Standard of care: how can we safeguard it?

published_or_final_versio

Pb contamination and isotopic composition of urban soils in Hong Kong

2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Analysis of heavy metal contaminated soils

Author name used in this publication: Coby S. C. Wong2002-2003 > Academic research: refereed > Publication in refereed journalAuthor’s OriginalPublishe

Urban environmental geochemistry of trace metals

Author name used in this publication: Coby S. C. Wong2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Heavy metal and Pb isotopic compositions of aquatic organisms in the Pearl River Estuary, South China

Author name used in this publication: C. C. M. IpAuthor name used in this publication: X. D. LiAuthor name used in this publication: G. ZhangAuthor name used in this publication: C. S. C. WongAuthor name used in this publication: W. L. Zhang2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Atmospheric deposition of heavy metals in the Pearl River Delta, China

Author name used in this publication: C. S. C. WongAuthor name used in this publication: X. D. LiAuthor name used in this publication: G. ZhangAuthor name used in this publication: S. H. QiAuthor name used in this publication: X. Z. Peng2002-2003 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

The study of metal contamination in urban soils of Hong Kong using a GIS-based approach

Author name used in this publication: Sze-chung Wong2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Heavy metals in agricultural soils of the Pearl River Delta, South China

Author name used in this publication: S. C. WongAuthor name used in this publication: X. D. LiAuthor name used in this publication: G. ZhangAuthor name used in this publication: S. H. QiAuthor name used in this publication: Y. S. Min2001-2002 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Simple non-laboratory-based and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus

This journal suppl. entitled: Abstracts of the 10th International Diabetes Federation–Western Pacific Region Congress and the 6th AASD Scientific MeetingBACKGROUND: Early detection for undiagnosed diabetes mellitus (DM), through routine screening periodically, is critical to prevent or delay severe diabetes-related complications. In order to classify high-risk subjects for DM screening, risk algorithms for undiagnosed DM detection have been richly developed and validated in diverse populations and health care settings. However, the majority of risk algorithms developed within Chinese population were developed and validated in low income setting. Furthermore, there are no nomograms for the use in detecting undiagnosed DM, of which are simple-to-use graphical tool to guide decision-making in both routine clinical practice and community setting. The purpose of this study was to develop simple a nomogram to predict the risk of undiagnosed DM for use in asymptomatic general population, based on non-laboratory-based ...postprin

Simple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus

Background: To develop a simple nomogram which can be used to predict the risk of diabetes mellitus (DM) in asymptomatic non-diabetic general population based on non-laboratory-based and laboratory-based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habit and family history of DM were collected from Chinese non-diabetic subjects aged 18-70. Logistic regression analysis was performed on the data of a random sample of 2518 subjects to construct non-laboratory-based and laboratory-based risk assessment algorithms for the detection of undiagnosed DM; both algorithms were validated on the data of the remaining sample (n=839). Hosmer-Lemeshow χ2 statistic and area under the receiver-operating characteristic curve (AUC) were employed to assess the calibration and discrimination of the different DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose≥7.0mmol/L or 2-hour post-load plasma glucose≥11.1mmol/L after oral glucose tolerance test. The non-laboratory-based risk algorithm, with score ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise and uncontrolled blood pressure; the laboratory-based risk algorithm, with score ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P=0.229 and P=0.483, respectively) and discrimination (AUC: 0.709 and 0.711, respectively) for the detection of undiagnosed DM. The optimal cutoff point on the receiver-operating characteristic curve was 18 for the detection of undiagnosed DM in both algorithms. Conclusions: Simple-to-use nomogram for detecting undiagnosed DM has been developed using the validated non-laboratory-based and laboratory-based risk algorithms.postprin