Data Linkage in Australia: The First 50 Years

Police and hospital data linkage for traffic injury surveillance: A systematic review

This systematic review examines studies of traffic injury that involved linkage of police crash data and hospital data and were published from 1994 to 2023 worldwide in English. Inclusion and exclusion criteria were the basis for selecting papers from PubMed, Web of Science, and Scopus, and for identifying additional relevant papers using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and supplementary snowballing (n = 60). The selected papers were reviewed in terms of research objectives, data items and sample size included, temporal and spatial coverage, linkage methods and software tools, as well as linkage rates and most significant findings. Many studies found that the number of clinically significant road injury cases was much higher according to hospital data than crash data. Under-estimation of cases in crash data differs by road user type, pedestrian cases commonly being highly under-counted. A limited number of the papers were from low- and middle-income countries. The papers reviewed lack consistency in what was reported and how, which limited comparability.

The knowledge and reuse practices of researchers utilising government health information assets, Victoria, Australia, 2008-2020

BACKGROUND

Using government health datasets for secondary purposes is widespread; however, little is known on researchers' knowledge and reuse practices within Australia.

OBJECTIVES

To explore researchers' knowledge and experience of governance processes, and their data reuse practices, when using Victorian government health datasets for research between 2008-2020.

METHOD

A cross-sectional quantitative survey was conducted with authors who utilised selected Victorian, Australia, government health datasets for peer-reviewed research published between 2008-2020. Information was collected on researchers': data reuse practices; knowledge of government health information assets; perceptions of data trustworthiness for reuse; and demographic characteristics.

RESULTS

When researchers used government health datasets, 45% linked their data, 45% found the data access process easy and 27% found it difficult. Government-curated datasets were significantly more difficult to access compared to other-agency curated datasets (p = 0.009). Many respondents received their data in less than six months (58%), in aggregated or de-identified form (76%). Most reported performing their own data validation checks (70%). To assist in data reuse, almost 71% of researchers utilised (or created) contextual documentation, 69% a data dictionary, and 62% limitations documentation. Almost 20% of respondents were not aware if data quality information existed for the dataset they had accessed. Researchers reported data was managed by custodians with rigorous confidentiality/privacy processes (94%) and good data quality processes (76%), yet half lacked knowledge of what these processes entailed. Many respondents (78%) were unaware if dataset owners had obtained consent from the dataset subjects for research applications of the data.

CONCLUSION

Confidentiality/privacy processes and quality control activities undertaken by data custodians were well-regarded. Many respondents included data linkage to additional government datasets in their research. Ease of data access was variable. Some documentation types were well provided and used, but improvement is required for the provision of data quality statements and limitations documentation. Provision of information on participants' informed consent in a dataset is required.

The burdensome logistics of data linkage in Australia - the example of a national registry for congenital heart disease

Objective Data linkage is a very powerful research tool in epidemiology, however, establishing this can be a lengthy and intensive process. This paper reports on the complex landscape of conducting data linkage projects in Australia. Methods We reviewed the processes, required documentation, and applications required to conduct multi-jurisdictional data linkage across Australia, in 2023. Results Obtaining the necessary approvals to conduct linkage will likely take nearly 2 years (estimated 730 days, including 605 days from initial submission to obtaining all ethical approvals and an estimated further 125 days for the issuance of unexpected additionally required approvals). Ethical review for linkage projects ranged from 51 to 128 days from submission to ethical approval, and applications consisted of 9-25 documents. Conclusions Major obstacles to conducting multi-jurisdictional data linkage included the complexity of the process, and substantial time and financial costs. The process was characterised by inefficiencies at several levels, reduplication, and a lack of any key accountabilities for timely performance of processes. Data linkage is an invaluable resource for epidemiological research. Further streamlining, establishing accountability, and greater collaboration between jurisdictions is needed to ensure data linkage is both accessible and feasible to researchers.

