Estimating person-based injury incidence: accuracy of an algorithm to identify readmissions from hospital discharge data

BACKGROUND: Effective use of routinely collected hospital discharge data (HDD) to estimate injury incidence requires a separate identification of new injuries from readmissions for a previous injury. The aim was to determine the accuracy of a computerised algorithm to identify injury readmissions in HDD. METHODS: A random sample of 2000 events ('key events') were selected from the 2006 injury subset of New Zealand's HDD. Discharge histories from 1989 to 2007 were extracted for individuals and manually reviewed by at least two people to determine the 'gold standard' readmission status of each key event. The algorithm relies on four variables: unique national person identifier, dates of injury, admission and discharge. Reviewers were provided with these variables as well as additional discharge information (eg, discharge type and external cause code narrative) recorded in the HDD. Results of the manual review were compared to those obtained from the algorithm. RESULTS: The algorithm assigned 1811 (90.6%) as incident admissions compared to 1800 (90.0%) classified by the gold standard. Agreement was 97.9%, and accuracy measures (sensitivity, specificity, negative predictive value and positive predictive value) ranged from 87% to 99%. No statistically significant differences between readmission assignation by the algorithm and the gold standard were observed by age, nature of injury, external cause of injury or body region. CONCLUSIONS: Any country with electronic HDD could readily identify readmissions and, thus, accurately estimate injury incidence from HDD, providing that a unique person identifier and the date of injury were included in addition to the obligatory dates of admission and discharge.