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Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.

Background: The Australian Orthopedic Association National Joint Replacement Registry has developed a standardized multi-stage approach to identify prostheses with a higher-than-anticipated rate of revision when comparing a prosthesis of interest to all other prostheses within the same broad class. However, the approach does not adequately differentiate between the conventional and complex design prostheses, and the comparator classes need to be re-evaluated. This study aimed to identify a more relevant comparator to better reflect conventional and complex surgical practices according to the stability design and also explore how the rate of revision estimated in the comparator groups affects the identification of “prosthesis outliers.” Methods: The cumulative percent revision (CPR) was calculated for 640,045 primary total knee replacements (TKRs) undertaken for Osteoarthritis from 1 January 2003 to 31 December 2019. At first, survivorship analyses were undertaken to calculate the rate of revision for primary TKR by stability design. A modified TKR comparator group was developed by excluding the “complex” group of prostheses with fully stabilized and hinged designs. The effectiveness of the modified comparator groups, including cruciate retaining and posterior stabilized designs, was evaluated based on the ability to detect additional prostheses by performing the Australian Orthopedic Association National Joint Replacement Registry standardized method for identifying prosthesis outliers. Results: The modified comparator to include only conventional designs had a 10-year CPR of 5.2% (5.1, 5.3). When the fully stabilized and hinged design groups were combined as a comparator group of complex devices to reflect devices used only for specific purposes in primary TKR, the CPR at 10 year was 10.3% (8.6, 12.0). Conclusions: The use of modified comparator groups led to identifying additional conventional prostheses but fewer complex designs as being at risk and has the potential to improve the early assessment of TKR prostheses.

Objective To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.Design Federated pharmacoepidemiological evaluation in LEGEND-T2DM.Setting 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.Participants 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.Exposure The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.Main outcomes measures The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.Results 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.Conclusions Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Abstract Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Abstract Background Case-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied. Methods We have extended findings by Vines and Farrington to develop a weighting method for the case-crossover study which removes bias from within-subject exposure dependency. Our method calculates the exposure probability at the case period in the case-crossover study which is used to weight the likelihood formulae presented by Greenland in 1999. We simulated data for the population with a disease where most patients receive a cyclic treatment pattern with within-subject exposure dependency but no time trends while some patients stop and start treatment. Finally, the method was applied to real-world data from Japan to study the association between celecoxib and peripheral edema and to study the association between selective serotonin reuptake inhibitor (SSRI) and hip fracture in Australia. Results When the simulated rate ratio of the outcome was 4.0 in a case-crossover study with no time-varying confounder, the proposed weighting method and the Mantel-Haenszel odds ratio reproduced the true rate ratio. When a time-varying confounder existed, the Mantel-Haenszel method was biased but the weighting method was not. When more than one control period was used, standard conditional logistic regression was biased either with or without time-varying confounding and the bias increased (up to 8.7) when the study period was extended. In real-world analysis with a binary exposure variable in Japan and Australia, the point estimate of the odds ratio (around 2.5 for the association between celecoxib and peripheral edema and around 1.6 between SSRI and hip fracture) by our weighting method was equal to the Mantel-Haenszel odds ratio and stable compared with standard conditional logistic regression. Conclusion Case-crossover studies may be biased from within-subject exposure dependency, even without exposure time trends. This bias can be identified by comparing the odds ratio by the Mantel-Haenszel method and that by standard conditional logistic regression. We recommend using our proposed method which removes bias from within-subject exposure dependency and can account for time-varying confounders.

Purpose: Alpha-1 blockers, often used to treat benign prostatic hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storm release. The proposed treatment based on this hypothesis currently lacks support from reliable real-world evidence, however. We leverage an international network of large-scale healthcare databases to generate comprehensive evidence in a transparent and reproducible manner.Methods: In this international cohort study, we deployed electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We assessed association between alpha-1 blocker use and risks of three COVID-19 outcomes—diagnosis, hospitalization, and hospitalization requiring intensive services—using a prevalent-user active-comparator design. We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We pooled database-specific estimates through random effects meta-analysis.Results: Our study overall included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH medications. We observed no significant difference in their risks for any of the COVID-19 outcomes, with our meta-analytic HR estimates being 1.02 (95% CI: 0.92–1.13) for diagnosis, 1.00 (95% CI: 0.89–1.13) for hospitalization, and 1.15 (95% CI: 0.71–1.88) for hospitalization requiring intensive services.Conclusion: We found no evidence of the hypothesized reduction in risks of the COVID-19 outcomes from the prevalent-use of alpha-1 blockers—further research is needed to identify effective therapies for this novel disease.

