Linking Australian Stroke Clinical Registry data with Australian government Medicare and medication dispensing claims data and the potential for bias

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 <65 years (aOR 0.24; 95% CI: 0.19, 0.29) but were more likely to be merged with dispensing data (aOR: 2.22 (95% CI: 1.73, 2.84). Conclusion/ImplicationsLinkage between a national clinical quality registry and the Medicare spine is feasible. These linkages will provide novel insights into post-stroke care.

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Are Procedures Codes in Claims Data a Reliable Indicator of Intraoperative Splenic Injury Compared with Clinical Registry Data?

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2014.02.029 ◽

2014 ◽

Vol 219 (2) ◽

pp. 237-244.e1 ◽

Cited By ~ 8

Author(s):

Anne M. Stey ◽

Clifford Y. Ko ◽

Bruce Lee Hall ◽

Rachel Louie ◽

Elise H. Lawson ◽

...

Keyword(s):

Splenic Injury ◽

Claims Data ◽

Registry Data ◽

Reliable Indicator ◽

Clinical Registry

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Using linked data to evaluate enhanced primary care policies for chronic diseases using stroke as a case study

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.882 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Nadine Andrew ◽

Joosup Kim ◽

Dominique Cadilhac ◽

Vijaya Sundararajan ◽

Amanda Thrift

Keyword(s):

Primary Care ◽

Chronic Diseases ◽

Large Scale ◽

Claims Data ◽

Data Linkage ◽

Australian Government ◽

Healthcare Provision ◽

Clinical Registry ◽

Care Policies

IntroductionThe global burden of chronic diseases is large and increasing. In response, governments are investing substantial funds in innovative models of primary care, characterised by multidisciplinary care and self-management support for people with chronic conditions. Currently, large scale population-based evaluations of the effectiveness of these policies are lacking. Objectives and ApproachWe aim to evaluate the effectiveness and cost-effectiveness of enhanced primary care policies for chronic diseases funded through Medicare Australia using stroke as a case study. Person-level linkages from the following will be used: Australian Stroke Clinical Registry (AuSCR) to define the cohort; Australian government-held Medicare claims data to identify receipt or not of enhanced primary care items; state government-held hospital data to define outcomes; and Australian government-held pharmaceutical and aged care claims data to define covariates. In Australia, unique identifiers are not used therefore, personal-identifiers will be submitted to data linkage units and content records merged using a Project-ID. ResultsIdentifiers from ~25,000 AuSCR registrants (2012-2016), from Victoria and Queensland will be submitted for linkage. The index event is the first event recorded in the AuSCR. Data applications to state health departments and the Australian Institute of Health and Welfare have commenced. To obtain detailed information on patient’s primary care experience 1,500 randomly selected AuSCR registrants are being sent surveys. Multivariable analyses using a competing risks Poisson regression model for multiple events and adjusted by a propensity score, will be used to test for differences in the rates of hospital presentations. We have power (a >0.05) to detect a ≥6% difference in the number of hospital contacts between those who did and did not receive enhanced primary care. An economic evaluation will also be undertaken. Conclusion/ImplicationsThis is the largest stroke data linkage study in Australia. The breadth of data will provide a comprehensive evaluation of the effectiveness of enhanced primary care policies within “real world” healthcare provision. Methods will advance the use of population data linkage in healthcare evaluation where unique identifiers are unavailable.

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Linking inpatient clinical registry data to Medicare claims data using indirect identifiers

American Heart Journal ◽

10.1016/j.ahj.2009.04.002 ◽

2009 ◽

Vol 157 (6) ◽

pp. 995-1000 ◽

Cited By ~ 257

Author(s):

Bradley G. Hammill ◽

Adrian F. Hernandez ◽

Eric D. Peterson ◽

Gregg C. Fonarow ◽

Kevin A. Schulman ◽

...