Does Legislation Impede Data Sharing in Australia Across Institutions and Jurisdictions? A Scoping Review

In Australia, regulations governing data, including formal legislation and policies promulgated by private and public agencies, are often seen as a barrier to data sharing. This sharing can include between institutions, as well as across jurisdictional borders in a federated jurisdiction such as Australia. In some cases, these regulations place a barrier to sharing data across borders or between institutions without a prerequisite requirement. In other cases, these regulations may be perceived as a justification not to share data. The objective of this review was to analyse published literature from Australia to see what regulations were used to justify not sharing data, along with any other factors that might discourage data sharing. We searched PubMed, Scopus and Web of Science for empirical and policy articles discussing data sharing in Australia. We then filtered these results via abstract and conducted a full text assessment to include 33 articles for analysis. Although there are a few areas of notable regulatory divergence with respect to legislation governing health data, most regulations in Australia are relatively consistent. Further, the absence of uniform ethics approval between sites in different states was frequently cited as a barrier to data sharing.

Demographic, health, and prognostic characteristics of Australians with liver cancer: a cohort study of linked data in New South Wales for informing cancer control

BACKGROUND

Australian age-standardized incidence and death rates for liver cancer are lower than world averages, but increasing as in other economically advanced western countries. World Health Organization emphasizes the need to address sociodemographic disparities in cancer risk. A more detailed sociodemographic risk profiling was undertaken for liver cancer in New South Wales (NSW) by diagnostic stage, than possible with NSW Cancer Registry (NSWCR) alone, by incorporating linked data from the Australian Bureau of Statistics (ABS). The purpose was to inform targeting and monitoring of cancer services.

METHODS

The ABS manages the Multi-Agency Data Integration Project (MADIP) which includes a wide range of health, educational, welfare, census, and employment data. These data were linked at person level to NSWCR liver cancer registrations for the period post 2016 census to December 2018. De-identified data were analyzed. Sex-specific age-adjusted odds ratios (95%CIs) of liver cancer were derived using logistic regression by age, country of birth, residential remoteness, proficiency in spoken English, household income, employment status, occupation type, educational attainment, sole person household, joblessness, socioeconomic status, disability status, multimorbidity, and other health-related factors, including GP consultations. These data complement the less detailed sociodemographic data available from the NSWCR, with alignment of numerators and population denominators for accurate risk assessment.

RESULTS

Results indicate liver cancer disproportionately affects population members already experiencing excess social and health disadvantage. Examples where 95% confidence intervals of odds ratios of liver cancer were elevated included having poor English-speaking proficiency, limited education, housing authority tenancy, living in sole-person households, having disabilities, multiple medicated conditions, and being carers of people with a disability. Also, odds of liver cancer were higher in more remote regions outside major cities, and in males, with higher odds of more advanced cancer stages (degrees of spread) at diagnosis in more remote regions.

CONCLUSIONS

Linked data enabled more detailed risk profiling than previously possible. This will support the targeting of cancer services and benchmarking.

The Australian Genetic Heart Disease Registry: Protocol for a Data Linkage Study

BACKGROUND

Genetic heart diseases such as hypertrophic cardiomyopathy can cause significant morbidity and mortality, ranging from syncope, chest pain, and palpitations to heart failure and sudden cardiac death. These diseases are inherited in an autosomal dominant fashion, meaning family members of affected individuals have a 1 in 2 chance of also inheriting the disease ("at-risk relatives"). The health care use patterns of individuals with a genetic heart disease, including emergency department presentations and hospital admissions, are poorly understood. By linking genetic heart disease registry data to routinely collected health data, we aim to provide a more comprehensive clinical data set to examine the burden of disease on individuals, families, and health care systems.

OBJECTIVE

The objective of this study is to link the Australian Genetic Heart Disease (AGHD) Registry with routinely collected whole-population health data sets to investigate the health care use of individuals with a genetic heart disease and their at-risk relatives. This linked data set will allow for the investigation of differences in outcomes and health care use due to disease, sex, socioeconomic status, and other factors.

METHODS

The AGHD Registry is a nationwide data set that began in 2007 and aims to recruit individuals with a genetic heart disease and their family members. In this study, demographic, clinical, and genetic data (available from 2007 to 2019) for AGHD Registry participants and at-risk relatives residing in New South Wales (NSW), Australia, were linked to routinely collected health data. These data included NSW-based data sets covering hospitalizations (2001-2019), emergency department presentations (2005-2019), and both state-wide and national mortality registries (2007-2019). The linkage was performed by the Centre for Health Record Linkage. Investigations stratifying by diagnosis, age, sex, socioeconomic status, and gene status will be undertaken and reported using descriptive statistics.

