bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying Prenatal Opioid Exposure in Health Administrative Data for Public Health Surveillance and Epidemiologic Research
Authors: Camden Andi , To Teresa , Ray Joel G , Gomes Tara , Bai Li , Fung Kinwah , Guttmann Astrid
Published: December 2020

IntroductionAccurate estimation of prenatal opioid exposure (POE) is needed for population-based surveillance & research but can be challenging with health administrative data due to varying definitions & methods. Prior research has relied primarily on infant records with a diagnosis of neonatal abstinence syndrome (NAS).

Objectives and Approach1) Evaluate the impact of using different definitions of maternal opioid use in the estimation of POE; 2) Investigate whether maternal characteristics vary by the type of definition used. Population-based cross-sectional study of all hospital births (N= 454,746) from 2014-2017 in Ontario, Canada. Multiple linked population-based health administrative databases were used to identify opioid-related pre- & perinatal Emergency Department visits & hospitalizations & opioid prescriptions. We examined how pre-conception & in-pregnancy maternal characteristics varied by using different approaches to ascertain POE.

ResultsThere were 9624 live/still births with POE. Ascertainment of POE was highest using maternal prescription drug data (79%) & infant hospital records with NAS (45%). Maternal characteristics varied by data source used for POE ascertainment. Opioid-related health care during pregnancy identified a high-risk phenotype, contrasted with those ascertained through prescription data, with respective rates of 64% vs. 54% for social assistance, 37% vs. 12% for polydrug use, 23% vs. 6% for alcohol use, 26% vs. 19% for 3+ live births, 13% vs. 5% for victim of violence, 12% vs. 6% for involvement in criminal justice system & 64% vs. 17% for mental health & addictions hospital care.

Conclusion / ImplicationsPOE ascertainment differs by health administrative data source & ability to link both across maternal records and with infant. Prescription drug data identified the highest number of opioid-exposed births and, with linked healthcare records, is useful to identify illicit opioid use & additional risk factors. Clinically meaningful differences in maternal characteristics of opioid users exist by POE ascertainment method.
Access to Linked Administrative Data Through an Indigenous Cultural Lens
Authors: McBeth Simon
Published: December 2020

IntroductionStats NZ provides the world-leading Integrated Data Infrastructure (IDI) to help improve New Zealanders’ lives. Ngā Tikanga Paihere (the Tikanga Framework) is a tool designed to ensure data use is carefully considered, and practice occurs in good faith. It also aims to build and maintain public trust and confidence in the way Stats NZ manages administrative data in the IDI. It draws on general customary concepts from Te Ao Māori (Māori world) and sits in alignment with the current model of the Five Safes Framework for access.
Objectives and ApproachTikanga are appropriate customary practices or ‘layers of the culture’ developed by Māori communities and individuals and informed by common cultural values and concepts.
Stats NZ applies the framework when reviewing applications to use data in the IDI and Longitudinal Business Database (LBD). The framework is used with the original ‘Five Safes’ framework in the review process to ensure that data will be used in an appropriate and collaborative way, without marginalising any specific populations. In research proposals relating specifically to Māori, researchers are asked to demonstrate what value their work will bring to Māori communities and how the researchers will work collaboratively with those communities.
ResultsUse of Ngā Tikanga Paihere has been well received. Stats NZ is now looking to broaden the use of Ngā Tikanga Paihere to assess all integrated data research proposals that focus on minority, identifiable, and marginalised populations.
Conclusion / ImplicationsWhile Ngā Tikanga Paihere does not resolve data governance or cultural licence issues, it encourages researchers to actively engage and collaborate with Māori groups when these communities are potentially impacted, or when the project might use data about these communities.
Stats NZ sees data governance and cultural licence matters as an ongoing conversation between agencies and Treaty/Te Tiriti partners. Ngā Tikanga Paihere should be a tool to help guide the government data system to work with Māori data ‘in good faith’.
An overview of Integrated Data at Stats NZ
Authors: Cutting Celeste
Published: December 2020

IntroductionStats NZ works with a range of organisations to provide the world-leading Integrated Data Infrastructure (IDI) to help improve New Zealanders’ lives. Stats NZ’s integrated data is a critical tool in the New Zealand government’s efforts to reduce poverty, improve health, and provide a growing, inclusive economy. It enables researchers to gain evidence-based insights, and to measure the effectiveness of government initiatives and services.
Objectives and ApproachThe IDI contains de-identified longitudinal unit record data about people and households. Access is provided when projects meet the ‘five safes’ framework, which ensures the data is kept safe. As well as this, the project must meet the standards of Ngā Tikanga Paihere, a customs-based framework that applies a Te Ao Māori lens to managing IDI access. We also produce the Longitudinal Business Database (LBD) which contains de-identified longitudinal microdata about businesses.
ResultsThe IDI has proved to be a vital tool to assist with decision making and insights on topical areas. There are currently approximately 550 researchers using the IDI for 280 different research projects. These researchers are from government agencies, universities and non-government organisations studying issues like vulnerable children, education and employment outcomes, impact of health conditions, business productivity and more.
Conclusion / ImplicationsThe number of projects and users continues to increase, with a large increase to the number of users projected for 2020/2021. The way our customers use data has changed since the establishment of the IDI, and the broader demand for data-driven evidence has with it. We are making changes to ensure that we can cope with increasing demand and ensure that those that need to access our services can.
Stats NZ Integrated Data Infrastructure (IDI) – Back to the Future: How a prototype design developed into a world leading tool for critical decision making
Authors: Strijbis Grace
Published: December 2020

