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Background with rationale Traumatic spinal injuries (TSI) include column fractures, spinal cord injury, or both. They are among the most severe injuries with potential long-term physical, psychological, and social consequences. Primary causes of TSIs are falls and motor vehicle crashes, however, mental illness and substance use are known to significantly increase all injury risk. Injury is also known to increase risks of mental deterioration and physical complications including self-harm and self-neglect. Main Aim We aimed to identify comorbid mental illness and/or substance use at incident TSI, quantifying associated costs and health service management of these inequities. Methods NSW record-linkage administrative data analyses (2013-2016) will determine accurate prevalence of mental illness and/or substance use disorder among all patients who sustained acute TSI during the study period. Using recurrent event analyses, we will estimate the contributions of mental illness and/or substance use disorder on the impact on hospital acquired complications (HAC), length of stay and costs; assessing records for social work and/or psychologist consultation. Results 13,489 individuals were hospitalised with acute TSI; 21% had either mental health and/or substance use diagnoses; 8.7% had both. These patients were more likely to have experienced falls or intentional self-harm, be male and have multiple comorbidity. Acute care stay and costs were on average twice that of patients with TSI without mental health and/or substance use diagnoses; additionally they were more than twice as likely to experience HACs. Only 56% of TSI patients with these comorbid conditions in the context of TSI, had documented social work or psychologist consultation. Conclusion Patients with mental illness and/or substance use disorder, experience significant health disparities that require concerted health system attention that should begin early in acute care.

Objectives • To present an efficient privacy-protecting model (PRIDAL) for linking routine and identifiable patient-reported research data. • To describe the application of the PRIDAL model with within a major portfolio trial linking a range of health and demographic datasets pertaining to >230,000 patients, alongside >2,300 questionnaires. • To summarise the lessons learned from the application of the PRIDAL model. Approach The rise in availability, quality and use of routine health data has resulted in well-developed methods for anonymised linkage of data from multiple sources. However methods for combining identifiable data (e.g. patient-reported questionnaires) with routine anonymised data are not yet tried and tested. Linking these data presents opportunities to improve the feasibility and effectiveness of observational and experimental studies, but emerging data linkage processes must address the appropriate balance between data security and usability. The Process for Routine and Identifiable Data Linkage (PRIDAL) was devised to efficiently link routine hospital data and patient-reported quality of life and quality of care questionnaire data as part of the PRISMATIC trial (http://www.trialsjournal.com/content/14/1/301). This is a mixed methods progressive cluster randomised trial of the efficacy of an emergency admission risk prediction tool in primary care, funded by the NIHR HS&DR programme. PRIDAL was conceived by a group of specialists in e-trials, health informatics, information governance and process mapping who reviewed data sources, flows, owners, and security to develop a practical and intuitive process model. Results We will present the PRIDAL process model and describe its application in relation to the PRISMATIC study. We will demonstrate that the model achieves high data matching rates (>99%), and consider the lessons learnt from its application. Conclusions The linking of routine health and patient self-reported data presents a valuable opportunity in health research, but clear, replicable models, are needed to support ethical and practical data linkage. We present the fully tested PRIDAL model as a potential solution.

BackgroundLinkage of demographic, health, and developmental administrative data can enrich population-based surveillance and research on developmental and educational outcomes. Transparency of the record linkage process and results are required to assess potential biases. ObjectivesTo describe the approach used to link records of kindergarten children from the Early Development Instrument (EDI) in Ontario to health administrative data and test differences in characteristics of children by linkage status. We demonstrate how socio-demographic and medical risk factors amass in their contribution to early developmental vulnerability and test the concordance of health diagnoses in both the EDI and health datasets of linked records. MethodsChildren with records in the 2015 EDI cycle were deterministically linked to a population registry in Ontario, Canada. We compared sociodemographic and developmental vulnerability data between linked and unlinked records. Among linked records, we examined the contribution of medical and social risk factors obtained from health administrative data to developmental vulnerability identified in the EDI using descriptive analyses. ResultsOf 135,937 EDI records, 106,217 (78.1%) linked deterministically to a child in the Ontario health registry using birth date, sex, and postal code. The linked cohort was representative of children who completed the EDI in age, sex, rural residence, immigrant status, language, and special needs status. Linked data underestimated children living in the lowest neighbourhood income quintile (standardized difference [SD] 0.10) and with higher vulnerability in physical health and well-being (SD 0.11) , social competence (SD 0.10), and language and cognitive development (SD 0.12). Analysis of linked records showed developmental vulnerability is sometimes greater in children with social risk factors compared to those with medical risk factors. Common childhood conditions with records in health data were infrequently recorded in EDI records. ConclusionsLinkage of early developmental and health administrative data, in the absence of a single unique identifier, can be successful with few systematic biases introduced. Cross-sectoral linkages can highlight the relative contribution of medical and social risk factors to developmental vulnerability and poor school achievement.

Springer, November 2020 ISBN-10: 3030597059, ISBN‑13: 978-3030597054, Price: £87.50 Key message from the preface A key message of this book is that any database that contains sensitive information about individuals in plaintext can be vulnerable to data breaches and attacks by adversaries, both external and internal to an organisation, as well as unintentional revealing or publication due to human or technical mishaps. Encoding personal sensitive information using the techniques and methods discussed in this book can significantly reduce the risks of sensitive data being breached or revealed. Overview summary from the back cover This book provides modern technical answers to the legal requirements of pseudonymisation as recommended by privacy legislation. It covers topics such as modern regulatory frameworks for sharing and linking sensitive information, concepts and algorithms for privacy-preserving record linkage and their computational aspects, practical considerations such as dealing with dirty and missing data, as well as privacy, risk, and performance assessment measures. Existing techniques for privacy-preserving record linkage are evaluated empirically and real-world application examples that scale to population sizes are described. The book also includes pointers to freely available software tools, benchmark data sets, and tools to generate synthetic data that can be used to test and evaluate linkage techniques. This book consists of fourteen chapters grouped into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers methods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases. The appendices provide pointers and describe freely available, open-source software systems that allow the linkage of sensitive data, and provide further details about the evaluations presented. A companion website at https://dmm.anu.edu.au/lsdbook2020 provides additional material and Python programs used in the book. Who this book is for This book is mainly written for applied scientists, researchers, and advanced practitioners in governments, industry, and universities who are concerned with developing, implementing, and deploying systems and tools to share sensitive information in administrative, commercial, or medical databases. Synopsis by part and chapter Part I: Introduction This part begins by setting the scene on the increase in data linkage and poses the question of why data should be linked at all. It introduces sources of personal data, and defines sensitive data and what constitute direct and indirect identifiers. It provides several case studies from areas including finance, law and health, to illustrate the benefits and importance of data linkage, whilst respecting data ethics. Chapter 2 focuses on regulatory frameworks and ethical principles of research. It describes the regulatory landscapes in jurisdictions including the EU and UK, the US, Australia, and Switzerland. It touches on statistical disclosure control (SDC) and how regulatory frameworks both protect individuals and make novel techniques to link sensitive data necessary. Chapter 3 provides a background to linking sensitive data, including a short history and how data can be linked across databases. It raises challenges such as issues in data quality and discusses evaluation methods for linkage quality and complexity. Towards the end of the chapter, it defines privacy-preserving record linkage (PPRL). Part II: Methods and Techniques This part begins with a consideration of conceptual protocols for private information sharing along with the roles of different participants in the linkage process. It explains the separation principle, and two-, three- and multi-party linkage protocols and describes various adversarial attack models, such as the malicious and the honest-but-curious models. Chapter 5 focuses on assessing privacy and risks and measuring risks when linking sensitive data. It defines and characterises attacks on sensitive data, such as linkage, dictionary, frequency, and collusion attacks with a consideration of motivations, costs and gains. It introduces SDC techniques and their evaluation. Chapter 6 deals with the building blocks used in protocols for linking sensitive data. Among those described are random number generation, hashing techniques, anonymisation and pseudonymisation, encryption and secure multiparty computation, along with guidance on choosing suitable building blocks. Chapter 7 is about encoding and comparing sensitive values. It begins with a taxonomy of linkage techniques namely, privacy, linkage, theoretical, evaluation, and practical aspects. It moves on to describe techniques such as, phonetic encoding, hashing, and differential privacy. Chapter 8 focuses specifically on Bloom filter based encoding methods with hashing and encoding techniques for textual and numerical data, along with guidance on choosing suitable settings for Bloom filter encoding. Chapter 9 discusses attacks and hardening techniques for Bloom filter encoding. Chapter 10 finishes this part of the book with considerations of computational demand and efficiency, describing blocking and indexing techniques and approaches that make use of modern parallel and distributed computing platforms. Part III: Practical Aspects, Evaluation, and Applications This part of the book focuses on practical considerations and starts by highlighting some of the challenges in working with data. It then takes a more in-depth look at data-related issues, such as dirty data, missing values, bias, and lack of ground truth. It highlights the implications of false positive and false negative matches. Chapter 12 provides an empirical evaluation of selected Bloom filter based encoding and hardening techniques. It shows how sensitive databases can be linked using PPRL techniques and how the linkage can be assessed on the dimensions of linkage quality, scalability, and privacy by means of an evaluation framework. The final chapter in this part of the book describes real-world applications of PPRL techniques in a range of countries where differing privacy frameworks and legislation make the use of such methods necessary, or just where PPRL was chosen to make linkage more secure. Part IV: Outlook This final part of the book looks to future challenges and directions – practical and conceptual. These include discussions on the development of frameworks to enable comparative evaluation of linkage techniques, benchmarking, linking sensitive data in a cloud environment, and how to assess linkage quality and completeness when only encoded/encrypted records are available. It also highlights challenges and opportunities in linking emerging data types, such as biometric and genetic data. The book also has two appendices: A) Software and datasets; B) Details of the empirical evaluation. It has an extensive glossary on data matching and linkage, over 650 references, and an index. In total the book has over 450 pages.

