Despite strong face validity for a variety of patient safety culture strategies, there is limited evidence to support definitive impacts on patient safety climate outcomes. Organisations are advised to consider robust evaluation designs when implementing these potentially resource intensive strategies.

Background Comprehensive national joint replacement registries with well-validated data offer unique opportunities for examining the potential future burden of hip and knee osteoarthritis (OA) at a population level. This study aimed to forecast the burden of primary total knee (TKR) and hip replacements (THR) performed for OA in Australia to the year 2030, and to model the impact of contrasting obesity scenarios on TKR burden. Methods De-identified TKR and THR data for 2003–2013 were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. Population projections and obesity trends were obtained from the Australian Bureau of Statistics, with public and private hospital costs sourced from the National Hospital Cost Data Collection. Procedure rates were projected according to two scenarios: (1) constant rate of surgery from 2013 onwards; and (2) continued growth in surgery rates based on 2003–2013 growth. Sensitivity analyses were used to estimate future TKR burden if: (1) obesity rates continued to increase linearly; or (2) 1–5% of the overweight or obese population attained a normal body mass index. Results Based on recent growth, the incidence of TKR and THR for OA is estimated to rise by 276% and 208%, respectively, by 2030. The total cost to the healthcare system would be $AUD5.32 billion, of which $AUD3.54 billion relates to the private sector. Projected growth in obesity rates would result in 24,707 additional TKRs totalling $AUD521 million. A population-level reduction in obesity could result in up to 8062 fewer procedures and cost savings of up to $AUD170 million. Conclusions If surgery trends for OA continue, Australia faces an unsustainable joint replacement burden by 2030, with significant healthcare budget and health workforce implications. Strategies to reduce national obesity could produce important TKR savings. Electronic supplementary material The online version of this article (10.1186/s12891-019-2411-9) contains supplementary material, which is available to authorized users.

BackgroundPolicy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes.MethodsA narrative review, using structured search methods was undertaken. Key words "data linkage" and Mesh term "medical record linkage" were applied to Medline, EMBASE and CINAHL databases between 1991 and 2007. Abstract inclusion criteria were; the article attempted an empirical evaluation of methodological issues relating to data linkage and reported on patient characteristics, the study design included analysis of matched versus unmatched records, and the report was in English. Included articles were grouped thematically according to patient characteristics that were compared between matched and unmatched records.ResultsThe search identified 1810 articles of which 33 (1.8%) met inclusion criteria. There was marked heterogeneity in study methods and factors investigated. Characteristics that were unevenly distributed among matched and unmatched records were; age (72% of studies), sex (50% of studies), race (64% of studies), geographical/hospital site (93% of studies), socio-economic status (82% of studies) and health status (72% of studies).ConclusionA number of relevant patient or population factors may be associated with incomplete data linkage resulting in systematic bias in reported clinical outcomes. Readers should consider these factors in interpreting the reported results of data linkage studies.

Results. There were 489,900 GP encounters at which OA was managed (rate of 26.4 per 1,000 encounters). OA-hip was managed at a rate of 2.3 per 1,000 encounters (n ‫؍‬ 1,106, 8.6% OA) and OA-knee at a rate of 6.2 per 1,000 (n ‫؍‬ 3,058, 23.7% OA). The encounter management rate per 1,000 for OA-hip was higher among non-metropolitan dwellers (2.85 per 1,000 versus 1.97 per 1,000) and lower for non-English-speaking people (1.53 per 1,000 encounters versus 2.39 per 1,000). The rate for OA-knee was higher for non-English-speaking background (8.50 per 1,000 encounters versus 6.24 per 1,000) and lower among indigenous people (3.16 per 1,000 encounters versus 6.46 per 1,000). Referral to an orthopedic surgeon was the most frequently used nonpharmacologic management (OA-knee 17.4 per 100 contacts and OA-hip 17.7 per 100), followed by advice, education, and counselling. As first-line treatment, medication prescription rates (OA-knee 78.7 per 100 contacts and OA-hip 73.2 per 100) were substantially higher than rates of lifestyle management (OA-knee 20.7 per 100 contacts and OA-hip 14.8 per 100). Conclusion. OA-hip and OA-knee encounters and management differ. Nonpharmacologic treatments as first-line management were low compared with pharmacologic management rates, and surgical referral rates were high. However, lack of longitudinal data limits definitive assessment of appropriateness of care.