Trends in Knee Arthroscopy and Subsequent Arthroplasty
Trends in Knee Arthroscopy and Subsequent Arthroplasty
Data from the New South Wales (NSW) Admitted Patient Data Collection (APDC) pertaining to arthroscopic surgery and TKA between 2000 and 2008 were obtained from the Centre for Health Record Linkage (CHeReL). The APDC contains mandatory data from all hospitals (public and private) and day-procedure centres in NSW. The Australian Classification of Health Intervention procedure codes were used to identify participants undergoing knee arthroscopy or TKA (Table 1). All arthroscopic codes were used except those for ligament reconstruction. Age-specific population estimates at 31 December of each of the years were derived from the Australian Bureau of Statistics, as these dates correspond to the mid-point of each financial year of hospitalisation.
Following data custodian and NSW Population and Health Services Research Ethics Committee approvals, the CHeReL supplied lists of de-identified specific records.
Data in the NSW Admitted Patient Data Collection prior to 1 July 2000 were incomplete, and thus the timeframe of 1st July 2000 to 31st December 2008 (the most recent available data) was chosen for this study. Data retrieved included number of knee arthroscopies and TKA by age, gender, year and hospital status (private vs. public). Data on the indications for knee arthroscopy were found to be incomplete and therefore too unreliable to report. A routine quality check by CHeReL using a 1000-person sample from our data found a false link error rate of 4/1,000 records (0.4%).
Knee arthroscopy rates were calculated per 100 000 of population aged ≥18 years. The rate of TKA within 24 months of knee arthroscopy was generated by calculating the total number of TKA within 24 months of the date of initial knee arthroscopy, divided by total number of knee arthroscopies performed for each year. Negative binomial regression analyses were used to determine the percentage change in rate. The main explanatory variable was year, with age and gender included as covariates.
Negative binomial regression models were applied to determine the percentage change in rate and to accommodate for over-dispersion in the data, with the log of NSW population used as an offset to control for the population changes over time.
Methods
Data from the New South Wales (NSW) Admitted Patient Data Collection (APDC) pertaining to arthroscopic surgery and TKA between 2000 and 2008 were obtained from the Centre for Health Record Linkage (CHeReL). The APDC contains mandatory data from all hospitals (public and private) and day-procedure centres in NSW. The Australian Classification of Health Intervention procedure codes were used to identify participants undergoing knee arthroscopy or TKA (Table 1). All arthroscopic codes were used except those for ligament reconstruction. Age-specific population estimates at 31 December of each of the years were derived from the Australian Bureau of Statistics, as these dates correspond to the mid-point of each financial year of hospitalisation.
Following data custodian and NSW Population and Health Services Research Ethics Committee approvals, the CHeReL supplied lists of de-identified specific records.
Data in the NSW Admitted Patient Data Collection prior to 1 July 2000 were incomplete, and thus the timeframe of 1st July 2000 to 31st December 2008 (the most recent available data) was chosen for this study. Data retrieved included number of knee arthroscopies and TKA by age, gender, year and hospital status (private vs. public). Data on the indications for knee arthroscopy were found to be incomplete and therefore too unreliable to report. A routine quality check by CHeReL using a 1000-person sample from our data found a false link error rate of 4/1,000 records (0.4%).
Knee arthroscopy rates were calculated per 100 000 of population aged ≥18 years. The rate of TKA within 24 months of knee arthroscopy was generated by calculating the total number of TKA within 24 months of the date of initial knee arthroscopy, divided by total number of knee arthroscopies performed for each year. Negative binomial regression analyses were used to determine the percentage change in rate. The main explanatory variable was year, with age and gender included as covariates.
Negative binomial regression models were applied to determine the percentage change in rate and to accommodate for over-dispersion in the data, with the log of NSW population used as an offset to control for the population changes over time.
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