Introduction The ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Current methods for prognostic prediction modelling are insufficient for the estimation of risk for multiple outcomes, as they do not properly capture the dependence that exists between outcomes. Objectives We developed a multivariate prognostic prediction model for the 5-year risk of diabetes, hypertension, and osteoarthritis that quantifies and accounts for the dependence between each disease using a copula-based model. Methods We used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2009 onwards, a collection of electronic medical records submitted by participating primary care practitioners across Canada. We identified patients 18 years and older without all three outcome diseases and observed any incident diabetes, osteoarthritis, or hypertension within 5-years, resulting in a large retrospective cohort for model development and internal validation (n=425228). First, we quantified the dependence between outcomes using unadjusted and adjusted ϕ coefficients. We then estimated a copula-based model to quantify the non-linear dependence between outcomes that can be used to derive risk estimates for each outcome, accounting for the observed dependence. Copula-based models are defined by univariate models for each outcome and a dependence function, specified by the parameter θ. Logistic regression was used for the univariate models and the Frank copula was selected as the dependence function. Results All outcome pairs demonstrated statistically significant dependence that was reduced after adjusting for covariates. The copula-based model yielded statistically significant θ parameters in agreement with the adjusted and unadjusted ϕ coefficients. Our copula-based model can effectively be used to estimate trivariate probabilities. Discussion Quantitative estimates of multimorbidity risk inform discussions between patients and their primary care practitioners around prevention in an effort to reduce the incidence of multimorbidity.

Background: Healthcare systems worldwide have adopted the electronic medical record (EMR). EMRs are an efficient method of interprofessional communication, and can improve data availability for secondary research purposes. The discharge summary (DS) is a crucial document for both interprofessional communication, and coding of data for research purposes. We aimed to assess the completeness of our EMRs by assessing the presence of a DS in the EMR. Additionally, we evaluated the presence of indicators for a missing DS. Method: A retrospective chart review was conducted on 3,011 inpatient charts from 3 hospitals in Calgary, Alberta Canada. 893 charts were missing an electronic DS. A 10% sample was drawn to assess for presence of a paper DS. A list of variables was compiled to assess for association between patient and hospital characteristics, patient comorbidities, and the absence of an electronic DS. A Chi-square test, Fisher’s test and logistic regression were conducted to assess for associations. Results: The univariate analyses showed that age, being a surgical patient, a Charlson Comorbidity Index (CCI) of

IntroductionHospital administrative data is a valuable source to measure myocardial infarction (MI) rates. However, admission counts are susceptible to over-inflation if the patient is transferred multiple times during a single episode of care, and variables denoting transfers may not be reliable. To obtain an accurate number of events, hospital transfers need to be correctly identified. Objectives and ApproachWe assessed multivariable logistic regression and various machine-learning models to predict transfers in hospital administrative data. Using Western Australian linked hospital data, we identified records from 2000-2016 with a principal discharge diagnosis of MI. Our standard method to compare against was a 24-hour look-back to identify a transfer using just admission and separation dates from the current and previous records for the same patient. Multivariable logistic regression and decision trees with various boosting algorithms were used to predict if a single record was a transfer, using variables recorded in the admission (e.g. age, sex, type of hospital, admitted from, emergency/elective admission). The performance of each model was calculated using metrics including area under the curve (AUC). ResultsRecords in the training, validation and testing samples had similar characteristics: mean age=68.9 years, 66% were male and 58% admitted to tertiary hospitals. Gradient Boosting Decision Tree (AUC=0.887, 95%CI: 0.886-0.887) outperformed multivariable logistic regression (AUC=0.875; 95% CI: 0.869-0.881) and random forest models (AUC=0.859; 95% CI: 0.853-0.865). Conclusion / ImplicationsMultivariable logistic regression and machine-learning models are able to identify transfers in a single record from existing variables. They can be used in unlinked hospital administrative data where records belonging to the same patient cannot be identified.

IntroductionHCTZ is first-line treatment for hypertension and among the most commonly used medicines in Australia. Recent evidence suggests increased risks of lip and skin cancers in association with HCTZ use. Objectives and ApproachTo determine the risk of SCC of the lip and melanoma among people prescribed HCTZ in Australia we conducted a case-control study nested within a cohort of Department of Veterans’ Affairs clients 65 years and older in 2004-2015. We identified incident cases of SCC of the lip (lip cancer) and of cutaneous melanoma (malignant melanoma), each matched by sex and age with up to 20 controls through risk-set sampling. We ascertained HCTZ use from dispensing data and classified use according to ever/never use and cumulative use. We estimated odds ratios (ORs) associating HCTZ use with lip cancer and malignant melanoma using conditional logistic regression, adjusting for predefined confounders obtained from dispensing and hospitalisation data. ResultsFor lip cancer (45 cases) ever-use of HCTZ yielded an OR of 2.6 (95%CI: 1.4–5.0) and high HCTZ use (>25,000mg) an OR of 4.7 (1.61–13.7). For malignant melanoma (659 cases) ever-use of HCTZ resulted in an OR of 1.2 (1.0–1.5) and high HCTZ use in an OR of 1.2 (0.8–1.8). Conclusion / ImplicationsOur study provides further evidence that the photosensitising properties of HCTZ may promote SCC carcinogenesis, and possibly melanoma, in susceptible sun-exposed tissues. Our findings are the first from the Australian population—already at elevated risk of developing skin cancer—and add to the growing body of data supporting the need for skin cancer prevention advice and behaviours, and potentially heightened surveillance, for individuals prescribed this medication.

IntroductionNotwithstanding Canada’s exceptional longitudinal health data and research centres with extensive experience transforming data into knowledge, many Canadian studies based on linked administrative data have focused on a single province or territory. Health Data Research Network Canada (HDRN Canada), a new not-for-profit corporation, will bring together major national, provincial and territorial health data stewards from across Canada. HDRN Canada’s first initiative is the $81 million SPOR Canadian Data Platform funded under the Canadian Institutes of Health Research Strategy for Patient-Oriented Research (SPOR). Objectives and ApproachHDRN Canada is a distributed network through which individual data-holding centres work together to (i) create a single portal and support system for researchers requesting multi-jurisdictional data, (ii) harmonize and validate case definitions and key analytic variables across jurisdictions, (iii) expand the sources and types of data linkages, (iv) develop technological infrastructure to improve data access and collection, (v) create supports for advanced analytics and (vi) establish strong partnerships with patients, the public and with Indigenous communities. We will share our experiences and gather international feedback on our network and its goals from symposium participants. ResultsIn January 2020, HDRN Canada launched its Data Access Support Hub (DASH) which includes an inventory listing over 380 datasets, information about more than 120 algorithms and a repository of requirements and processes for accessing data. HDRN Canada is receiving requests for multi-province research studies that would be challenging to conduct without HDRN Canada. Conclusion / ImplicationsThus far, HDRN Canada services and tools have been developed primarily for Canadian researchers but HDRN Canada can also serve as a prompt for an international discussion about what has/has not worked in terms of multi-jurisdictional research data infrastructure. It can also present an opportunity for the development of metadata, standards and common approaches that support more multi-country research.

IntroductionCancer deaths are a major contributor to premature mortality in people with mental health conditions. Some cancers occur more often in people with mental health conditions because of increased risk factors. However, most premature cancer mortality in people with mental health conditions arises from increased cancer case fatality rates due to health care related factors. While there is substantial evidence that a problem exists, further evidence is needed to support effective action and the translation of research findings into better policy, services and care. Objectives and ApproachThe NSW Mental Health Living Longer program involves population-wide data linkage that combines records from nine NSW data collections. Our collection includes over 120 million records for more than nine million people. This presentation focuses on the use of linked data to develop indicators to support reporting on premature breast and cervical cancer mortality for women living with mental illness. These indicators will be used to identify variation in care, assess areas for targeted intervention, and evaluate the effectiveness of research translation into more effective care. ResultsThis work is ongoing and will be finalised by August 2020. We will use regression techniques to examine predictors of participation in breast and cervical cancer screening for women who use mental health services in NSW. These results will be used to assess geographical variation in risk-adjusted screening participation rates. We will also present methods and results for measuring incidence and stage at presentation, as well as 5 year survival for women who use mental health services in NSW. Conclusion / ImplicationsIf cancer survival is a key measure of the effectiveness of healthcare systems, then reduced survival in people with mental health problems reflects less effective health care. Improving screening and treatment services is likely to be the most important strategy for reducing the cancer mortality gap for women with mental illness.

