Suchergebnisse

ObjectiveFamilial relationships can provide researchers with important insight into genetic, environmental, and social influences across many domains of research. While most administrative datasets do not collect information about relationships, familial linkage is an approach which seeks to identify such relationships among individuals within a linked data environment. We sought to develop a familial linkage resource which permits researchers access to relationship information otherwise not available in unlinked disparate administrative collections.
ApproachLeveraging birth and marriage registration data, we sought to identify relationships between Victorians. Familial links were formed by summarising relationships that were either explicit in the data (e.g. Parent and Child), or implied (e.g. A parent of a parent is a grandparent). Resulting relationship data was stored in a data asset which interfaces with our linkage infrastructure for easy access and use by linked data end-users.
ResultsThrough familial linkage, we have identified over 8.8 million unique relationships for Victorians, spanning 24 relationship subtypes which include both biological and non-biological connections. This data can be linked to all administrative datasets within our linkage environment, however representation varies across sources. Overall, the highest coverage of known relationships is found in datasets which specialise in child services, while older Victorians remain a gap.
Conclusion and ImplicationsFamilial linkage offers new dimensions of insight to researchers than what is accessible in source data alone. This information enables our data end-users to gain critical insights into the complex interplay between biological and social influences on Victorians' health and well-being.

ObjectiveDemand for real-time data during the COVID-19 pandemic revealed a need to increase efficiencies in manual linkage processes to respond to events in near real-time. In response, our jurisdictional linkage agency transitioned from yearly to daily, weekly and monthly linkage practices through increasing automation and improving process flows.
ApproachOur linkage agency transitioned to a fully automated process utilising scalable cloud infrastructure. Source data is now provided directly to a common data platform. This data is split into linkage and content, cleansed and quality assured in Python and set to automatically run via Azure data pipelines. The data is then linked via a combination of deterministic and probabilistic criteria, with data quality checks automatically performed along the way. Researchers can analyse this data in a secure virtual machine that only they can access and retrieve data from.
Results This infrastructure expedites the data linkage process allowing daily linkage results to select datasets, and enables advanced research such as a predictive micro-simulation model, which leverages the platform to predict and intervene on outcomes influenced by governmental policies. This model relies on timely administrative data to build targeted interventions for groups with poor future outcomes, tests these interventions, and monitors outcomes in near real-time.
ConclusionOur linkage agency has transitioned from manual to automated linkage processes in response to increasing need for timely data. By embracing cloud infrastructure and leveraging automation, we have streamlined our operations, enabling responsive linkage depending on need and expediting the provision of de-identified, linked data to researchers.

ObjectivePoor quality data confounds efforts to link clients across datasets. To combat this, we have trialed an approach which seeks to identify linkage candidates using associations with related services. We utilised a machine learning (ML) approach to query linkage candidates' dataset associations and make predictions about whether candidates are likely to be a client of the service being integrated for linkage.
ApproachUtilizing a K-nearest neighbors algorithm, we trained a model using 15 variables to predict whether a linkage candidate is a client of a family service dataset. We then evaluated the model's success, and modified its native performance to minimise false positive matches. Subsequently, we tested the validity of these predictions as linkage criteria by employing blocking strategies and linking the service dataset to our linkage spine. The trained model was then used to identify correct links against the spine.
ResultsThe evaluation of the machine learning model yielded promising results, with high accuracy (88.5%) and precision (95.5%). Testing the predictions as linkage criteria resulted in highly accurate links ranging from 96.0% to 98.7% across different blocking strategies. Despite some records failing to establish any links to the spine, rates of false positive matches remained low (0.7% to 3.1%).
ConclusionMissingness and inaccuracy in data remains a key problem for data linkage, and a robust approach is required to resolve complex linkage cases. However, these findings suggest that machine learning can present novel options for a toolbox of many approaches to link problem records to a linkage spine.