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Background: Joint arthroplasty patients have a high prevalence of co-morbidities and this impacts their surgical outcomes. There are different ways to ascertain co-morbidities and appropriate measurement is necessary. The purpose of this study was to: (1) describe the prevalence of co-morbidities in a cohort of total hip arthroplasty (THA) and knee arthroplasty (TKA) patients using two diagnoses-based measures (Charlson and Elixhauser) and one prescription-based measure (RxRisk-V); (2) compare the agreement of co-morbidities amongst the measures.Methods: A cross-sectional study of Australian veterans undergoing THAs (n = 11,848) and TKAs (n = 18,972) between 2001 and 2012 was conducted. Seventeen co-morbidities were identified using the Charlson, 30 using the Elixhauser, and 42 using the RxRisk-V measure. Agreement between co-morbidities was calculated using Kappa (κ) statistics.Results: Combining measures, 64 conditions were identified, of these 28 were only identified using the RxRisk-V, 11 using the Elixhauser, and 2 using the Charlson. The most prevalent conditions was pain treated with anti-inflammatories (58.7% THAs, 55.9% TKAs), pain treated with narcotics (55.0% THAs, 50.9% TKAs), hypertension (56.0% THAs and TKAs), and anticoagulation disorders (53.0% THAs, 48.6% TKAs). Diabetes was the only condition with substantial agreement (all κ > 0.6) amongst all measures. When comparing the diagnoses based algorithms, agreement was high for overlapping conditions (all κ > 0.71).Conclusions: Different measures identified different co-morbidities, provided different estimates for the same co-morbidity, and had different levels of agreement for common co-morbidities. This highlights the importance of understanding co-morbidity measures and using them appropriately in studies and case-mix adjustments analyses. [ABSTRACT FROM AUTHOR]

Background: Relatively little is known about the social distribution of total knee joint replacement (TKR) uptake in Australia. We examine associations between socioeconomic status (SES) and TKR performed for diagnosed osteoarthritis 2003–10 for all Australian males and females aged ≥30 yr. Methods: Data of primary TKR (n = 213,018, 57.4% female) were ascertained from a comprehensive national joint replacement registry. Residential addresses were matched to Australian Census data to identify area-level social disadvantage, and categorised into deciles. Estimated TKR rates were calculated. Poisson regression was used to model the relative risk (RR) of age-adjusted TKR per 1,000py, stratified by sex and SES. Results: A negative relationship was observed between TKR rates and SES deciles. Females had a greater rate of TKR than males. Surgery utilisation was greatest for all adults aged 70-79 yr. In that age group differences in estimated TKR per 1,000py between deciles were greater for 2010 than 2003 (females: 2010 RR 4.32 and 2003 RR 3.67; males: 2010 RR 2.04 and 2003 RR 1.78). Conclusions: Identifying factors associated with TKR utilisation and SES may enhance resource planning and promote surgery utilisation for end-stage osteoarthritis. [ABSTRACT FROM AUTHOR]