IntroductionStats NZ maintains and provides access to the Integrated Data Infrastructure (IDI). This is a critical tool in the government's efforts to reduce poverty, improve health, and support a growing, inclusive economy. Insights are used at all levels of government decision making. Objectives and ApproachSince the IDI prototype was created in 2011, it has grown rapidly. The use cases for the IDI continue to develop, with more complex projects that extend the boundaries of what might be considered traditional research. The Ministry of Education is now running a key school funding model, the Equity Index, out of the IDI. We are seeing an increased interest for the IDI to support fast responses to policy questions. Statistical production areas in and outside Stats NZ increasingly need administrative data for production, and the IDI is the best source of relevant linked administrative data. ResultsStats NZ has received one-off funding to continue sustainable expansion of the IDI and improve the data and services it provides. This ‘IDI 2’ project will enhance the IDI, making it easier, quicker and more stable for our customers to engage with data. We have focussed on strengthening, and futureproofing, our core offering. The IDI 2 project is currently looking to replace the systems that are used to build our databases and streamline production to provide the technical capability to cope with a much higher volume of data. Conclusion / ImplicationsWhile the IDI 2 project looks to shore up the infrastructure and lay a platform for future development, Stats NZ seeks to undertake customer focussed improvements to be responsive to other customer needs. There are opportunities to grow the integrated data community and provide more ways for researchers to connect with each other.

IntroductionSolid organ transplant recipients are at risk of infections, which may be either derived through transplantation or acquired later. Blood-borne viruses (BBV) are a particular concern for donor-derived transmissions. There is an increasing emphasis on biovigilance – monitoring the safety of donated organs. However, systematic surveillance to distinguish donor-transmitted infection from de novo post-transplant infection is challenging. Additional information can be obtained through linkage of administrative health data. Objectives and ApproachWe aimed to identify donor-transmitted and de novo BBV infections among organ transplant recipients. We conducted a cohort study of all solid organ donor-recipient pairs in New South Wales, Australia, 2000-2015. Donor and recipient BBV infections were identified by linking transplant registries with administrative health data. Proven/probable donor-transmissions were identified among new recipient infections within 12 months of transplant, classified according to an international algorithm. All other new BBV infections were classified as de novo infections. ResultsAmong 2,120 organ donors, 73 had a BBV infection (11/73 active, 62/73 past). Donors with BBV donated to 176 recipients, of whom 24/176 had the same BBV as their donor, and 152/176 did not; these 152 recipients were at risk of donor-transmission. Among those at risk, there were 3/152 proven/probable BBV transmissions (1 hepatitis C, 2 hepatitis B [HBV]) and 149/152 recipients with non-transmissions. All donor-transmissions were previously recognised by donation services, and were from donors with known BBV. There were no deaths from transmissions. There were 70 recipients with de novo BBV; 2/70 died from new HBV. Conclusion / ImplicationsThis work confirms the safety of Australian organ donation, with no unrecognised BBV transmissions and many non-transmissions from donors with BBV. This may support increasing targeted donation from donors with BBV. However, de novo BBV infections were substantial and preventable. Data-linkage may be a useful adjunct to current biovigilance systems.

IntroductionDemand continues to grow for accessible and large scale linked data assets to answer complex cross-sector, and cross-jurisdiction research questions. To meet this demand, a number of Multi-source, Enduring Linked Data Assets (MELDAs) have emerged including the National Integrated Health Service Infrastructure (NIHSI), National Disability Data Asset (NDDA) and Multi-Agency Data Integration Project (MADIP). Using these MELDAs has proven much more efficient than project-specific linkages, and provides consistent national data assets for multiple uses. However, the development of these assets raises new challenges, including complex data models, governance, and access arrangements, and new approaches to analysis. Objectives and ApproachThrough developing the NIHSI Analytical Asset in collaboration with state/territory and Federal Government partners, the AIHW has identified challenges in traditional linkage approaches, which require innovative approaches to ensure high quality linkage. As AIHW commences scoping on new MELDAs, we are taking lessons from building the NIHSI and applying them to future design. ResultsAIHW’s development of MELDAs across jurisdictions and portfolios provides new learnings on how to address advanced real world data integration issues. This review will focus on lessons learnt at the Australian Institute of Health and Welfare (AIHW) working with new data sharing arrangements, applications of technologies and innovative approaches to streamline MELDA processes. Conclusion / ImplicationsThe learnings from the AIHW development of MELDAs will assist others developing enduring assets to establish effective sharing arrangements, governance and technical solutions to ensure efficient management. These learnings will save time and resources, and prompt further discussion on a gold standard for building MELDAs moving forwards.

IntroductionLinkage of Federal Government data in Australia is conducted primarily through Accredited Integrating Authorities (AIAs). These agencies hold different dataset from Commonwealth and state/territory government agencies. Historically, linkage projects involving data held by different AIAs has been inefficient, requiring the transfer of identifiable data between agencies, and relinking data that have already been linked by another agency. Objectives and ApproachTwo AIAs (the AIHW and ABS) have developed a system of interoperable linkage spines to address this issue. By using common datasets as a base, the agencies have improved the efficiency and security of linkage projects. This process was developed through an analysis of spine datasets, and two test projects to share data between the agencies. ResultsThe two test projects were successfully able to link cross-portfolio and cross-jurisdictional data without the need to share additional identifying information between the AIAs. Preliminary results suggest a high linkage rate from this process, and work is underway to quantify the linkage quality compared to traditional linkage methodologies. The ABS and AIHW are also investigating the implications for linkage quality as more datasets are included in the agencies’ linkage spines. Conclusion / ImplicationsThe success of this project will increase the efficiency of cross-jurisdictional and cross-portfolio linkage in Australia. It will also allow specialised AIAs to work on datasets where they have specific expertise, and feed these into broader projects. This is expected to have an additional impact on public trust in the linkage system, by minimising the sharing of personally identifiable information while still maintaining high quality linkage.

IntroductionCardiovascular disease (CVD) is a leading contributor to disease burden worldwide. Older people hospitalized with CVD might experience significant decline in physical function and loss of independence. Objectives and ApproachThe aim of this study was to investigate the use of community aged care (CAC) and permanent residential aged care (PRAC) services 3 months, 6 months and 12 months after hospital admission for myocardial infarction (MI), stroke and congestive heart failure (CHF). Survey data from the 45 and Up Study (2006-09) for 266,942 people aged 45+ was linked with records for hospital stays (APDC), aged care service use (NACDC), and deaths (RBDM) for 2006-14 through CHeReL and AIHW. Relative risks of using aged care (CAC or PRAC) after MI, stroke or CHF hospitalization were estimated using Cox regression. We described and visualized sequences of health service states (none, re-hospitalization, CAC, PRAC, death) after the index hospitalization. ResultsCompared with people without MI, people hospitalized with a principal diagnosis of MI (multivariable-adjusted HR:1.11, 95%CI:1.04-1.18), stroke (HR:1.52, 95%CI:1.43-1.61) and CHF (HR:1.12, 95%CI:1.06-1.19) were more likely to use CAC within 3 months of the hospital discharge. Likewise, people with MI (HR:1.16, 95%CI:1.03-1.29), stroke (HR:2.81, 95%CI:2.58-3.05) and CHF (HR:1.36, 95CI:1.24-1.49) were more likely to enter PRAC within 3 months of discharge. Similar findings were observed for 6 months and 12 months. MI, stroke and CHF patients were more likely to die but less likely to be re-hospitalized after the first 3 months. The number use of CAC and PRAC remains unchanged over 12 months. While this study provides a broadly representative sample of the older population, participants may be healthier than the general population. Conclusion / ImplicationsCVD increases use of community and residential care services. Coordination of cardiac and stroke rehabilitation is warranted to maximize ageing in place.

IntroductionDescribing out-of-pocket (OOP) healthcare costs in relation to ability to pay requires multiple linked data sources not previously available. Current estimates of the progressivity of OOP healthcare costs in Australia are based on self-report surveys. Using newly linked Census to administrative income and medical claims data, we aimed to quantify, for the first time, the progressivity of OOP costs for government-subsidised out-of-hospital healthcare in Australia. Objectives and ApproachWe used Australian Census 2011 linked to Personal Income Tax (PIT), Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data compiled through the Multi-Agency Data Integration Project (MADIP). Personal disposable income was estimated using a combination of PIT data and Census self-reported income, and aggregated across the household to estimate equivalised household income. We estimated annual MBS (out-of-hospital only) and PBS OOP costs as a proportion of equivalised household income, and assessed progressivity by reporting this for each income decile and computing a Kakwani Index. ResultsWe will present findings on progressivity overall, and separately by age, sex and location (incomplete at time of abstract submission). Conclusion / ImplicationsOur study will present one measure regarding the equity of healthcare costs, and help to identify vulnerable or at-risk groups. These findings may inform policy changes on equity in the financing of healthcare. Newly linked data from the MADIP can be used to relate healthcare costs to ability to pay.