IntroductionStats NZ maintains and provides access to the Integrated Data Infrastructure (IDI). This is a critical tool in the government's efforts to reduce poverty, improve health, and support a growing, inclusive economy. Insights are used at all levels of government decision making.

Objectives and ApproachSince the IDI prototype was created in 2011, it has grown rapidly. The use cases for the IDI continue to develop, with more complex projects that extend the boundaries of what might be considered traditional research. The Ministry of Education is now running a key school funding model, the Equity Index, out of the IDI. We are seeing an increased interest for the IDI to support fast responses to policy questions. Statistical production areas in and outside Stats NZ increasingly need administrative data for production, and the IDI is the best source of relevant linked administrative data.

ResultsStats NZ has received one-off funding to continue sustainable expansion of the IDI and improve the data and services it provides. This ‘IDI 2’ project will enhance the IDI, making it easier, quicker and more stable for our customers to engage with data. We have focussed on strengthening, and futureproofing, our core offering. The IDI 2 project is currently looking to replace the systems that are used to build our databases and streamline production to provide the technical capability to cope with a much higher volume of data.

Conclusion / ImplicationsWhile the IDI 2 project looks to shore up the infrastructure and lay a platform for future development, Stats NZ seeks to undertake customer focussed improvements to be responsive to other customer needs. There are opportunities to grow the integrated data community and provide more ways for researchers to connect with each other.
Do Home Adaptations Prevent Falls for Older People?
Authors: Hollinghurst Joe , Fry Richard , Akbari Ashley , Williams Neil , Hillcoat-Nalletamby Sarah , Lyons Ronan A , Watkins Alan , Clegg Andrew , Rodgers Sarah E
Published: December 2020

IntroductionAround a third of people aged 65+, and around half of people aged 80+ fall at least once a year in the United Kingdom. Homes may be adapted to try to prevent falls, but evidence on the effectiveness of home adaptations is limited.

Objectives and ApproachOur objective was to determine if proactive (Care&Repair service) and reactive (rapid response) home adaptations provided by Care&Repair Cymru resulted in a reduced risk of a fall for older people aged 60+ in Wales.

We constructed a longitudinal dataset from the Secure Anonymised Information Linkage Databank. We created quarterly intervals for 5-years pre and post the date a home adaptation was received, or a randomly assigned date for the comparator. Per quarter, we created a binary indicator of whether someone had a fall at home that was recorded in either the emergency department or hospital admission data sources for Wales. We included key demographic variables as covariates. We analysed the data using logistic regression and a difference-in-difference approach.

ResultsWe analysed 634,046 individuals, of whom 60,794 received the proactive Care&Repair service, and 47,244 received the reactive rapid response service. People receiving proactive or reactive home adaptations from Care & Repair were at around a two-fold increased risk of falling, compared to those who did not receive home adaptation with Odds Ratios (ORs) of 2.13 [95%CI: 2.07, 2.20] and 2.73 [2.64,2.81] respectively. Falls risk increased per quarter for all individuals, but after intervention delivery, the rate of increase fell in the intervention groups.

ConclusionPeople receiving home adaptations from Care&Repair had a higher chance of a fall, indicating the service was successfully identifying those in need. Falls risk increased for everyone over time, but this was counteracted for people receiving an intervention.
The New Brunswick Act Respecting Research Three Years Later: A Data Trickle Turns into a Flood
Authors: McDonald Ted
Published: December 2020

IntroductionIn New Brunswick Canada (NB) in 2017, the Provincial Government passed a bill called the Act Respecting Research. This Act took the form of an omnibus bill modifying 20 pieces of legislation to define a legal authority for the New Brunswick Institute for Research, Data and Training (NB-IRDT) to receive prepared, linkable microdata from multiple public bodies in NB. This was followed in 2019 by a second Act Respecting Research that broadened the scope of the first. Together this legislation has helped to expand NB-IRDT’s data holdings from its first dataset – hospital records received in 2015 – to over 40 different linkable datasets as of March 2020. These datasets are underpinning ambitious research partnerships between NB-IRDT and the Provincial Government.