Abstract Background This a priori statistical analysis plan describes the analysis for CRISTAL. Methods CRISTAL (cluster-randomised, crossover, non-inferiority trial of aspirin compared to low molecular weight heparin for venous thromboembolism prophylaxis in hip or knee arthroplasty, a registry nested study) aims to determine whether aspirin is non-inferior to low molecular weight heparin (LMWH) in preventing symptomatic venous thromboembolism (VTE) following hip arthroplasty (HA) or knee arthroplasty (KA). The study is nested within the Australian Orthopaedic Association National Joint Replacement Registry. The trial was commenced in April 2019 and after an unplanned interim analysis, recruitment was stopped (December 2020), as the stopping rule was met for the primary outcome. The clusters comprised hospitals performing > 250 HA and/or KA procedures per annum, whereby all adults (> 18 years) undergoing HA or KA were recruited. Each hospital was randomised to commence with aspirin, orally, 85–150 mg daily or LMWH (enoxaparin), 40 mg, subcutaneously, daily within 24 h postoperatively, for 35 days after HA and 14 days after KA. Crossover was planned once the registration target was met for the first arm. The primary end point is symptomatic VTE within 90 days. Secondary outcomes include readmission, reoperation, major bleeding and death within 90 days, and reoperation and patient-reported pain, function and health status at 6 months. The main analyses will focus on the primary and secondary outcomes for patients undergoing elective primary total HA and KA for osteoarthritis. The analysis will use an intention-to-treat approach with cluster summary methods to compare treatment arms. As the trial stopped early, analyses will account for incomplete cluster crossover and unequal cluster sizes. Conclusions This paper provides a detailed statistical analysis plan for CRISTAL. Trial registration Australian and New Zealand Clinical Trials Registry ACTRN12618001879257 . Registered on 19/11/2018.

Aim: To examine the incidence and nature of medicine-related problems over time experienced by nursing home residents.Method: We analyzed records collected in the Reducing Medicine-Induced Deterioration and Adverse Events (ReMInDAR) trial. The trial pharmacists provided services to reduce medicine-induced deterioration and adverse reactions for residents every 8-weeks over a year. The problems identified by the pharmacists were documented in reports and subsequently classified independently by research pharmacists using the D.O.C.U.M.E.N.T system. The number and type of problems at each service and time to develop a new problem post first session were assessed. All analyses were performed using R software (Version 4.1.1).Results: The cohort was 115 nursing home residents who received 575 services. In the 12-months, a total of 673 medicine-related problems or symptom reports were identified in 112 residents. Most residents (75%) experienced a new medicine-related problem by the fourth month post the first assessment. After the first session, the proportion of residents with a new medicine-related problem or symptom report declined at each repeated pharmacy session (59% at visit 2 vs. 28% at visit 6, p < 0.01).Conclusion: Residents living in nursing homes frequently experience medicine-related problems. Our results suggest clinical pharmacist services performed every 4-months may have the potential to reduce the medicine-related problems in nursing homes.

Objectives Tapentadol is a µ-opioid agonist, moderate noradrenaline reuptake inhibitor (NRI) and very weak serotonin reuptake inhibitor. The sustained release (SR) formulation is indicated for relief of chronic moderate to severe pain. We examined utilisation trends and concordance with prescribing guidelines of prescription SR tapentadol in Australia between 2014-2021. Approach We used a 10% sample of Australian Pharmaceutical Benefits Scheme dispensing data linked to death records. We assessed incidence, prevalence, and trends in monthly dispensed prescriptions between June 2014 (date of public subsidy) and September 2021. We defined incident users in adults aged ≥18 years as no tapentadol dispensings in the previous year. We assessed characteristics of initiators and patterns of utilization in the year following initiation, including prescription strengths, concurrent dispensing of tapentadol and medicines with known interactions or contraindications, and switching to other opioids. Results 4,883,840 prescriptions of tapentadol were dispensed during the study period. Total monthly dispensed prescriptions increased from 2,040 (June 2014) to 91,300 (September 2021). The lowest strength formulations (50mg, 100mg) comprised nearly three quarters of all dispensed prescriptions (3,628,400; 74.3%). We identified 66,334 new episodes of tapentadol use. Incidence rose from 0.8/1000 population in 2014 to a high of 8.4/1000 in 2019, dropping to 5.5/1000 in 2020. Most people initiating tapentadol were aged 45-84 years (48,464; 73.1%) and female (37,709; 56.9%). More than half (38,157; 57.5%) did not receive a second tapentadol dispensing within 90 days of initiation. 10,868 (16.4%) were dispensed a non-opioid medicine with known interaction on the same day as initiation, and 23,205 (35.0%) were concurrently dispensed tapentadol and other opioids. Conclusion The only approved subsidized indication is tapentadol SR for chronic pain. However, the very high prevalence of single dispensings of tapentadol SR suggests it may be being used off-label for acute pain instead of tapentadol immediate-release; this would be less safe, less effective and also against Australian therapeutic guidelines.

Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early.Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes.Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects.Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.

Regulators and payers play a pivotal role in facilitating timely and affordable access to safe and efficacious medicines. They use evidence generated from randomised clinical trials (RCTs) to support decisions to register and subsidise medicines. However, at the time of registration and subsidy approval, regulators and payers face uncertainty about how RCT outcomes will translate to real-world clinical practice. In response to this situation, medicines policy agencies worldwide have endorsed the use of real-world data (RWD) to derive novel insights on the use and outcomes of prescribed medicines. Recent reforms around data availability and use in Australia are creating unparalleled data access and opportunities for Australian researchers to undertake large-scale research to generate evidence on the safety and effectiveness of medicines in the real world. Highlighting the critical importance of research in this area, Quality Use of Medicines and Medicine Safety was announced as Australia's 10th National Health Priority in 2019. The National Health and Medical Research Council, Medicines Intelligence Centre of Research Excellence (MI-CRE) has been formed to take advantage of the renewed focus on quality use of medicines and the changing data landscape in Australia. It will generate timely research supporting the evidentiary needs of Australian medicines regulators and payers by accelerating the development and translation of real-world evidence on medicines use and outcomes. MI-CRE is developing a coordinated approach to identify, triage and respond to priority questions where there are significant uncertainties about medicines use, (cost)-effectiveness, and/or safety and creating a data ecosystem that will streamline access to Australian data to enable researchers to generate robust evidence in a timely manner. This paper outlines how MI-CRE will partner with policy makers, clinicians, and consumer advocates to leverage real-world data to co-create real-world evidence, to improve quality use of medicines and reduce medicine-related harm.

Abstract Background Researchers developing prediction models are faced with numerous design choices that may impact model performance. One key decision is how to include patients who are lost to follow-up. In this paper we perform a large-scale empirical evaluation investigating the impact of this decision. In addition, we aim to provide guidelines for how to deal with loss to follow-up. Methods We generate a partially synthetic dataset with complete follow-up and simulate loss to follow-up based either on random selection or on selection based on comorbidity. In addition to our synthetic data study we investigate 21 real-world data prediction problems. We compare four simple strategies for developing models when using a cohort design that encounters loss to follow-up. Three strategies employ a binary classifier with data that: (1) include all patients (including those lost to follow-up), (2) exclude all patients lost to follow-up or (3) only exclude patients lost to follow-up who do not have the outcome before being lost to follow-up. The fourth strategy uses a survival model with data that include all patients. We empirically evaluate the discrimination and calibration performance. Results The partially synthetic data study results show that excluding patients who are lost to follow-up can introduce bias when loss to follow-up is common and does not occur at random. However, when loss to follow-up was completely at random, the choice of addressing it had negligible impact on model discrimination performance. Our empirical real-world data results showed that the four design choices investigated to deal with loss to follow-up resulted in comparable performance when the time-at-risk was 1-year but demonstrated differential bias when we looked into 3-year time-at-risk. Removing patients who are lost to follow-up before experiencing the outcome but keeping patients who are lost to follow-up after the outcome can bias a model and should be avoided. Conclusion Based on this study we therefore recommend (1) developing models using data that includes patients that are lost to follow-up and (2) evaluate the discrimination and calibration of models twice: on a test set including patients lost to follow-up and a test set excluding patients lost to follow-up.

