Linkage of health and aged care service events: comparing linkage and event selection methods

<p>Abstract</p> <p>Background</p> <p>Data linkage is a technique that has long been used to connect information across several disparate data sources – most commonly for medical and population health research. Often the purpose is to connect data for individuals over extended time periods or across different service settings, and so person-based linkage using detailed personal information is preferred. Linkage which aims to link connected events, on the other hand, requires information about the time and place of the event as well as the person or persons involved in that event in order to make high quality linkages.</p> <p>This paper describes the detailed process of event linkage and compares directly an event-based linkage method for identifying transition events between two care sectors in Australia with a well-established high quality longitudinal person-based linkage which facilitates identification of event data for individuals.</p> <p>Methods</p> <p>Direct comparisons are made between transition events identified using an event-based linkage and an existing person-based linkage for people moving from hospital into aged care in Western Australia. Several aspects of event-based linkage are examined: refinement of the strategy to reduce false positives, causes of false positives and false negatives, quality of the linked event dataset, and utility of the linked event dataset for transition analysis.</p> <p>Results</p> <p>Over 97% of the event-based links were among those selected using the person-based linkage and over 90% of the latter were identified by the event-based method, with the remainder missed mostly due to differences in reported event date or residential region. Consequently the two linked datasets were sufficiently similar to give very similar results for analyses, but the actual volume of movement from hospital to RAC was underestimated by about 10% by the event-based method.</p> <p>Conclusion</p> <p>This project has allowed a 'preferred event' event-based linkage strategy to be selected and deployed across Australia to study movements from hospital to residential aged care facilities using databases in which only limited personal information is available, but event dates and details can be readily accessed. The utility of this approach in other transition situations depends on the volume of movement and the accuracy of recording information in each setting.</p

Unpacking analyses relying on area-based data: are the assumptions supportable?

BACKGROUND: In the absence in the major Australian administrative health record collections of a direct measure of the socioeconomic status of the individual about whom the event is recorded, analysis of the association between the health status, use of health services and socioeconomic status of the population relies an area-based measure of socioeconomic status. This paper explores the reliability of the area of address (at the levels typically available in administrative data collections) as a proxy measure for socioeconomic disadvantage. The Western Australian Data Linkage System was used to show the extent to which hospital inpatient separation rates for residents of Perth vary by socioeconomic status of area of residence, when calculated at various levels of aggregation of area, from smallest (Census Collection District) to largest (postcode areas and Statistical Local Areas). Results are also provided of the reliability, over time, of the address as a measure of socioeconomic status. RESULTS: There is a strong association between the socioeconomic status of the usual address of hospital inpatients at the smallest level in Perth, and weaker associations when the data are aggregated to larger areas. The analysis also shows that a higher proportion of people from the most disadvantaged areas are admitted to hospital than from the most well-off areas (13% more), and that these areas have more separations overall (47% more), as a result of larger numbers of multiple admissions. Of people admitted to hospital more than once in a five year period, four out of five had not moved address by the time of their second episode. Of those who moved, the most movement was within, or between, areas of similar socioeconomic status, with people from the most well off areas being the least likely to have moved. CONCLUSION: Postcode level and SLA level data provide a reliable, although understated, indication of socioeconomic disadvantage of area. The majority of Perth residents admitted to hospital in Western Australia had the same address when admitted again within five years. Of those who moved address, the majority had moved within, or between, areas of similar socioeconomic status. Access to data about individuals from the Western Australian Data Linkage System shows that more people from disadvantaged areas are admitted to a hospital, and that they have more episodes of hospitalisation. Were data to be available across Australia on a similar basis, it would be possible to undertake research of greater policy-relevance than is currently possible with the existing separations-based national database

No major association between TGFBR1*6A and prostate cancer

Prostate cancer is the most commonly diagnosed cancer in men and one of the leading causes of cancer deaths. There is strong genetic evidence indicating that a large proportion of prostate cancers are caused by heritable factors but the search for prostate cancer susceptibility genes has thus far remained elusive. TGFBR1*6A, a common hypomorphic variant of the type I Transforming Growth Factor Beta receptor, is emerging as a tumor susceptibility allele that predisposes to the development of breast, colon and ovarian cancer. The association with prostate cancer has not yet been explored. A total of 907 cases and controls from New York City were genotyped to test the hypothesis that TGFBR1*6A may contribute to the development of prostate cancer. TGFBR1*6A allelic frequency among cases (0.086) was slightly higher than among controls (0.080) but the differences in TGFBR1*6A genotype distribution between cases and controls did not reach statistical significance (p = 0.67). Our data suggest that TGFBR1*6A does not contribute to the development of prostate cancer

Linkage of health and aged care service events: comparing linkage and event selection methods-1

<p><b>Copyright information:</b></p><p>Taken from "Linkage of health and aged care service events: comparing linkage and event selection methods"</p><p>http://www.biomedcentral.com/1472-6963/8/149</p><p>BMC Health Services Research 2008;8():149-149.</p><p>Published online 17 Jul 2008</p><p>PMCID:PMC2488340.</p><p></p

The effect of cross-jurisdictional linked hospital and death data on estimating risk-adjusted grouped hospital standardised mortality ratios in Australia

ABSTRACT Objectives The Population Health Research Network (PHRN) was established to increase data linkage capacity in Australia. A proof of concept study investigating cross border hospital use and hospital mortality was undertaken to demonstrate the effectiveness of increased data linkage capacity in supporting nationally significant health research. The objective of this study was to evaluate whether cross-jurisdictional linkage of hospital and death records across Australian states could refine estimation of Hospital Standardised Mortality Ratios (HSMRs). Approach In Australia, administrative hospital and death data are collected by individual state governments. The newly established Centre for Data Linkage created a cross-jurisdictional linkage key that brought together hospital and death records belonging to individuals across four Australian states over a five year period (1st July 2004 – 30th June 2009). Hospital inpatient records from public, psychiatric and private hospitals and private day surgery centres were provided by New South Wales, Western Australia and Queensland. South Australia provided public hospital inpatient records only. The linked data underwent extensive cleaning and standardisation to improve the validity of interstate comparisons. The final cohort comprised 7.7 million hospital patients. In-hospital deaths and deaths within 30 days of hospital discharge from the four state jurisdictions were used to estimate the SMR of hospital groups defined by geography and type of hospital (grouped HSMR) under three record linkage scenarios; 1) cross-jurisdictional person-level linkage, 2) within-jurisdictional (state-based) person-level linkage and 3) unlinked records. All public and private hospitals in New South Wales, Queensland, Western Australia and public hospitals in South Australia were included in this study. Death registrations from all four states were obtained from state-based registries of births, deaths and marriages. Results Cross-jurisdictional linkage identified 11,116 cross-border hospital transfers of which 170 resulted in a cross-border in-hospital death. An additional 496 cross-border deaths occurred within 30 day of hospital discharge. The inclusion of cross-jurisdictional person-level links to unlinked hospital records reduced the coefficient of variation amongst the grouped HSMRs from 0.19 to 0.15; the inclusion of 30 day deaths reduced the coefficient of variation further to 0.11. There were minor changes in grouped HSMRs between cross-jurisdictional and within-jurisdictional linkages, although the impact of cross-jurisdictional linkage increased when restricted to geographic regions with high cross-border hospital use such as the New South Wales and Queensland border area. Conclusion Cross-jurisdictional data linkage modified estimates of grouped HSMRs, particularly for hospitals groups that were likely to receive a high proportion of cross-border users