IntroductionRisk of traumatic injury is increased in individuals with mental illness, substance use disorder and dual diagnosis (mental disorders); these conditions will pre-exist among individuals hospitalised with acute traumatic spinal injury (TSI). Although early intervention can improve outcomes for people who experience mental disorders or TSI, the incidence, management, and cost of this often complex comorbid health profile is not sufficiently understood. Objectives and ApproachIn a whole-population cohort of patients hospitalised with acute TSI, we aimed to describe the prevalence of pre-existing mental disorders, and compare differences in injury epidemiology, costs and inpatient allied health service access. Record-linkage study of all hospitalised cases of TSI between June 2013 and June 2016 in New South Wales, Australia. TSI was defined by specific ICD-10-AM codes. Mental disorder status was considered as pre-existing where specific ICD-10-AM codes were recorded in incident admissions. Results13,489 individuals sustained acute TSI during this study. 13.11%, 6.06%, and 1.82% had pre-existing mental illness, substance use disorder, and dual diagnosis, respectively. Individuals with mental disorder were older (p

IntroductionAgeing populations place considerable burden on healthcare systems. Innovative methods to implement and evaluate new care models to reduce unwarranted hospital presentations, are needed. Objectives and ApproachTo use linked Electronic Medical Record (EMR) data to evaluate a pilot community-based model of care to reduce hospital presentations in a cohort of patients at-risk of hospital representations following an acute admission. Patients admitted to a metropolitan hospital with a non-surgical condition andidentified as being at-risk of readmission using a state-wide risk algorithm, were eligible to receive a 30-day care navigation model of care. The intervention group were matched to similar eligible patients, who received usual care using propensity score matching. Linked data were obtained from the EMR based Data Warehouse to provide information on subsequent readmissions and community-based health service contacts. Readmission rates were compared at 30, 60, and 90-days post-discharge using Cox Proportional Hazards Regression, adjusted for confounders and weighted by the propensity score. Descriptive analyses were used to compare demographics and healthcare utilisations. ResultsData from 412,971 potentially eligible patients were extracted. Sixty-five received the intervention of which 63 were matched to 280 controls (aged 51-91 years, 54% female, 63% lived alone). At 30-days post-discharge (during intervention delivery) an 80% reduction in readmission rate in the intervention group was observed (Hazard Ratio [HR] 0.20; 95%Confidence Interval (CI) [0.01, 4.20]). At 60-days (HR 0.48; 95%CI [0.03, 8.05]) and 90-days (HR 0.83; 95%CI [0.06, 11.36]), readmission rate reductions decreased to 52% and 17% respectively. In the intervention group, 68.9% received an out-patient appointment compared to 40% of controls (P

IntroductionIncreasingly 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 ApproachWe 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. ResultsGuidance 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/ImplicationsOur 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.

IntroductionTo prevent further vascular events, prescribing of multiple classes of medications (antihypertensive, antithrombotic and lipid-lowering) is recommended in national clinical guidelines following ischaemic stroke. Objectives and ApproachUsing real-world data, we determined the association between optimal combination pharmacotherapy (supply of all three classes, “optimal pharmacotherapy”) and survival after stroke. We linked a cohort of patients with first-ever ischaemic stroke from the Australian Stroke Clinical Registry (2010-2014) with national pharmaceutical dispensing and national mortality data. Cox regression was used to determine associations between pharmacotherapy in the first 30 days of stroke with 1-year (from day 31 to 395) all-cause mortality. All analyses were adjusted for socio-demographic (age, sex) and clinical characteristics (stroke severity, discharge destination). ResultsAmong 6684 patients discharged following first-ever ischaemic stroke (median length-of-stay 5 days), 6466 patients who survived to 30 days were included (44% female, median age 74 years). During the first 30 days from discharge, 71.4% received ≥1 medication class, and 32.9% (n=2125) received optimal pharmacotherapy. Patients with optimal pharmacotherapy were older (≥75 years 50.3% vs

IntroductionThe Dementias Platform UK (DPUK) Cohort Explorer is an interactive, online visualisation tool that allows users to explore data for a number of DPUK cohorts. Over 30 variables across cohorts have been harmonised, including information on demographics, lifestyle, cognition, health, and genetic biomarkers. Objectives and ApproachThe tool has been developed to complement existing DPUK cohort metadata to provide a visual representation of participant numbers and field-level information for a selection of cohorts. This enables users to determine a cohort’s eligibility before applying for access to a cohort’s data, and aid in shaping potential hypotheses. Developed using Microsoft PowerBI, the Explorer hosts a subset of the cohort’s baseline, harmonised data, allowing a user to interrogate the visualisations of the uploaded data in a secure manner on the DPUK Data Portal website. Visualisations are linked so that participant numbers and distributions can be explored interactively. ResultsThis approach allows the user to explore the harmonised data across a number of cohorts simultaneously whilst setting and adjusting filters that are of interest to the user’s search criteria. This provides a better understanding of the real-world data and enables the user to determine the feasibility of each cohort for potential studies, whilst facilitating meaningful comparisons across cohorts. The tool currently visualises five DPUK cohorts with a total of 82,391 participants, however it is being incrementally developed with more cohorts being added continually. Conclusion / ImplicationsBy combing an easy-to-use, interactive dashboard with harmonised sets of real-world data, the tool allows the user to explore, interrogate and better understand field-level information in a secure manner with zero data transfer. This provides more insight for the user when applying for access to a cohort dataset using the DPUK Data Portal and may help the user to make more informed decisions and/or hypotheses.

IntroductionData Linkage of hashed datasets is much more difficult than linking in-the-clear data. Hashing prevents the use of matching tools that overcome messy data such as ‘contained-within’ functions and edit distance metrics. Hashing sensitive data received from third parties is becoming more common due to increased Data Security concerns. Institutions need to be ready to link hashed data with high accuracy, otherwise the quality of outputs from these linked datasets will suffer. Objectives and ApproachWe designed an innovative matching method, Derive and Conquer (D&C). We derived variables containing substrings or patterns of the full variable (e.g. Soundex or first 4 characters of a string) to match on instead. However, using lots of combinations of these derived variables would require thousands of traditional match keys to be programmed, run, and reviewed. Instead, D&C runs matchkeys on a derived agreement variable which amalgamates information stored in multiple derived variables into one value, reducing the number of matchkeys to a manageable amount. D&C runs on distributing computing systems using PySpark to link datasets containing millions of records in a timely manner. ResultsD&C was developed using in-the-clear UK Census and health records with results comparable to the in-the-clear gold standard. It is currently being tested on hashed data to link UK tax and benefits data to UK health records. 66.4 million records were declared matched - a realistic match rate for the UK population. Research into the linkage quality is ongoing to produce estimates on the amount of bias in the linkage and the precision and recall. We will be excited to present these results at the Conference in October. These results will be used to improve D&C. Conclusion / ImplicationsUsing these derived variables, we have been able to overcome the challenge of matching massive hashed datasets with a realistic match rate and in a realistic time frame.

IntroductionThe Inverse Care Law (ICL) programme in Wales was setup to tackle health inequalities. Eligible populations from deprived communities, at higher risk of cardiovascular disease (CVD) were invited to a health-check and offered appropriate lifestyle and clinical interventions. Objectives and ApproachEvaluation of this programme is vital to ensure that targeted interventions have been received by those most in need, including referrals to lifestyle services and support. The use of longitudinal population-scale routine-data required the development of an approach which was both efficient and cost effective. To achieve this, the Welsh Longitudinal General Practice (WLGP) data held in SAIL Databank was utilised. A programme-specific methodology was agreed by the programme-board and developed so that data collected from GP records prior, during and post health-check accurately identified the eligible population and allowed the effective assessment of lifestyle and clinical risk factors for CVD; poor diet, physical inactivity, smoking and high alcohol intake, so appropriate interventions could be offered. ResultsWe evaluated the programme from 2015 to 2019 in 70 GP’s across the participating Health-Boards, and identified 175,671 individuals eligible by the programme criteria. Substantial preliminary work has been carried out to ensure the specification of outcome measures are both clinically and epidemiologically accurate and relevant. The final report scheduled for release in August-2020, which will evaluate the impact of the programme. Conclusion / ImplicationsThis ambitious evaluation of a large-scale programme set in the community involving disparate systems and a range of stakeholders, has been both complex and challenging, requiring substantial effort to design and implement. We hope the outcomes and lessons learned from our experience will improve the design, implementation and evaluation of the programme and lead to improvements in services and the quality of life for people in Wales, and provide an exemplar for health care providers worldwide wishing to conduct similar programmes in the future.

Introduction“The Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research”, recent data breaches, and growing public awareness and controversy associated with secondary use of health data all highlight the need to understand what data sharing the public will support, under what circumstances, for what purposes and with whom. Objectives and ApproachThis symposium explores methods and findings from public engagement at all stages of data linkage research, beginning with short presentations (~6-8 minutes) on recent work: Mhairi Aitken: Consensus Statement - principles and an application using deliberative workshops to explore public expectations of public benefits from data-intensive health research Annette Braunack-Mayer/Felicity Flack: Surveys and citizens’ juries: Sharing government data with private industry Kim McGrail/Mike Burgess: Public deliberations on cross-sector data linkage, and combining public and private sources of data Alison Paprica: Plain language communication informed by Health Data Research Network Canada’s Public Advisory Council. Half the session will be spent interacting with the audience through live polling. The moderator will post a series of poll question such as “What is the most important thing for meaningful public engagement?” to prompt audience thinking on the topic. After the audience responses are revealed, panelists will share their own views about what they think is the best answer, and the main reason(s) behind their choice. The last 10-15 minutes of the session will be reserved for Q&A and dialogue with the audience. ResultsWe anticipate that this approach will surface emerging and tacit knowledge from presenters and the audience, and augment that through generative discussion. Conclusion / ImplicationsSession attendees will leave with a better understanding of the current state of knowledge and ways to talk about that understanding with other researchers, policy makers and the public.