Purpose: Inhaled Corticosteroids (ICSs) and oral Leukotriene Receptor Antagonists (LTRAs) are commonly prescribed asthma preventers, however, concerns have been raised as to whether montelukast (LTRA) is associated with an increase in occurrences of neuropsychiatric side effects in children. Our study was conducted to observe prescribing patterns of asthma preventers among paediatric patients specifically focusing on ICSs and LTRAs between Australia and South Korea to see intercountry differences in the use of these medicines.Materials and Methods: The Health Insurance Review and Assessment Paediatric Patients Sample dataset for South Korea and data provided by Services Australia were used in the study. Paediatric patients aged between 3 and 19 with more than one dispensing of an asthma preventer and at least one reliever between 1 Jan 2018 and 31 December 2018 were selected. Prevalence per 1,00,000 persons and standardised prevalence were estimated.Results: A total of 3,58,470 patients (2,04,270 from South Korea and 1,54,200 from Australia) were included in the study. A higher prevalence of ICS-based inhalers was seen in Australia with 80.1% compared to 13.5% in South Korea. In addition, Australia showed a stronger tendency of prescribing high dose ICS-based inhalers compared to South Korea with 22.9% vs. 4.9%. In contrast, use of LTRAs was more prevalent in South Korea with 57.6% while in Australia, montelukast was the only LTRA dispensed at a proportion of 18.9%. Moreover, 29.9% of xanthines which are orally available preventers, were prescribed more frequently in South Korea compared to Australia (0.1%).Conclusion: Australia showed a tendency of prescribing ICS-based preventers whereas South Korea exhibited a preference towards the oral LTRAs. Given the potential risk of neuropsychiatric side effects among paediatric patients with montelukast, reasons for the high use of montelukast in South Korea should be investigated further.

Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance–for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.

Using real-world data and past vaccination data, we conducted a large-scale experiment to quantify bias, precision and timeliness of different study designs to estimate historical background (expected) compared to post-vaccination (observed) rates of safety events for several vaccines. We used negative (not causally related) and positive control outcomes. The latter were synthetically generated true safety signals with incident rate ratios ranging from 1.5 to 4. Observed vs. expected analysis using within-database historical background rates is a sensitive but unspecific method for the identification of potential vaccine safety signals. Despite good discrimination, most analyses showed a tendency to overestimate risks, with 20%-100% type 1 error, but low (0% to 20%) type 2 error in the large databases included in our study. Efforts to improve the comparability of background and post-vaccine rates, including age-sex adjustment and anchoring background rates around a visit, reduced type 1 error and improved precision but residual systematic error persisted. Additionally, empirical calibration dramatically reduced type 1 to nominal but came at the cost of increasing type 2 error.

Introduction Monitoring of joint replacement (JR) data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) has reduced revision rates and improved surgical practice. Outcome assessment post-arthroplasty is limited however, to revision (reoperation) surgery and mortality outcomes. The AOANJRR National Data Linkage project seeks to broaden the scope of outcomes investigation in Australia by linking registry and health administrative datasets. Objectives and Approach Using linked registry and administrative data, the project seeks to describe and quantify national/regional trends and variation in major complications (infection, dislocation, arthrofibrosis, chronic pain, venous thromboembolism, cardiac events), malignancy and health service utilisation (readmissions, emergency encounters and inpatient rehabilitation) following hip, knee and shoulder joint replacement surgery. Evidence will be generated on how these outcomes are associated with and vary according to patient, surgical, implant, hospital and pharmacological factors. As Australia lacks a national identifier, seven linkage agencies are probabilistically linking AOANJRR hip, knee and shoulder replacement data (1999-2017) with 20 datasets. Datasets include government-subsidised health services, procedural and prescription data. Hospital separations and emergency attendance data from Australia’s eight jurisdictions together with national cancer registry and rehabilitation service data are also planned for linkage. Linked data are maintained in a secure remote access computing environment. Results To date, national Medicare Benefits Schedule, Pharmaceutical Benefits Scheme and the Australian Cancer Database data have been linked with >900,000 AOANJRR patients, representing 607.6 million health service records (1999-2018), 467.7 million prescriptions (2002-2018) and 184,000 cancer records, respectively. Remaining linked data will be available in mid-2020. Some initial summary results across a selected range of studies will be presented. Conclusion / Implications This national data-linkage program will identify areas for improvement in joint replacement surgery and modifiable risk factors contributing to poor patient outcomes.

Introduction Patient comobidity at time of primary joint replacement (JR) impacts on outcomes including revision and mortality. Understanding changes in comorbidity profiles is important when assessing change in outcomes over time. Most arthroplasty registries have limited comorbidity information due to their minimum dataset. One approach to obtaining additional comorbidity data is linking registry data with national administrative data. Objectives and Approach Objectives were to quantify pre-operative comorbidity profile of patients undergoing primary total hip replacement (THR) and total knee replacement (TKR) for osteoarthritis. Also, to examine temporal trends in individual comorbidities for THR and TKR patients. National pharmaceutical dispensing data were linked with THR and TKR arthroplasty patients. Medication dispensing histories in 12-months preceding JR (2003-2017) for 237,333 THR and 394,965 TKR patients, were mapped to 47 comorbidity classes using the Rx-Risk-V measure - a pharmacy-based measure of comorbidity. Comorbidity scores were calculated by summing comorbidity categories for individual patients. Trends in comorbidity scores/categories were described, with comorbidity information presented by PBS beneficiary category (concessional/general), stratified by age (