Objectives and ApproachThis presentation will detail the rapid progress made since the first Act was passed in 2017 and presented at IPDLN-2018. It will outline enabling factors, including secured funding, the central role of the Department of Health, engagement with senior decisionmakers, and interaction with other provincial data centres and national networks including HDRN Canada. Ongoing and new challenges arising from the rapid increase in the scale of data collection and their resolution will be discussed.

ResultsIn addition to a wide range of provincial health administrative and clinical datasets, recent datasets include school records (report cards, standardized testing, attendance), income support data, workers compensation claims data, higher education program and graduation data, court appearances, adult training/retraining programs and immigration landing records. All files are linkable at the individual level. Multi-year research projects to support program evaluation are underway, with departments now able to access other agency data through NB-IRDT.

Conclusion / ImplicationsNB-IRDT and the experience in NB offer important lessons for other jurisdictions aiming to expand access to linkable multi-agency data for research and evaluation.
Using Logistic Regression to Estimate the False Positive Rate in The IDI (SoLinks)
Authors: Lin Anna , Song Soon , Wang Nancy
Published: December 2020

IntroductionStats NZ’s Integrated Data Infrastructure (IDI) is a linked longitudinal database combining administrative and survey data. Previously, false positive linkages (FP) in the IDI were assessed by clerical review of a sample of linked records, which was time consuming and subject to inconsistency.

Objectives and ApproachA modelled approach, ‘SoLinks’ has been developed in order to automate the FP estimation process for the IDI. It uses a logistic regression model to calculate the probability that a given link is a true match. The model is based on the agreement types defined for four key linking variables – first name, last name, sex, and date of birth. Exemptions have been given to some specific types of links that we believe to be high quality true matches. The training data used to estimate the model parameters was based on the outcomes of the clerical review process over several years.

ResultsWe have compared the FP rates estimated through clerical review to the ones estimated through the SoLinks model. Some SoLinks estimates fall outside the 95% confidence intervals of the clerically reviewed ones. This may be the result of the pre-defined probabilities for the specific types of links are too high.

ConclusionThe automation of FP checking has saved analyst time and resource. The modelled FP estimates have been more stable across time than the previous clerical reviews. As this model estimates the probability of a true match at the individual link level, we may provide this probability to researchers so that they can calculate linked quality indicators for their research populations.
Comparison of Time-Varying and Landmark Analysis Methods in Estimating the Association Between Medication Adherence and Outcomes: A Heart Failure Cohort
Authors: Qin Xiwen Simon , Knuiman Matthew W , Hung Joseph , Briffa Tom , Teng Tiew-Hwa Katherine , Sanfilippo Frank M
Published: December 2020

IntroductionMedication adherence is associated with a reduction of adverse outcomes in heart failure (HF). However, this association is complex to estimate accurately because adherence (exposure) can vary during the follow-up period. Adherence can be estimated as a fixed exposure to predict outcomes, and this is known as a landmark analysis. In contrast, adherence can also be estimated as a dynamic exposure which varies over time in the follow-up period. This is known as a time-varying analysis and is expected to be the more precise method.

Objectives and ApproachWe compared these two methods in a HF cohort. We identified a population-based cohort of 3619 heart failure patients, aged 65-84 years hospitalised in Western Australia from 2003-2007 and who survived to 1-year post-discharge (landmark date). Adherence to renin-angiotensin system inhibitors (RASI) and β-blockers was calculated using proportion of days covered (PDC) expressed either as a fixed time exposure (in landmark analysis) or a varying exposure (in time-dependent analysis). The latter was updated every 30 days after the landmark date. Cox regression models were used to investigate the association between adherence and all-cause death at 1- and 3-years post-landmark date.

ResultsFor 1-year outcomes, hazard ratios (HR) for every 10% increase in PDC were similar between models from landmark analyses (RASI adherence: 0.93, 0.90-0.97; β-blocker adherence: 0.96, 0.92-1.0) and time-dependent analyses (RASI adherence: 0.94, 0.91-0.97; β-blockers adherence: 0.95, 0.92 -0.99). However, 95% confidence intervals estimated from time-dependent models were narrower than those from landmark analyses. HRs were slightly closer to the null when estimated from time-dependent models. A similar pattern was seen with 3-year outcomes.

Conclusion / ImplicationsTime-dependent analysis of adherence-outcome associations results in more precise estimates of hazard ratios. Estimates of HRs from landmark analysis models were similar but usually lower than those from time-dependent models.
One-Year Readmission and Emergency Department Presentation After an Epilepsy-Related Admission in People with Intellectual Disability: A Registry-Linkage Study
Authors: Liao Peiwen
Published: December 2020

IntroductionIntellectual disability (ID) is a neurodevelopmental condition that affects approximately 1-2% of the population, and epilepsy is a common comorbidity in people with ID. Although hospital admission for epilepsy is also common, little is known about the impact of ID on healthcare use following an epilepsy admission.