IntroductionStudies of select groups (e.g. pre-industrial or subsistence farming populations) suggest that children had improved survival if their grandparents were alive, particularly maternal grandmothers (MGMs). This question is unanswered for Aboriginal Australian communities, where care of children is commonly shared by extended family members, and it is not known if this association holds for physical health in addition to mortality. Objectives and ApproachWe examined relationships between the health of Aboriginal children born in Western Australia from 2000 to 2013 and the health and vital status of their grandparents. Birth, death, inpatient hospital and emergency department records of all Aboriginal children and their grandparents were linked. Grandparents were categorised as ‘healthy’ (alive with Charlson comorbidity index score of 0 or 1), ‘unhealthy’ (alive with a score of 2 or more), or dead when the child was born. Results27,425 Aboriginal children linked to their MGM (at a minimum). Mortality up to age 2 was lower with healthy grandparents than unhealthy or dead grandparents (e.g. 11 deaths per 1000 live births with healthy MGM; 22 with unhealthy MGMs and 16 with dead MGMs). Children also had fewer acute healthcare contacts (e.g. 13% with healthy MGMs spent at least 7 days in hospital in their first 2 years compared with 19% with unhealthy or dead MGMs). However, healthcare contacts was largely unrelated to grandfathers. Outcomes were also associated with the number of living grandmothers. (e.g. 1.5% of children with 2 grandmothers were discharged against medical advice in their first 2 years, compared to 2.7% with one healthy grandmother and 3.7% with no grandmothers). Conclusion / ImplicationsChildren with healthy grandmothers have lower mortality and morbidity, possibly because of their care giving. These associations are unlikely to be due to genetic or environmental factors, as they are weaker or missing for grandfathers.

IntroductionRegular contact with a general practitioner (GP) has been shown to lower the risk of potentially avoidable hospitalisations (PAHs) independently of continuity of provider and frequency of contact. Multimorbidity affects between 55 and 98% people aged 65+ years and continues to place pressure on healthcare systems globally. However, little is known about its impact on the relationship between continuity of primary care and PAHs. Objectives and ApproachA retrospective, longitudinal cohort study using survey data linked to routinely-collected administrative health data from the 45 and Up Study conducted in New South Wales, Australia was used to investigate the effect measure modification by multimorbidity on the relationship between regularity of GP contact and PAHs. Multimorbidity was assessed using the Rx-Risk comorbidity score, which captures the number of condition groups, assigned based on medicine dispensing records, using a 5-year look-back period. PAHs were: (i) any unplanned hospitalisations, (ii) chronic ambulatory care sensitive conditions (ACSC) hospitalisations or (iii) unplanned ACSC hospitalisations. Multivariable logistic regression and population attributable fractions (PAF) were used to examine effect measure modification by multimorbidity. ResultsHigher GP regularity was significantly associated with a reduction in the probability of each PAH type. This reduction diminished with increasing multimorbidity with the effect measure modification most apparent for chronic ACSC and unplanned chronic ACSC hospitalisations. The PAF of moving to the highest quintile of regularity significantly reduced with increasing multimorbidity. For example, a reduction in the PAF of unplanned ACSC hospitalisations of 31.1% was observed in those with a RX Risk score of >10 (17.8%) compared with those with no multimorbidity (48.9%). Conclusion / ImplicationsWeakening of the relationship between GP visit regularity and PAHs with increasing levels of multimorbidity suggests a need to focus on improving primary care support to prevent PAHs for patients with multimorbidity.

IntroductionMonash University’s health researchers are at the forefront of Health, Epidemiological and Translational research. Powered by research funding of over $69 million per year, Monash encompasses 13+ registries, 44+ clinical trials and 47+ cohort studies underpinned by complex multimodal, large and often sensitive datasets. Many of these research activities are occurring in isolation. Opportunities exist to better deliver higher impact research at scale through capacity building in data sharing. Objectives and ApproachWe have designed this session to demonstrate how significant investment in health data platform infrastructure by a University, can support best practice in data linkage and health research. ResultsContent will include: An overview of Helix – people as a platform, research stakeholders, and key capabilities. Institutional data governance and sharing capabilities – linking two or more researcher held datasets exampled by a use-case of linking biobank data to a clinical registry dataset. Monash Helix data linkage working group – established to develop a community of practice to advance data linkage capabilities across Monash with a focus on: knowledge sharing; ethics, governance and data security; analytic training; and improved access to external administrative datasets. Secure eResearch Platform (SeRP) – developed by Swansea University who have partnered with Monash University to bring their best practice framework and technologies in managing sensitive health data to Monash researchers and their national and international collaborators. Future directions - participation in defining the strategic priorities of the Health Studies National Data Asset Program by creating distributed data infrastructure capability to develop, share and link FAIR data collections from the government, research and health care sectors. Conclusion/ImplicationsThe session will facilitate discussions on how we can build on these activities, both nationally and internationally, to develop a community of practice around knowledge sharing and best practice in data linkage.

IntroductionIn Australia, the notification rate for hepatitis C is 5 times greater among Aboriginal and Torres Strait Islander peoples than non‑Indigenous populations (164 vs. 35 per 100,000). Infection with Hepatitis C virus is associated with increased morbidity, mortality and health-related costs. Recently, the use of simple, tolerable and short-duration HCV therapies with extremely high efficacy (cure rates >90%) became available through the Pharmaceutical Benefits Scheme (PBS) on 1 March 2016. Objectives and ApproachOverall, this project aims to identify inequities in hepatitis C healthcare for Aboriginal and Torres Strait Islander peoples across SA and NT. By using 19 routinely collected health administration datasets, we will evaluate hepatitis C diagnoses, treatment uptake, and treatment outcomes, with focus on identifying patterns in healthcare access among Aboriginal and Torres Strait Islander people. The study links state-based and federal datasets, including Medicare, Pharmaceutical Benefits Scheme, and National Mortality Database, to hepatitis C notifications reported in SA since 1992 or in NT since 1999 using probabilistic data linkage methodology in a secured online environment. ResultsThe study population includes all hepatitis C notifications recorded in SA and NT, totalling more than 25,000 cases. Preliminary findings will be reported including incidence of hospitalisation, mortality, and morbidity by Aboriginal and Torres Strait Islander status. Conclusion / ImplicationsThis project is of national importance to address hepatitis C related morbidity and mortality in Australia, particularly among Aboriginal and Torres Strait Islander peoples. Now that highly effective direct acting anti-viral treatments are available, it is crucial to ensure key affected populations, including Aboriginal and Torres Strait Islander people, have access to these novel biomedical approaches. Findings will underpin future policy to reduce the burden of hepatitis C across SA and NT populations.

IntroductionStats NZ’s Integrated Data Infrastructure (IDI) is a linked longitudinal database combining administrative and survey data. Previously, false positive linkages (FP) in the IDI were assessed by clerical review of a sample of linked records, which was time consuming and subject to inconsistency. Objectives and ApproachA modelled approach, ‘SoLinks’ has been developed in order to automate the FP estimation process for the IDI. It uses a logistic regression model to calculate the probability that a given link is a true match. The model is based on the agreement types defined for four key linking variables – first name, last name, sex, and date of birth. Exemptions have been given to some specific types of links that we believe to be high quality true matches. The training data used to estimate the model parameters was based on the outcomes of the clerical review process over several years. ResultsWe have compared the FP rates estimated through clerical review to the ones estimated through the SoLinks model. Some SoLinks estimates fall outside the 95% confidence intervals of the clerically reviewed ones. This may be the result of the pre-defined probabilities for the specific types of links are too high. ConclusionThe automation of FP checking has saved analyst time and resource. The modelled FP estimates have been more stable across time than the previous clerical reviews. As this model estimates the probability of a true match at the individual link level, we may provide this probability to researchers so that they can calculate linked quality indicators for their research populations.

