Predictors of variation in colorectal cancer care and outcomes in New South Wales: a population‐based health data linkage study

ML Jorgensen, JM Young, TA Dobbins… - Medical Journal of …, 2014 - Wiley Online Library
Medical Journal of Australia, 2014Wiley Online Library
Objective: To identify predictors of variation in colorectal cancer care and outcomes in New
South Wales. Design, setting and patients: Multilevel logistic regression analysis using a
linked population‐based dataset based on the records of patients with cancer of the colon,
rectosigmoid junction or rectum who were registered in 2007 and 2008 by the NSW Central
Cancer Registry and treated in 105 hospitals in NSW. Main outcome measures: Six outcome
measures (30‐day mortality, 28‐day emergency readmission, prolonged length of stay, 30 …
Abstract
Objective: To identify predictors of variation in colorectal cancer care and outcomes in New South Wales.
Design, setting and patients: Multilevel logistic regression analysis using a linked population‐based dataset based on the records of patients with cancer of the colon, rectosigmoid junction or rectum who were registered in 2007 and 2008 by the NSW Central Cancer Registry and treated in 105 hospitals in NSW.
Main outcome measures: Six outcome measures (30‐day mortality, 28‐day emergency readmission, prolonged length of stay, 30‐day wound infection, 90‐day venous thromboembolism, 1‐year mortality) and five care process measures (discussion at multidisciplinary team [MDT] meeting, documented cancer stage, recorded pathological stage, treatment within 31 days of decision to treat, treatment within 62 days of referral).
Results: We analysed data for 6890 people. There was wide variation between hospitals in care process measures, even after adjusting for patient and hospital factors. Older adults were less likely to be discussed at an MDT meeting and receive treatment within suggested time frames (all P < 0.001 for colon cancer). Increasing patient age, greater extent of disease, higher Charlson comorbidity score and resection after emergency admission consistently showed strong evidence of an association with poor outcomes. Much of the variation between hospitals in outcome measures was accounted for by patient characteristics.
Conclusions: Patient characteristics should be included in risk‐adjustment models for comparing outcomes between hospitals and for quantifying hospital variation. Further exploration of the reasons why certain hospitals and patients appear to be at risk of poorer care is needed.
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