Keyword(s):

Claims Data ◽

Registry Data ◽

Clinical Registry ◽

Medicare Claims ◽

Medicare Claims Data

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Cohort profile: the St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry, a pragmatic prospective database defining outcomes in total hip and knee replacement patients

BMJ Open ◽

10.1136/bmjopen-2020-040408 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040408

Author(s):

Daniel Gould ◽

Sharmala Thuraisingam ◽

Cade Shadbolt ◽

Josh Knight ◽

Jesse Young ◽

...

Keyword(s):

Registry Data ◽

Prescribing Patterns ◽

Audit And Feedback ◽

Economic Evaluations ◽

Tertiary Referral Hospital ◽

Clinical Registry ◽

Patient Reported ◽

Prospective Database ◽

Future Plans

PurposeThe St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry is an institutional clinical registry housed at a tertiary referral hospital in Australia. The SMART Registry is a pragmatic prospective database, which was established to capture a broad range of longitudinal clinical and patient-reported outcome data to facilitate collaborative research that will improve policy and practice relevant to arthroplasty surgery for people with advanced arthritis of the hip or knee. The purpose of this cohort profile paper is to describe the rationale for the SMART Registry’s creation, its methods, baseline data and future plans for the Registry. A full compilation of the data is provided as a reference point for future collaborators.ParticipantsThe SMART Registry cohort comprises over 13 000 consecutive arthroplasty procedures in more than 10 000 patients who underwent their procedure at St Vincent’s Hospital Melbourne, since January 1998. Participant recruitment, data collection and follow-up is ongoing and currently includes up to 20 years follow-up data.Findings to dateSMART Registry data are used for clinical audit and feedback, as well as for a broad range of research including epidemiological studies, predictive statistical modelling and health economic evaluations. At the time of writing, there were 46 publications from SMART Registry data, with contributions from more than 67 coauthors.Future plansWith the recent linking of the SMART Registry with Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data through the Australian Institute of Health and Welfare, research into prescribing patterns and health system utilisation is currently underway. The SMART Registry is also being updated with the Clavien-Dindo classification of surgical complications.

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Lessons learned in the use of clinical registry data in a multi-centre prospective study: the Pediatric Heart Network Residual Lesion Score Study

Cardiology in the Young ◽

10.1017/s1047951119001148 ◽

2019 ◽

Vol 29 (7) ◽

pp. 930-938 ◽

Cited By ~ 1

Author(s):

Carol J. Prospero ◽

Felicia L. Trachtenberg ◽

Victoria L. Pemberton ◽

Sara K. Pasquali ◽

Brett R. Anderson ◽

...

Keyword(s):

Heart Surgery ◽

Data Extraction ◽

Lessons Learned ◽

Registry Data ◽

Lesion Score ◽

Clinical Registry ◽

Score Study ◽

Future Studies ◽

Center Staff ◽

Residual Lesion

AbstractBackground:Using existing data from clinical registries to support clinical trials and other prospective studies has the potential to improve research efficiency. However, little has been reported about staff experiences and lessons learned from implementation of this method in pediatric cardiology.Objectives:We describe the process of using existing registry data in the Pediatric Heart Network Residual Lesion Score Study, report stakeholders’ perspectives, and provide recommendations to guide future studies using this methodology.Methods:The Residual Lesion Score Study, a 17-site prospective, observational study, piloted the use of existing local surgical registry data (collected for submission to the Society of Thoracic Surgeons-Congenital Heart Surgery Database) to supplement manual data collection. A survey regarding processes and perceptions was administered to study site and data coordinating center staff.Results:Survey response rate was 98% (54/55). Overall, 57% perceived that using registry data saved research staff time in the current study, and 74% perceived that it would save time in future studies; 55% noted significant upfront time in developing a methodology for extracting registry data. Survey recommendations included simplifying data extraction processes and tailoring to the needs of the study, understanding registry characteristics to maximise data quality and security, and involving all stakeholders in design and implementation processes.Conclusions:Use of existing registry data was perceived to save time and promote efficiency. Consideration must be given to the upfront investment of time and resources needed. Ongoing efforts focussed on automating and centralising data management may aid in further optimising this methodology for future studies.

