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Quantification of injury burden using multiple data sources: a longitudinal study.

ABSTRACT: Quantification of injury burden is vital for injury prevention, as it provides a guide for setting policies and priorities. This study generated a set of Hong Kong specific disability weights (DWs) derived from patient experiences and hospital records. Patients were recruited from the Accident and Emergency Department (AED) of three major trauma centers in Hong Kong between September 2014 and December 2015 and subsequently interviewed with a focus on health-related quality of life at most three times over a 12-month period. These patient-reported data were then used for estimation of DWs. The burden of injury was determined using the mortality and inpatient data from 2001 to 2012 and then compared with those reported in the UK Burden of Injury (UKBOI) and global burden of diseases (GBD) studies. There were 22,856 mortality cases and 817,953 morbidity cases caused by injuries, in total contributing to 1,027,641 disability-adjusted life years (DALYs) in the 12-year study timeframe. Estimates for DALYs per 100,000 in Hong Kong amounted to 1192, compared with 2924 in UKBOI and 3459 in GBD. Our findings support the use of multiple data sources including patient-reported data and hospital records for estimation of injury burden.

SUBMITTER: Tung KTS

PROVIDER: S-EPMC7862366 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Quantification of injury burden using multiple data sources: a longitudinal study.

Tung Keith T S KTS Ho Frederick K FK Wong Wilfred H S WHS Wong Rosa S RS Tsui Matthew S H MSH Ho Paul P Kam Chak Wah CW Chan Esther W Y EWY Leung Gilberto K K GKK Chan Ko Ling KL Chow Chun Bong CB Ip Patrick P

Scientific reports 20210204 1

Quantification of injury burden is vital for injury prevention, as it provides a guide for setting policies and priorities. This study generated a set of Hong Kong specific disability weights (DWs) derived from patient experiences and hospital records. Patients were recruited from the Accident and Emergency Department (AED) of three major trauma centers in Hong Kong between September 2014 and December 2015 and subsequently interviewed with a focus on health-related quality of life at most three ...[more]

PMID: 33542517

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Project description:IntroductionTraumatic injury is a leading contributor to the global disease burden in children and adolescents, but methods used to estimate burden do not account for differences in patterns of injury and recovery between children and adults. A lack of empirical data on postinjury disability in children has limited capacity to derive valid disability weights and describe the long-term individual and societal impacts of injury in the early part of life. The aim of this study is to establish valid estimates of the burden of non-fatal injury in children and adolescents.Methods and analysisFive longitudinal studies of paediatric injury survivors <18 years at the time of injury (Australia, Canada, UK and USA) and two whole-of-population linked administrative data paediatric studies (Australia and Wales) will be analysed over a 3-year period commencing 2018. Meta-analysis of deidentified patient-level data (n≈2,600) from five injury-specific longitudinal studies (Victorian State Trauma Registry; Victorian Orthopaedic Trauma Outcomes Registry; UK Burden of Injury; British Columbia Children's Hospital Longitudinal Injury Outcomes; Children's Health After Injury) and >1 million children from two whole-of-population cohorts (South Australian Early Childhood Data Project and Wales Electronic Cohort for Children). Systematic analysis of pooled injury-specific cohort data using a variety of statistical techniques, and parallel analysis of whole-of-population cohorts, will be used to develop estimated disability weights for years lost due to disability, establish appropriate injury classifications and explore factors influencing recovery.Ethics and disseminationThe project was approved by the Monash University Human Research Ethics Committee project number 12 311. Results of this study will be submitted for publication in internationally peer-reviewed journals. The findings from this project have the capacity to improve the validity of paediatric injury burden measurements in future local and global burden of disease studies.

Project description:BackgroundPatient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity.ObjectiveTo develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows.MethodsTen years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost.ResultsFrom the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods.ConclusionsThis paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient's show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.

Dataset Information

Quantification of injury burden using multiple data sources: a longitudinal study.

Publications

Quantification of injury burden using multiple data sources: a longitudinal study.

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