IntroductionThere are few examples of functional health information exchange environments or data linkage centres in the African settings. New opportunities are emerging as unique health identifiers and patient registration systems are being established nationally in many countries, and increasingly individuated health data are available for linkage, often linked to priority global health initiatives such as to support HIV and tuberculosis services. Objectives and ApproachWe sought to establish a province-wide health information exchange and data centre for individuated health data, leveraging a unique identifier and available individuated data. The intention with the Provincial Health Data Centre (PHDC) was to create a single well-governed environment which could simultaneous fulfil the functions of an exchange directly supporting care, as well as support research requests. Often the demands of academics, funders and global agencies for reporting result in data consolidation for research and reporting taking precedence over service delivery. ResultsThrough pragmatic use of data from all sources, the PHDC is able to usefully enumerate many health conditions of interest with sufficient fidelity for both service and research purposes. For research data requests, there has been a huge improvement in data governance alongside increased data availability as a result of the single environment with clear procedures for patient protection, and the benefits of data linkage prior to anonymisation. Many of the inference approaches have benefited hugely from interactions with researchers, which has in turn improved the quality of outputs for routine care. Conclusion / ImplicationsIn maturing digital health environments which are establishing consolidated data environments for the first time, aspiring from the start to a single well-governed environment for both patient care and research, is a virtuous model with many benefits over fragmented data linkage efforts

IntroductionAboriginal Community-Controlled Health Services (ACCHSs) routinely collect sexually transmissible infection (STI) and blood-borne virus (BBV) clinical data from individuals through electronic medical records (EMRs). At a population level these data are an underutilised resource and can be used to help drive clinical and public health interventions. Accordingly, we have established a national STI and BBV sentinel surveillance network for ACCHSs —the ATLAS network. Objectives and ApproachIn establishing the ATLAS network, we standardised and automated extraction of deidentified clinical data from multiple EMRs using The University of Melbourne’s GRHANITE™ software. Regular reports are provided to each ACCHS, addressing 12 clinical indicators considered best practice in STI and BBV control and management. Data are used to drive Continuous Quality Improvement strategies, can be aggregated at regional and national levels, and have the ability to link to other surveillance networks also using GRHANITE™. ResultsThe ATLAS network currently includes more than 30 ACCHSs, including urban, regional and remote areas. To date, data have been analysed for >50,000 individuals aged ≥15 years attending medical consultations at ACCHS between January 2016 and December 2018. For clients in the key age-range for STI testing of 15–29 years, the mean annual rate of chlamydia/gonorrhoea testing was 25% (range: 1–59%). The mean annual rate of chlamydia positivity for clients 15–29 years was 3% (range: 0–19%) and the mean annual rate of gonorrhoea positivity was 2% (range: 0–18%). Conclusion / ImplicationsAn integrated network of STI and BBV testing and management data has been successfully established amongst ACCHSs nationally. The ATLAS network can be readily expanded and has the potential to link to other datasets using the GRHANITE™ tool. ATLAS provides a unique data infrastructure with capacity to support novel research programs as well as underpin quality improvement activities within participating sites.

Background/ObjectivePatients with IgA vasculitis (IgAV aka Henoch Schonlein) may require aggressive initial treatment, are prone to disease relapses and may have sustained abnormal IgA responsiveness. We investigated whether the relatively rare diagnosis of childhood IgAV is associated with subsequent comorbidity, hospitalisation and interventions. MethodsUsing linked administrative health data from the Western Australian Rheumatic Disease Registry we performed an observational cohort study comparing hospitalisation and ED visit rates(per 1000 person years), comorbidity accrual (Charlson comorbidity index; CCI) and procedures between 494 IgAV patients < 20 years and 1385 matched controls over a twenty-year period. ResultsHospitalisation was proportionally (73.5 vs 51.5%) and by rate (21.7 vs 18.9; rate ratio 1.15, p

IntroductionLinked health record collections, when combined with large longitudinal surveys, are a rich research resource to inform policy development and clinical practice across multiple sectors. Objectives and ApproachThe Australian Longitudinal Study on Women’s Health (ALSWH) is a national study of over 57,000 women in four cohorts. Survey data collection commenced in 1996. Over the past 20 years, ALSWH has also established an extensive data linkage program. The aim of this poster is to provide an overview of ALSWH’s program of regularly up-dated linked data collections for use in parallel with on-going surveys, and to demonstrate how data are made widely available to research collaborators. ResultsALSWH surveys collect information on health conditions, ageing, reproductive characteristics, access to health services, lifestyle, and socio-demographic factors. Regularly updated linked national and state administrative data collections add information on health events, health outcomes, diagnoses, treatments, and patterns of service use. ALSWH’s national linked data collections, include Medicare Benefits Schedule, Pharmaceutical Benefits Scheme, the National Death Index, the Australian Cancer Database, and the National Aged Care Data Collection. State and Territory hospital collections include Admitted Patients, Emergency Department and Perinatal Data. There are also substudies, such as the Mothers and their Children’s Health Study (MatCH), which involves linkage to children’s educational records. ALSWH has an internal Data Access Committee along with systems and protocols to facilitate collaborative multi-sectoral research using de-identified linked data. Conclusion / ImplicationsAs a large scale Australian longitudinal multi-jurisdictional data linkage and sharing program, ALSWH is a useful model for anyone planning similar research.

IntroductionAround the world, the need for mental health, addictions and home care services is growing. Government commitment and collective efforts to bridge data gaps, develop indicators and publicly report results are key elements in Canada’s efforts to improve access. Objectives and ApproachThis symposium will demonstrate how a coalition of stakeholders united to use real-world data to measure progress and drive change. Each presentation highlights a different aspect of the project with participant interaction, aiming for the Canadian context to spark knowledge exchange across sectors and countries: Presentation 1 - Coalitions and consensus (10 min.): processes and engagement for successful collaboration between governments, providers, measurement experts and people with lived experiences to select and develop indicators Facilitated Q&A (5 min.) Presentation 2 - Standards and data infrastructure (10 min.): new standards to enhance data comparability and strengthened data infrastructure to support measurement and reporting Facilitated Q&A (5 min.) Presentation 3 - Indicator development (10 min.): the indicator development cycle, methodological approaches using linked and partial data, and development strategies for new concepts Facilitated Q&A (5 min.) Presentation 4 - Public reporting and policy impact (10 min.): describes how public reporting supports sustained commitments and energizes change using targeted tools and messages Facilitated Q&A (15 min.) ResultsPublic reporting began in 2019, with 3 new indicators released annually over 4 years. Initial reporting provides a baseline to track improvements, and a starting point for health system planners to learn from peers across Canada. The indicators have been a catalyst to fill important data gaps in emergency and home care services. Conclusion / ImplicationsThrough shared priorities, coalitions and linked data, information gaps are being filled to drive advancements in access to mental health and addictions services, and home care. Lessons learned in Canada can be adapted internationally to galvanize needed improvements in these sectors.

IntroductionWhile the quantity and type of datasets used by data linkage projects is growing, there remain some datasets that are ‘not available’ or ‘hard to access’ by researchers and linkers, either due to legal/regulatory constraints restricting the release of personally identifying information or because of privacy or reputational concerns. Advances in privacy-preserving record linkage methods (e.g. PPRL-Bloom) have made it possible to overcome this impasse. These techniques aim to provide strong privacy protection while still maintaining high linkage quality. PPRL-Bloom methods are being used in practice. The Centre for Data Linkage (CDL) at Curtin University has been involved in several PPRL linkage and evaluation projects using real-world data. As the methods are relatively new, published information on achievable linkage quality in real-world scenarios is limited. Objectives and ApproachWe present and describe several real-world applications of privacy preserving record linkage (PPRL-Bloom) where the quality of the linkage could be ascertained. In each case, data was linked ‘blind’; that is, without linkers having access to the original personal identifiers at any stage, or having any additional information about the records. Evaluations include a linkage of state-based morbidity and mortality records, a linkage of a number of general practice datasets to morbidity and emergency records, and a linkage of a range of state-based non-health administrative data, including education, police, housing, birth and child protection records. ResultsThe privacy preserving record linkage performed admirably, with very high-quality results across all evaluations. Conclusion / ImplicationsPrivacy preserving linkage is a useful and innovative methodology that is currently being used in real world projects. The results of these evaluation suggest it can be an appropriate linkage tool when legal or other constraints block release of personally identifying information to third party linkage units.

IntroductionIn 2016, the Provincial Overdose Cohort (ODC) was created following the declaration of the public health emergency in British Columbia (BC), Canada. The ODC is a set of longitudinal and linked administrative data which identifies illicit drug-related overdose events, including death, ambulance, emergency room, hospital, physician, and prescription drug records. Objectives and ApproachThe ODC was developed to better understand factors associated with overdose in order to support response activities, prevent overdose deaths, and identify trends and opportunities for interventions. Person-level linkages were conducted using provincial health insurance and health history data; socio-economic information, mental and physical illness diagnoses, and corrections history were also appended. The ODC currently includes people who have had a drug-related overdose between January 1 st 2015 and December 31 st 2017 as well as a 20% random sample of the general population. ResultsThe ODC contains 36,576 overdose episodes and 23,161 people who have a drug-related overdose between January 1 st , 2015 and December 31 st 2017. Of the 23,161 people, 3,604 (15.6%) had a fatal overdose and 19,557 (84.4%) had a non-fatal overdose. 49.9% of people in BC who had an overdose were 20-39 years of age and 67.4% were males. From 2015 to 2017, the proportion of people experiencing 3 or more overdoses a year increased from 3.6% to 8.7%, respectively. There were an increasing number of fatal and non-fatal drug-related overdoses in BC during this time period. Conclusion / ImplicationsLarge population data linkages can be invaluable tools during a public health emergency. Collaborative partnerships and a shared data governance across jurisdictions was central in building the ODC and understanding how various social determinants of health impact risk of overdose among people who use drugs.