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PCN267 PREVALENCE OF DIFFERENT TYPES OF CANCER IN GERMANY: A COMPARATIVE ANALYSIS OF CLAIMS DATA AND REGISTRY DATA IN GERMANY

Value in Health ◽

10.1016/j.jval.2019.09.462 ◽

2019 ◽

Vol 22 ◽

pp. S487

Author(s):

F. Hardtstock ◽

M. Ghiani ◽

T. Wilke ◽

U. Maywald ◽

S. Mueller

Keyword(s):

Comparative Analysis ◽

Claims Data ◽

Registry Data ◽

Different Types ◽

Analysis Of Claims Data

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Using Both Clinical Registry and Administrative Claims Data to Measure Risk-adjusted Surgical Outcomes

Annals of Surgery ◽

10.1097/sla.0000000000001031 ◽

2016 ◽

Vol 263 (1) ◽

pp. 50-57 ◽

Cited By ~ 18

Author(s):

Elise H. Lawson ◽

Rachel Louie ◽

David S. Zingmond ◽

Greg D. Sacks ◽

Robert H. Brook ◽

...

Keyword(s):

Surgical Outcomes ◽

Claims Data ◽

Administrative Claims ◽

Clinical Registry ◽

Administrative Claims Data

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Validating health conditions in a clinical registry using administrative data algorithms

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.691 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Lisa Lix ◽

Lisa Zhang ◽

Lin Yan ◽

Tolu Sajobi ◽

Richard Sawatzky ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Blood Pressure ◽

Back Pain ◽

Administrative Data ◽

High Blood Pressure ◽

Registry Data ◽

Health Conditions ◽

Clinical Registry ◽

Patient Reported ◽

Reported Health

IntroductionClinical registries are a potentially valuable resource to study the effects of medical interventions on outcomes, particularly patient-reported outcomes like health-related quality of life, which are not included in administrative data. However, because clinical registries are primarily intended for patient management and not for research, their validity must be established. Objectives and ApproachOur objective was to validate patient self-reported health conditions in a clinical registry. Study data were from a population-based regional joint replacement registry in the Canadian province of Manitoba. The clinical registry data were linked to administrative health data. Validated administrative data algorithms for 12 conditions were defined using diagnosis codes in hospital and physician records and medication codes in prescription drug records for the period up to three years prior to the joint replacement surgery. Accuracy of the clinical registry data was estimated using Cohen’s kappa coefficient, sensitivity, specificity, and positive and negative predictive values (PPV; NPV); 95% confidence intervals were also estimated. Analyses were stratified by joint type, age group, and sex. ResultsThe study cohort included 20,592 individuals (average age 66.3 years; 58.4% female); 8,424 (40.9%) had a total hip replacement. Sensitivity of the clinical registry data ranged from 16% (anemia) to more than 70% (diabetes, high blood pressure, rheumatoid arthritis); specificity was greater than 92% for all conditions, except back pain and high blood pressure. PPV ranged from 19% (back pain) to 83% (diabetes). Chance-adjusted agreement was very good for diabetes (kappa: 0.74), moderate for heart disease and high blood pressure (kappa range: 0.41-0.53) and poor or fair for back pain, anemia, cancer, depression, kidney disease, liver disease, rheumatoid arthritis and stomach ulcers (kappa range: 0.14-0.37). Estimates varied by sex (i.e., generally higher agreement for females) and age (i.e., generally lower agreement for older age groups), but not joint type. Conclusion/ImplicationsSelf-reported health conditions in registry data had good validity for conditions with clear diagnostic criteria, but low validity for conditions that are difficult to diagnose or rare (e.g., cancer). Linked registry and administrative data is strongly recommended to ensure valid and accurate comorbidity measures when developiong risk prediction models and conducting inter-jurisdictional comparisons of patient-reported outcome measures.

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