Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised.

ObjectiveTo count and characterise injuries resulting from legal intervention by US law enforcement personnel and injury ratios per 10 000 arrests or police stops, thus expanding discussion of excessive force by police beyond fatalities.DesignEcological.PopulationThose injured during US legal police intervention as recorded in 2012 Vital Statistics mortality census, 2012 Healthcare Cost and Utilization Project nationwide inpatient and emergency department samples, and two 2015 newspaper censuses of deaths.Exposure2012 and 2014 arrests from Federal Bureau of Investigation data adjusted for non-reporting jurisdictions; street stops and traffic stops that involved vehicle or occupant searches, without arrest, from the 2011 Police Public Contact Survey (PPCS), with the percentage breakdown by race computed from pooled 2005, 2008 and 2011 PPCS surveys due to small case counts.ResultsUS police killed or injured an estimated 55 400 people in 2012 (95% CI 47 050 to 63 740 for cases coded as police involved). Blacks, Native Americans and Hispanics had higher stop/arrest rates per 10 000 population than white non-Hispanics and Asians. On average, an estimated 1 in 291 stops/arrests resulted in hospital-treated injury or death of a suspect or bystander. Ratios of admitted and fatal injury due to legal police intervention per 10 000 stops/arrests did not differ significantly between racial/ethnic groups. Ratios rose with age, and were higher for men than women.ConclusionsHealthcare administrative data sets can inform public debate about injuries resulting from legal police intervention. Excess per capita death rates among blacks and youth at police hands are reflections of excess exposure. International Classification of Diseases legal intervention coding needs revision.

Findings of increased hospital admission rates, prolonged length of hospital stay and increased long-term mortality related to circulatory system diseases in the burn cohort provide evidence to support that burn has long-lasting systemic impacts on the heart and circulation.

BackgroundThe Centre for Data Linkage (CDL) has been established to enable national and cross-jurisdictional health-related research in Australia. It has been funded through the Population Health Research Network (PHRN), a national initiative established under the National Collaborative Research Infrastructure Strategy (NCRIS). This paper describes the development of the processes and methodology required to create cross-jurisdictional research infrastructure and enable aggregation of State and Territory linkages into a single linkage “map”.MethodsThe CDL has implemented a linkage model which incorporates best practice in data linkage and adheres to data integration principles set down by the Australian Government. Working closely with data custodians and State-based data linkage facilities, the CDL has designed and implemented a linkage system to enable research at national or cross-jurisdictional level. A secure operational environment has also been established with strong governance arrangements to maximise privacy and the confidentiality of data.ResultsThe development and implementation of a cross-jurisdictional linkage model overcomes a number of challenges associated with the federated nature of health data collections in Australia. The infrastructure expands Australia’s data linkage capability and provides opportunities for population-level research. The CDL linkage model, infrastructure architecture and governance arrangements are presented. The quality and capability of the new infrastructure is demonstrated through the conduct of data linkage for the first PHRN Proof of Concept Collaboration project, where more than 25 million records were successfully linked to a very high quality.ConclusionsThis infrastructure provides researchers and policy-makers with the ability to undertake linkage-based research that extends across jurisdictional boundaries. It represents an advance in Australia’s national data linkage capabilities and sets the scene for stronger government-research collaboration.

Background: Within the field of record linkage, numerous data cleaning and standardisation techniques are employed to ensure the highest quality of links. While these facilities are common in record linkage software packages and are regularly deployed across record linkage units, little work has been published demonstrating the impact of data cleaning on linkage quality. Methods: A range of cleaning techniques was applied to both a synthetically generated dataset and a large administrative dataset previously linked to a high standard. The effect of these changes on linkage quality was investigated using pairwise F-measure to determine quality. Results: Data cleaning made little difference to the overall linkage quality, with heavy cleaning leading to a decrease in quality. Further examination showed that decreases in linkage quality were due to cleaning techniques typically reducing the variability -although correct records were now more likely to match, incorrect records were also more likely to match, and these incorrect matches outweighed the correct matches, reducing quality overall. Conclusions: Data cleaning techniques have minimal effect on linkage quality. Care should be taken during the data cleaning process.