TY - JOUR
T1 - Estimating the distribution of external causes in hospital data from injury diagnosis
AU - Bhalla, Kavi
AU - Shahraz, Saeid
AU - Naghavi, Mohsen
AU - Lozano, Rafael
AU - Murray, Christopher
N1 - Funding Information:
This work was supported by a grant from The World Bank Global Road Safety Facility. The two hospital discharge databases (IMSS and SAEH) were provided by the Mexican Ministry of Health.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008/11
Y1 - 2008/11
N2 - Hospital discharge datasets are a key source for estimating the incidence of non-fatal injuries. While hospital records usually document injury diagnosis (e.g. traumatic brain injury, femur fracture, etc.) accurately, they often contain poor quality information on external causes (e.g. road traffic crashes, falls, fires, etc.), if such data is recorded at all. However, estimating incidence by external causes is essential for designing effective prevention strategies. Thus, we developed a method for estimating the number of hospital admissions due to each external cause based on injury diagnosis. We start with a prior probability distribution of external causes for each case (based on victim age and sex) and use Bayesian inference to update the probabilities based on the victim's injury diagnoses. We validate the method on a trial dataset in which both external causes and injury diagnoses are known and demonstrate application to two problems: redistribution of cases classified to ill-defined external causes in one hospital data system; and, estimation of external causes in another hospital data system that only records nature of injuries. In comparison with age-sex proportional distribution (the method usually employed), we found the Bayesian method to be a significant improvement for generating estimates of incidence for many external causes (e.g. fires, drownings, poisonings). But the method, performed poorly in distinguishing between falls and road traffic injuries, both of which are characterized by similar injury codes in our datasets. While such stop gap methods can help derive additional information, hospitals need to incorporate accurate external cause coding in routine record keeping.
AB - Hospital discharge datasets are a key source for estimating the incidence of non-fatal injuries. While hospital records usually document injury diagnosis (e.g. traumatic brain injury, femur fracture, etc.) accurately, they often contain poor quality information on external causes (e.g. road traffic crashes, falls, fires, etc.), if such data is recorded at all. However, estimating incidence by external causes is essential for designing effective prevention strategies. Thus, we developed a method for estimating the number of hospital admissions due to each external cause based on injury diagnosis. We start with a prior probability distribution of external causes for each case (based on victim age and sex) and use Bayesian inference to update the probabilities based on the victim's injury diagnoses. We validate the method on a trial dataset in which both external causes and injury diagnoses are known and demonstrate application to two problems: redistribution of cases classified to ill-defined external causes in one hospital data system; and, estimation of external causes in another hospital data system that only records nature of injuries. In comparison with age-sex proportional distribution (the method usually employed), we found the Bayesian method to be a significant improvement for generating estimates of incidence for many external causes (e.g. fires, drownings, poisonings). But the method, performed poorly in distinguishing between falls and road traffic injuries, both of which are characterized by similar injury codes in our datasets. While such stop gap methods can help derive additional information, hospitals need to incorporate accurate external cause coding in routine record keeping.
KW - Bayesian inference
KW - Hospital discharge
KW - ICD
KW - Injury surveillance
KW - Missing data
KW - Non-fatal injuries
UR - http://www.scopus.com/inward/record.url?scp=54949127860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=54949127860&partnerID=8YFLogxK
U2 - 10.1016/j.aap.2008.07.002
DO - 10.1016/j.aap.2008.07.002
M3 - Article
C2 - 19068282
AN - SCOPUS:54949127860
SN - 0001-4575
VL - 40
SP - 1822
EP - 1829
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
IS - 6
ER -