer duration of diabetes and quadratic effect of body mass index. Interaction factors with age showed a greater weighting of insulin and urine ACR in younger males, and eGFR in younger females. Conclusions Our newly developed gender-specific models provide a more accurate 5-year ESRD risk predictions for Chinese diabetic primary care patients than other existing models. The models included several modifiable risk factors that clinicians can use to counsel patients, and to target at in the delivery of care to patients