RESULTS

NSW AGHD Registry participants were linked to routinely collected health data sets using probabilistic matching (November 2019). Of 1720 AGHD Registry participants, 1384 had linkages with 11,610 hospital records, 7032 emergency department records, and 60 death records. Data assessment and harmonization were performed, and descriptive data analysis is underway.

CONCLUSIONS

We intend to provide insights into the health care use patterns of individuals with a genetic heart disease and their at-risk relatives, including frequency of hospital admissions and differences due to factors such as disease, sex, and socioeconomic status. Identifying disparities and potential barriers to care may highlight specific health care needs (eg, between sexes) and factors impacting health care access and use.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48636.

Evaluating data quality in the Australian and New Zealand dialysis and transplant registry using administrative hospital admission datasets and data-linkage

Background: Clinical quality registries provide rich and useful data for clinical quality monitoring and research purposes but are susceptible to data quality issues that can impact their usage. Objective: This study assessed the concordance between comorbidities recorded in the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry and those in state-based hospital admission datasets. Method: All patients in New South Wales, South Australia, Tasmania, Victoria and Western Australia recorded in ANZDATA as requiring chronic kidney replacement therapy (KRT) between 01/07/2000 and 31/12/2015 were linked with state-based hospital admission datasets. Coronary artery disease, diabetes mellitus, cerebrovascular disease, chronic lung disease and peripheral vascular disease recorded in ANZDATA at each annual census date were compared overall, over time and between different KRT modalities to comorbidities recorded in hospital admission datasets, as defined by the International Classification of Diseases (ICD-10-AM), using both the kappa statistic and logistic regression analysis. Results: 29, 334 patients with 207,369 hospital admissions were identified. Comparison was made at census date for every patient comparison. Overall agreement was "very good" for diabetes mellitus (92%, k = 0.84) and "poor" to "fair" (21-61%, k = 0.02-0.22) for others. Diabetes mellitus recording had the highest accuracy (sensitivity 93% (±SE 0.2) and specificity 93% (±SE 0.2)), and cerebrovascular disease had the lowest (sensitivity 54% (±SE 0.2) and specificity 21% (±SE 0.3)). The false positive rates for cerebrovascular disease, peripheral vascular disease and chronic airway disease ranged between 18 and 33%. The probability of a false positive was lowest for kidney transplant patients for all comorbidities and highest for patients on haemodialysis. Conclusions and Implications: Agreement between the clinical quality registry and hospital admission datasets was variable, with the prevalence of comorbidities being higher in ANZDATA.

De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation

BACKGROUND

Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier.

METHODS

We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage.

RESULTS

The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband's presence in the sample database with an area under the receiver operating curve of 0.997-0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ. Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931-0.994), and the misidentification rate was 0.00249 (range 0.00123-0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language.

CONCLUSIONS

Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available.

The application of spatial measures to analyse health service accessibility in Australia: a systematic review and recommendations for future practice

BACKGROUND

Australia's inequitable distribution of health services is well documented. Spatial access relates to the geographic limitations affecting the availability and accessibility of healthcare practitioners and services. Issues associated with spatial access are often influenced by Australia's vast landmass, challenging environments, uneven population concentration, and sparsely distributed populations in rural and remote areas. Measuring access contributes to a broader understanding of the performance of health systems, particularly in rural/remote areas. This systematic review synthesises the evidence identifying what spatial measures and geographic classifications are used and how they are applied in the Australian peer-reviewed literature.

METHODS

A systematic search of peer-reviewed literature published between 2002 and 2022 was undertaken using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Search terms were derived from three major topics, including: [1] Australian population; [2] spatial analysis of health service accessibility; and [3] objective physical access measures.

RESULTS

Database searches retrieved 1,381 unique records. Records were screened for eligibility, resulting in 82 articles for inclusion. Most articles analysed access to primary health services (n = 50; 61%), followed by specialist care (n = 17; 21%), hospital services (n = 12; 15%), and health promotion and prevention (n = 3; 4%). The geographic scope of the 82 articles included national (n = 33; 40%), state (n = 27; 33%), metropolitan (n = 18; 22%), and specified regional / rural /remote area (n = 4; 5%). Most articles used distance-based physical access measures, including travel time (n = 30; 37%) and travel distance along a road network (n = 21; 26%), and Euclidean distance (n = 24; 29%).