The Journey to Social Inclusion (J2SI) Social Impact Investment (SII) is Victoria’s first social impact investment. It has two outcome payments, for stable housing and reduction in hospital bed days. Linked administrative data is being used to calculate the performance levels and outcome payments from the Victorian Government to J2SI. This paper discusses the methodologies and challenges encountered in the use of the linked administrative data for the calculation of the outcome payments for the J2SI SII. IntroductionJ2SI will receive half of the program costs for service delivery based on the level of performance of two outcome payments. The outcome payments are the difference between the results for the Intervention Group and the Control Group. The results are being calculated using linked administrative datasets for health, housing and justice, by the Centre for Victorian Data Linkage (CVDL) and the Centre for Social Impact, University of Western Australia (CSI UWA) respectively. Objectives and ApproachThe paper will outline the approach taken to calculate the results for the Intervention Group and the Control Group, explore the benefits and challenges of arising from different parties being involved in the calculations and present the lessons learned for future social impact bonds in Victoria and other States/Territories. ResultsThe work has highlighted the importance of clearly understanding and documenting all of the datasets and how they are to be used prior to the commencement of the calculations. High level descriptions are not adequate preparation for the precision required for the actual calculations. The work has also highlighted the differences is using linked data for research and service implementation. Conclusion / ImplicationsLinked administrative data is a robust source of data for the calculation of outcome payments for social impact investments. However, with the timeliness of payments important for service delivery and financing, thorough preparation for the calculation of the results and outcome payments is essential.

IntroductionLittle is known of the potential impacts of introducing data linkage processes on response rates and biases in school-based surveys. Objectives and ApproachThis paper assessed: i) the impact on parental consent rates for student participation in a school survey; ii) sample representativeness; and iii) the quality of identifiable data provided to facilitate linkage. An option for data linkage was piloted in a sub-sample of schools participating in the Student Health and Wellbeing survey, a national survey of adolescents in Wales, UK. Schools agreeing to participate were randomized 2:1 to receive the data linkage question. Survey responses from consenting students were anonymised and linked to routine datasets (e.g. general practice, inpatient, and outpatient records). Parental withdrawal rates were calculated for linkage and non-linkage samples. Multilevel logistic regression models compared characteristics between: i) consenters and non-consenters; ii) successfully and unsuccessfully linked students; and iii) linked cohort and peers within the general population, with additional comparisons of mental health diagnoses and health service contacts. ResultsThe sub-sample comprised 64 eligible schools (out of 193), with data linkage piloted in 39. Parental consent was comparable across linkage and non-linkageschools. 48.7% (n=9,232) of students consented to data linkage. Consenting students were more likely to be younger, more affluent, have higher positive mental wellbeing, and report fewer risk-related behaviours compared to non-consenters. Overall, 69.8% of consenting students were successfully linked, with higher rates of success among younger students. The linked cohort had lower rates of mental health diagnoses (5.8% vs. 8.8%) and specialist contacts (5.2% vs. 7.7%) than general population peers. ConclusionIntroducing data linkage within a national survey of adolescents had no impact on study completion rates. Students consenting to data linkage, and those successfully linked, differed from non-consenting students on numerous key characteristics, raising questions concerning the representativeness of linked cohorts.

IntroductionCanadians report persistent problems accessing primary care despite an increasing per-capita supply of primary care physicians (PCPs). There is speculation that PCPs, especially those early in their careers, may be working less and/or choosing to practice in focused clinical areas rather than comprehensive family medicine. Objectives and ApproachOur objectives were to compare practice patterns among early-career (

IntroductionDespite recommendations, most studies examining health inequalities fail to report both absolute and relative summary measures. We examine colorectal cancer (CRC) survival for patients with and without severe psychiatric illness (SPI) to demonstrate the use and importance of relative and absolute effects. Objectives and ApproachWe conducted a retrospective cohort study of CRC patients diagnosed between 01/04/2007 and 31/12/2012, using linked administrative databases. SPI was defined as diagnoses of major depression, bipolar disorder, schizophrenia, and other psychotic illnesses six months to five years preceding cancer diagnosis and categorized as inpatient, outpatient or none. Associations between SPI history and risk of death were examined using Cox Proportional Hazards regression to obtain hazard ratios and Aalen’s semi-parametric additive hazards regression to obtain absolute differences. Both models controlled for age, sex, primary tumour location, and rurality. ResultsThe final cohort included 24,507 CRC patients, 482 patients had an outpatient SPI history and 258 patients had an inpatient SPI history. 58.1% of patients with inpatient SPI history died, and 47.1% of patients with outpatient SPI history died. Patients with an outpatient SPI history had a 40% (HR 1.40, 95% CI: 1.22-1.59) increased risk of death and patients with an inpatient SPI history had a 91% increased risk of death (HR 1.91, 95% CI: 1.63-2.25), relative to no history of a mental illness. An outpatient SPI history was associated with an additional 33 deaths per 1000 person years, and an inpatient SPI was associated with an additional 82 deaths per 1000 person years after controlling for confounders. Conclusion / ImplicationsWe demonstrated that reporting of both relative and absolute effects is possible and calculating risk difference is relatively simple using Aalen models. We encourage future studies examining inequalities with time-to-event data to use this method and report both relative and absolute effect measures.

IntroductionMusculoskeletal problems, including conditions such as back pain, neck pain, rheumatoid arthritis, gout and osteoarthritis are common in the population and significant contributors to global disease burden. Age is one of the most common risk factors for musculoskeletal conditions and over 40% of older people accessing residential aged care have a musculoskeletal condition. It is not known whether individuals living in the community with musculoskeletal conditions have similar needs to those in permanent care and this is important to know in order to provide appropriate care. Objectives and ApproachThe objective of this study was to profile individuals with musculoskeletal conditions in different aged care service settings (i.e. permanent care, community care only, transition/ respite care, or no services). Specifically, we examined the concurrent chronic conditions, health risk factors and functional limitations of individuals by service setting. A cross-sectional evaluation of individuals in the National Historical Cohort of the Registry of Senior Australians (ROSA) between 2004 and 2014 was conducted. Multivariable logistic regression models estimated the factors associated with being in different aged care settings. Odds ratios (OR) and 95% confidence intervals (CI) were determined. Results401,026 (42.5%) individuals with musculoskeletal conditions were assessed for aged care service eligibility during the study period. Of these 197,181 (49.2%) accessed permanent care, 37,003 (9.2%) accessed home care, 54,826 (13.7%) transition/respite, and 112,016 (27.9%) - no care. Individuals accessing community care compared to residential care were more likely to be female, have pain and have difficulty maintaining their home, as were individuals accessing no services compared to residential care. Conclusion / ImplicationsCompared to those in residential care, individuals with musculoskeletal conditions in the community with or without assistance had few differences related to other chronic conditions and functional limitations. But the reasons why some had support, while others did not, are unclear.

IntroductionLarge volumes of health data are generated through the interaction of individuals with hospitals, government agencies and health care providers. There is potential in the linkage and sharing of administrative data with private industry to support improved drug and device provision but data sharing is highly contentious. Objectives and ApproachWe conducted a scoping review of quantitative and qualitative studies examining public attitudes towards the sharing of health data, held by government, with private industry for research and development. We searched four data bases, PubMed, Scopus, Cinahl and Web of Science as well as Google Scholar and Google Advanced. The search was confined to English-only publications since January 2014 but was not geographically limited. We thematically coded included papers. ResultsWe screened 6788 articles. Thirty-six studies were included primarily from UK and North America. No Australian studies were identified. Across studies, willingness to share non-identified data was generally high with the participant’s own health provider (84-91%) and academic researchers (64-93%) but fell if the data was to be shared with private industry (14-53%). There was widespread misunderstanding of the benefits of sharing data for health research. Publics expressed concern about a range of issues including data security, misuse of data and use of data to generate profit. Conditions which would increase public confidence in sharing of data included: strict safeguards on data collection and use including secure storage, opt-in or opt-out consent mechanisms, and good communication through trusted agents. Conclusion / ImplicationsWe identified a research gap: Australian views on sharing government health data with private industry. The international experience suggests that public scepticism about data sharing with private industry will need to be addressed by good communication about public benefit of data sharing, a strong program of public engagement and information sharing conducted through trusted entities.

IntroductionChildren with special educational needs (SEN) often struggle academically. Previous studies found that children’s abilities in kindergarten are predictive of their future SEN status. It is currently unknown whether these predictors differ in children with and without an early identification of a special need in kindergarten (SN-K). Objectives and ApproachWe investigated early predictors of SEN in Grade 3, in a cohort of Ontario children, with and without SN-K (1,824 and 62,842, respectively), who attended kindergarten between 2003/04 and 2005/06. Early Development Instrument data, a teacher-completed checklist of children’s development, were linked to Grade 3 standardized reading, writing, and mathematics test scores. Controlling for children’s demographics, multivariate binary logistic regressions were conducted examining the association between children’s developmental outcomes, their functional impairments, the necessity for further assessment (all reported by their kindergarten teacher) and their SEN status in Grade 3. ResultsOverall, 69.8% of children with SN-K had SEN in Grade 3, while 11.6% of children without SN-K had SEN. Our analyses revealed that, for children with SN-K, having a functional impairment was the most significant predictor of having SEN in Grade 3 (Odds Ratio=3.61, 2.59-5.02 95% confidence interval). For children without SN-K, teachers reporting the need for further assessment was the strongest predictor of having SEN in Grade 3 in children without SN-K (Odds Ratio=2.70, 2.49-2.93). Conclusion / ImplicationsEarly predictors of SEN in Grade 3 differ for children who receive an early identification (SN-K) compared to those who don’t. How children with SN-K function in a classroom is the best predictor of SEN in Grade 3, while teachers’ observation that a child needs further assessment is the strongest predictor of SEN in Grade 3 for those without SN-K. Addressing these areas early on may help reduce the number of children with SEN in later grades and may positively impact their future academic success.