Introduction Increasingly in pharmacoepidemiology, linking is required to enrich analytic data to more accurately define study populations, enable adjustment for confounding, and improve capture of health outcomes. When creating such novel linked datasets, researchers should consider their suitability to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections exist. Additionally, while the RECORD-PE guidelines assist in the reporting of studies using observational health data specific to pharmacoepidemiology, they do not address the unique requirements for transparent evaluation and reporting of the data linkage process. Objectives and Approach We aimed to 1) provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiological research and 2) generate a checklist with specific recommendations to assist researchers in providing clear and transparent assessment of the linkage process. To develop these guidelines, a working group comprised of members of the International Society of harmacoepidemiology was formed. Recommendations were open for comment by Society members and endorsed by the Society. Results Guidance for feasibility assessment was categorized into five domains: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. A checklist for evaluation and reporting of data-linkage processes covered five domains including; (1) data sources; (2) linkage variables; (3) linkage methods; (4) linkage results; and (5) linkage evaluation, including validation and verification of the resulting linked data. Conclusion/Implications Our guidelines for data linkage feasibility assessment and reporting can be used to inform the design of sustainable linked data resources and for transparent communication of linkage processes. Together, these guidelines will help various stakeholders to critically assess the potential for bias in research based on linked data and help generate actionable evidence.

Introduction Infection is a major complication following joint replacement (JR) surgery. However, little data exist on baseline use of antibiotics following primary JR and how use changes with subsequent revision surgery. Objectives & Approach Our study objectives were to describe community use of antibiotics before and after primary total hip replacement (THR) and change in use pre and post revision procedure. Registry data were linked with national medication dispensing data using probabilistic record linkage. Patients with THR for osteoarthritis in a private hospital between 1999 and 2017 were included. Three groups were analysed: patients with primary procedures revised for infection, revised for non-infection reasons and those not revised. Rate of antibiotic dispensing/month was calculated as number of patients dispensed at least one antibiotic in a given month divided by number of patients at-risk. Results There were 102,577 patients included in the non-revised group, 3,156 revised for non-infection and 520 revised for infection. Prior to primary THR, baseline antibiotic dispensing rate was 9-11%/month in all groups. Post-primary rates were similar (10-11%) for non-revised and revised non-infection patients but higher (16-17%) for revised-infection patients. In 1, 6 and 12 months preceding revision for infection, antibiotic use was 55%, 27% and 22%, respectively. For patients revised for non-infection, antibiotic use was 21%, 14%, 13%, respectively. One-month following revision for infection, 82% of patients were dispensed antibiotics, remaining high (38%) at 6-months and 28% at 12-months. In the revision non-infection group, antibiotic use was 48% first month post-surgery, reducing rapidly to 15% at 6-months. Conclusion / Implications Non-revision and revision non-infection patients had similar antibiotic dispensing before and after surgery. Revision infection patients however, maintained higher antibiotic dispensing post-primary, pre and post revision. This may reflect either ongoing infection, need for long-term suppressive therapy or reluctance of treating physicians to terminate treatment.

Introduction To enhance the value of the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) we recently linked these data with administrative datasets including, Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS). Understanding the validity of administrative data is important in establishing the reliability of these data for informing clinical practice and policy. Objectives & Approach The study objective was to determine the validity of MBS data for capturing the occurrence of a joint replacement procedure. Using the AOANJRR procedures as the gold standard, we determined the sensitivity of the MBS data in correctly identifying hip joint replacement procedures. Results Of the 178,047 patients with a single primary total hip replacement occurring in a private hospital setting and recorded in the AOANJRR, 76% had a same-day MBS service claim indicative of that procedure, 2% had MBS procedures within +/- 7 days of the procedure while 18% had no MBS procedure codes indicative of a total hip joint replacement procedure. Of the procedures with no total hip MBS codes, 2% had MBS procedures codes indicating a total knee procedure, 1.7% had MBS procedure codes indicating a revision hip on that day and 13% of procedures had an in-hospital MBS hip anaesthetic administration code claimed on that day. Conclusion / Implications Given the increasing application of MBS data to describe health service use it is important to understand the validity of these data for identifying procedures undertaken in the private hospital setting. Using validated, gold standard data captured by the AOANJRR we identified that MBS data likely underestimate the occurrence of total hip replacement procedures. In addition, some MBS procedure codes are misattributed to other procedure types such as knee procedures and revision procedures.