CONCLUSION

This review is the first comprehensive systematic review to synthesise the evidence on how spatial measures have been applied to measure health service accessibility in the Australian context over the past two decades. Objective and transparent access measures that are fit for purpose are imperative to address persistent health inequities and inform equitable resource distribution and evidence-based policymaking.

Record linkage for routinely collected health data in an African health information exchange

Introduction

The Patient Master Index (PMI) plays an important role in management of patient information and epidemiological research, and the availability of unique patient identifiers improves the accuracy when linking patient records across disparate datasets. In our environment, however, a unique identifier is seldom present in all datasets containing patient information. Quasi identifiers are used to attempt to link patient records but sometimes present higher risk of over-linking. Data quality and completeness thus affect the ability to make correct linkages.

Aim

This paper describes the record linkage system that is currently implemented at the Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, and assesses its output to date.

Methods

We apply a stepwise deterministic record linkage approach to link patient data that are routinely collected from health information systems in the Western Cape province of South Africa. Variables used in the linkage process include South African National Identity number (RSA ID), date of birth, year of birth, month of birth, day of birth, residential address and contact information. Descriptive analyses are used to estimate the level and extent of duplication in the provincial PMI.

Results

The percentage of duplicates in the provincial PMI lies between 10% and 20%. Duplicates mainly arise from spelling errors, and surname and first names carry most of the errors, with the first names and surname being different for the same individual in approximately 22% of duplicates. The RSA ID is the variable mostly affected by poor completeness with less than 30% of the records having an RSA ID.The current linkage algorithm requires refinement as it makes use of algorithms that have been developed and validated on anglicised names which might not work well for local names. Linkage is also affected by data quality-related issues that are associated with the routine nature of the data which often make it difficult to validate and enforce integrity at the point of data capture.

Improving Cohort-Hospital Matching Accuracy through Standardization and Validation of Participant Identifiable Information

Linking very large, consented birth cohorts to birthing hospitals clinical data could elucidate the lifecourse outcomes of health care and exposures during the pregnancy, birth and newborn periods. Unfortunately, cohort personally identifiable information (PII) often does not include unique identifier numbers, presenting matching challenges. To develop optimized cohort matching to birthing hospital clinical records, this pilot drew on a one-year (December 2020-December 2021) cohort for a single Australian birthing hospital participating in the whole-of-state Generation Victoria (GenV) study. For 1819 consented mother-baby pairs and 58 additional babies (whose mothers were not themselves participating), we tested the accuracy and effort of various approaches to matching. We selected demographic variables drawn from names, DOB, sex, telephone, address (and birth order for multiple births). After variable standardization and validation, accuracy rose from 10% to 99% using a deterministic-rule-based approach in 10 steps. Using cohort-specific modifications of the Australian Statistical Linkage Key (SLK-581), it took only 3 steps to reach 97% (SLK-5881) and 98% (SLK-5881.1) accuracy. We conclude that our SLK-5881 process could safely and efficiently achieve high accuracy at the population level for future birth cohort-birth hospital matching in the absence of unique identifier numbers.

Health Statistics in Australia: What We Know and Do Not Know

Australia is a federation of six states and two territories (the States). These eight governmental entities share responsibility for health and health services with the Australian Government. Mortality statistics, including causes of death, have been collected since the late 19th century, with national data produced by the (now) Australian Bureau of Statistics (ABS) from 1907. Each State introduced hospital in-patient statistics, assisted by State offices of the ABS. Beginning in the 1970s, the ABS conducts regular health surveys, including specific collections on Aboriginal and Torres Strait Islander peoples. Overall, Australia now has a comprehensive array of health statistics, published regularly without political or commercial interference. Privacy and confidentiality are guaranteed by legislation. Data linkage has grown and become widespread. However, there are gaps, as papers in this issue demonstrate. Most notably, data on primary care patients and encounters reveal stark gaps. This paper accompanies a range of papers from expert authors across the health statistics spectrum in Australia. It is hoped that the collection of papers will inform interested readers and stand as a comprehensive review of the strengths and weaknesses of Australian health statistics in the early 2020s.