IntroductionResilience is a key factor in healthy development of children who have experienced adversity in early life. Current methods of assessment involve using questionnaires - few of which are appropriate for young children, most are time consuming and rely on parent recall, thereby introducing bias. Additionally, widespread implementation of these would be costly, making population-level assessment of resilience impractical. The current study will leverage multi-sector, linkable, whole-population data from health, education, justice and social services to explore alternative ways to assess resilience in children. Objectives and ApproachThe purpose of this study is to identify factors in administrative data that emerge as significant determinants of resilience, demonstrated by children who experience adversity in early life but develop normally. Children born in Manitoba between 2000-2012 will be included. Adversity will be identified as families receiving income assistance, and/or the presence of adverse childhood experiences (ACEs) in linked databases such as Justice (incarcerated parent) and Health (parent with mental or substance use disorder). Development will be measured using the Early Development Instrument (EDI). EDI outcomes will be assessed according to severity and frequency of adversity. Pre-identified covariates that map onto sub-constructs of resilience will be assessed using multivariable linear regression to determine whether they are associated with higher EDI scores in the context of adversity. ResultsThe identification of administrative data variables associated with resilience in children will serve as a valuable tool for population-level assessment of resilience among children who experience adversity at young ages. Conclusion / ImplicationsWhile complete eradication of childhood adversity is unlikely, the continued development of programs and policies that build resilience in children is crucial. The results of this study will provide a means for population-level evaluation of such programs and policies, ultimately improving evidence for policy and decision-makers in the area of child development.

IntroductionRecent advances in Australia mean that it is possible to link national clinical registries with government held administrative datasets. However, formal evaluations of such activities and the potential impact for research are lacking. Objectives and ApproachWe aimed to assess the feasibility and accuracy of linking registrants from the Australian Stroke Clinical Registry (AuSCR) with the Medicare enrolment file. Following data custodian and ethics approvals, personal linkage identifiers were submitted to the Australian Institute of Health and Welfare (AIHW). De-identified data from AuSCR and the AIHW were submitted into the Secure Unified Research Environment and merged using project specific person-based IDs. We calculated the proportion of patients linked with the Medicare enrolment file that were present in the associated Medicare and medication dispensing datasets and the proportion with claims after their date of death. Logistic regression was used to identify factors associated with a non-merged patient. Results17,980 AuSCR registrants (January 2010-July 2014) were submitted for linkage (median age 76 years; 46% female; 67% ischaemic stroke; 16% TIA). Of these, 93% were merged with Medicare (N=16,648) and 95% with subsidised medication dispensing claims data (N=17,079). In those who died, 127 (0.8%) had one or more Medicare claim and 411 (2.4%) had one or more medications dispensed after their death date. Asian born registrants were less likely to be merged with Medicare (adjusted Odds Ratio [aOR]: 0.54; 95% Confidence Interval [CI]: 0.40, 0.72) than Australian born registrants. Those aged ≥85 years were less likely to be merged with Medicare data than those aged

IntroductionThe Census is the largest statistical collection undertaken by the Australian Bureau of Statistics (ABS), with its data critical to informing the planning and delivery of Government and community services. While the Census measure of income supports a wide range of analysis, demand exists for additional income topics to complement and extend the range of socio economic analysis that can be undertaken. The ABS has recently developed three experimental income topics for the 2016 Census using linked administrative data: main source of income; main source of government payments; and previous financial year income. Objectives and ApproachThis research utilised administrative data integrated by the ABS for the Multi-Agency Data Integration Project (MADIP). Taxation data from the Australian Taxation Office (ATO) and social security data from the Department of Social Services (DSS) were used in conjunction with the 2016 Census data to derive the additional topics. ResultsOverall, the three measures compare relatively closely to similar measures from the ABS Survey of Income and Housing (SIH). The ‘Main source of income’ and ‘Main source of government payments’ measures exhibit similar distributions to those from the SIH. The ‘Previous financial year income’ measures compare more closely with Census and SIH at the higher end of the income distribution, with some differences apparent at the lower end of the income distribution. Conclusion / ImplicationsThis work demonstrates the potential to supplement and enhance existing Census topics with linked administrative data. Further research, development and consultation with data users and the Australian community is needed.

IntroductionApplications in domains ranging from healthcare to national security increasingly require records about individuals in sensitive databases to be linked in privacy-preserving ways. Missing values make the linkage process challenging because they can affect the encoding of attribute values. No study has systematically investigated how missing values affect the outcomes of different encoding techniques used in privacy-preserving linkage applications. Objectives and ApproachBinary encodings, such as Bloom filters, are popular for linking sensitive databases. They are now employed in real-world linkage applications. However, existing encoding techniques assume the quasi-identifying attributes used for encoding to be complete. Missing values can lead to incomplete encodings which can result in decreased or increased similarities and therefore to false non-matches or false matches. In this study we empirically evaluate three binary encoding techniques using real voter databases, where pairs of records that correspond to the same voter (with name or address changes) resulted in files of 100,000 and 500,000 records containing from 0% to 50% missing values. ResultsWe encoded between two and four of the attributes first and last name, street, and city into three record-level binary encodings: Cryptographic long-term key (CLK) [Schnell et al. 2009], record-level Bloom filter (RBF) [Durham et al. 2014], and tabulation Min-hashing (TBH) [Smith 2017]. Experiments showed a 10% to 25% drop on average in both precision and recall for all encoding techniques when missing values are increasing. CLK resulted in the highest decrease in precision, while TBH resulted in the highest decrease in recall compared to the other encoding techniques. ConclusionBinary encodings such as Bloom filters are now used in practical applications for linking sensitive databases. Our evaluation shows that such encoding techniques can result in lower linkage quality if there are missing values in quasi-identifying attributes. This highlights the need for novel encoding techniques that can overcome the challenge of missing values.

IntroductionDuring 2019, the Western Australian (WA) government and Curtin University’s Centre for Data Linkage (CDL) created a large, de-identified researchable database – the Social Investment Data Resource (SIDR) – to support government in delivering targeted early interventions to young offenders and their families to reduce the likelihood of re-offending (the Target 120 program). Objectives and ApproachSIDR brings together administrative data from health, education, justice, child protection, disability and housing sectors. The linked, de-identified data provides an invaluable resource for actuarial assessment and social investment analytics to assess long-term costs and benefits of the Target 120 program. SIDR also provides an invaluable tool for academic research. SIDR adopted a distributed linkage model where linkage workload was shared between the Department of Health Data Linkage Branch who create and maintain the WA Data Linkage System (WADLS) and the CDL. Design elements of the model included a common spine (embedded into the infrastructure of both groups), methods for leveraging quality from WADLS, and inclusion of family relationships data from the WA Family Connections database. The linkage model within SIDR uses a combination of traditional and privacy-preserving record linkage (PPRL) methods. PPRL does not require release of personal identifiers; instead, data is irreversibly hashed prior to release for probabilistic linkage. ResultsThrough cooperation (distributed linkage) and innovation (a mix of traditional and PPRL linkage), the project has delivered a large, linked, cross-sectoral data resource for policymakers and researchers. Sharing of the linkage workload maximised the capacity and unique capabilities of each linkage unit. PPRL enabled ‘hard to get’ datasets from justice to be included. SIDR is being updated in 2020. Conclusion / ImplicationsSIDR provides a resource for whole-of-government policy development, service evaluation, academic research and social investment analytics for T120 and beyond. The SIDR linkage model has potential for adaptation and use elsewhere.

IntroductionIn their 2019 homelessness strategy, Welsh Government make the commitment for a Wales where homelessness is a rare experience, and if it does occur is brief and non-recurrent. However, measuring repeated homelessness/housing issues (or ‘non-recurrence’) has thus far not been possible in Wales, with only aggregate data collected nationally on interactions between people and local government housing-services. Drawing on recently available individual level data from a single local government area in Wales, we set out to explore repeated use of housing/homelessness services. Objectives and ApproachThe sample used in this analysis relates to approximately 6,000 individuals who sought assistance from Swansea local government housing-service during a four-year period, from January 2012 to December 2015. In order to group people with similar epeated interactions with the housing service, we use analyses that firstly aggregate the total time spent interacting with the service and over how many distinct periods (Cluster analysis), and secondly explore patterns in sequences of month-by-month service use histories (Sequence Analysis). ResultsWe describe the patterns of repeat housing-service use identified through the Cluster Analysis and the Sequence Analysis, and the characteristics of each group. We also directly compare the ‘time-aggregating’ and ‘time-patterned’ based approaches to classifying people, in order to illustrate the differences that methodology can bring to our conceptualisation of repeat housing service use. Conclusion / ImplicationsThe findings from this study have the potential to inform Wales’ move away from aggregate to individual level national homelessness data collection, by illustrating how individual level data can be used to provide new insights into homelessness. Exploring different conceptualisations of repeated housing-service use can also inform future developments in the measurement of repeat housing problems, and therefore monitor progress in achieving a Wales where homelessness is rare, brief, and non-recurrent.

IntroductionPatient 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 ApproachObjectives 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 (