title: Incidence and Variation of Discrepancies in Recording Chronic Conditions in Australian Hospital Administrative Data.
abstract: Diagnostic data routinely collected for hospital admitted patients and used for case-mix adjustment in care provider comparisons and reimbursement are prone to biases. We aim to measure discrepancies, variations and associated factors in recorded chronic morbidities for hospital admitted patients in New South Wales (NSW), Australia. Of all admissions between July 2010 and June 2014 in all NSW public and private acute hospitals, admissions with over 24 hours stay and one or more of the chronic conditions of diabetes, smoking, hepatitis, HIV, and hypertension were included. The incidence of a non-recorded chronic condition in an admission occurring after the first admission with a recorded chronic condition (index admission) was considered as a discrepancy. Poisson models were employed to (i) derive adjusted discrepancy incidence rates (IR) and rate ratios (IRR) accounting for patient, admission, comorbidity and hospital characteristics and (ii) quantify variation in rates among hospitals. The discrepancy incidence rate was highest for hypertension (51% of 262,664 admissions), followed by hepatitis (37% of 12,107), smoking (33% of 548,965), HIV (27% of 1500) and diabetes (19% of 228,687). Adjusted rates for all conditions declined over the four-year period; with the sharpest drop of over 80% for diabetes (47.7% in 2010 vs. 7.3% in 2014), and 20% to 55% for the other conditions. Discrepancies were more common in private hospitals and smaller public hospitals. Inter-hospital differences were responsible for 1% (HIV) to 9.4% (smoking) of variation in adjusted discrepancy incidences, with an increasing trend for diabetes and HIV. Chronic conditions are recorded inconsistently in hospital administrative datasets, and hospitals contribute to the discrepancies. Adjustment for patterns and stratification in risk adjustments; and furthermore longitudinal accumulation of clinical data at patient level, refinement of clinical coding systems and standardisation of comorbidity recording across hospitals would enhance accuracy of datasets and validity of case-mix adjustment.
authors.affiliation: Epidemiology and Health Analytics, Western Sydney Local Health District, Sydney, Australia.
authors.forename: Hassan
authors.initials: H
authors.lastname: Assareh
authors.affiliation: Epidemiology and Health Analytics, Western Sydney Local Health District, Sydney, Australia.
authors.forename: Helen M
authors.initials: HM
authors.lastname: Achat
authors.affiliation: Epidemiology and Health Analytics, Western Sydney Local Health District, Sydney, Australia.
authors.forename: Joanne M
authors.initials: JM
authors.lastname: Stubbs
authors.affiliation: Epidemiology and Health Analytics, Western Sydney Local Health District, Sydney, Australia.
authors.forename: Veth M
authors.initials: VM
authors.lastname: Guevarra
authors.affiliation: Executive Medical Services, Western Sydney Local Health District, Sydney, Australia.
authors.forename: Kim
authors.initials: K
authors.lastname: Hill
mesh_terms: D002908:Chronic Disease / Q000453:epidemiology*; D015897:Comorbidity; D066264:Datasets as Topic; D003953:Diagnosis-Related Groups; D006753:Hospital Records / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D007902:Length of Stay; D019300:Medical Errors; D009517:New South Wales / Q000453:epidemiology; D010343:Patient Admission / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D016012:Poisson Distribution; D011446:Prospective Studies