Objectives and ApproachUsing linked administrative health datasets that included hospital admissions and disability service data, we aimed to examine whether the presence of ID led to greater or lesser use of healthcare services following an epilepsy admission, as represented by all-cause unplanned readmissions and emergency department (ED) presentations. Comparing the rate of readmissions and ED presentations within 30, 90 and 365 days of the first epilepsy admission during the study period, the effect of ID was assessed using Poisson regression.

ResultsA total of 18,987 patients had an epilepsy admission between 2005 and 2014, and of these, 3,256 (17.1%) had ID. Compared to patients without ID, patients with ID had a higher risk of unplanned readmissions within each follow-up period (adjusted incidence rate ratio (IRR) with 95% CI: 30 days: 1.48 (1.34, 1.65); 90 days: 1.42 (1.31, 1.54); 365 days: 1.49 (1.40, 1.59)). Differences were also found in the reasons for readmission, including more readmissions for neurological disorders. Similarly, the ED presentation risk was elevated in patients with ID (adjusted IRR: 30 days: 1.34 (1.23, 1.46); 90 days: 1.33 (1.24, 1.42); 365 days: 1.38 (1.30, 1.46)).

Conclusion / ImplicationsIn patients with epilepsy, the presence of ID appears to increase the chance of a readmission or ED presentation following a hospital admission for epilepsy, with the reasons for readmission also potentially different. This suggests the potential for improvements in post-admission screening and management.
A Linked Data Analysis Exploring the Real-World Burden and Hospital-Related Cost of Sarcoma in Western Australia: 1982 – 2016
Authors: Wright Cameron , Halkett Georgia , Carey Smith Richard , Moorin Rachael
Published: December 2020

IntroductionBecause sarcomas are a rare group of malignancies, centralised referral and management improves outcomes. To plan centralised care, an understanding of disease burden is essential. Epidemiological studies on sarcoma from Australia are lacking, as previous studies have focused on a sarcoma type (e.g. soft tissue) or anatomical sites. As notification of all cancer diagnoses is required in Western Australia (WA), routinely collected data provides a platform to improve understanding of the sarcoma burden and associated cost.

Objectives and ApproachThis study aimed to determine incidence, prevalence, relative survival and cost of cancer-related hospitalisation for sarcoma in WA. Routinely linked cancer registry, hospital morbidity and death registration data from 1982 to 2016 were used. All new sarcoma cases among WA residents were included, using the Information Network on Rare Cancers (RARECARENet) definitions. To provide a reference point, comparisons were made with female breast, colorectal, prostate and lung cancers.

ResultsThe age-standardised incidence and prevalence of STS increased over time, while bone sarcoma remained more stable. For 2012-16, the combined sarcoma crude annual incidence was 7.3 per 100,000, with the majority of these soft tissue sarcoma (STS, incidence of 5.9 per 100,000). Five-year relative survival for the period 2012-16 for STS was 65% for STS, while five-year relative survival was 71% for bone sarcoma. This was higher than lung cancer, but lower than breast, colorectal and prostate cancer. Cancer-related hospitalisations cost an estimated $(Australian) 29.1 million over the period.

Conclusion / ImplicationsThe analysis of health service use showed sarcoma had a lower mean episode of cancer-related hospitalisation compared to the reference cancers in 2016, but the mean cost per prevalent person was higher for sarcoma than for female breast, colorectal and prostate cancer. STS incidence increased over time in WA, with an increasing proportion of people diagnosed aged ≥65 years.
Housing Insecurity Over Time: Exploratory Analysis of Repeat Housing-Service Use Within A Single Local Government Area in Wales
Authors: Thomas Ian , Mackie Peter
Published: December 2020

IntroductionIn their 2019 homelessness strategy, Welsh Government make the commitment for a Wales where homelessness is a rare experience, and if it does occur is brief and non-recurrent. However, measuring repeated homelessness/housing issues (or ‘non-recurrence’) has thus far not been possible in Wales, with only aggregate data collected nationally on interactions between people and local government housing-services. Drawing on recently available individual level data from a single local government area in Wales, we set out to explore repeated use of housing/homelessness services.

Objectives and ApproachThe sample used in this analysis relates to approximately 6,000 individuals who sought assistance from Swansea local government housing-service during a four-year period, from January 2012 to December 2015. In order to group people with similar epeated interactions with the housing service, we use analyses that firstly aggregate the total time spent interacting with the service and over how many distinct periods (Cluster analysis), and secondly explore patterns in sequences of month-by-month service use histories (Sequence Analysis).

ResultsWe describe the patterns of repeat housing-service use identified through the Cluster Analysis and the Sequence Analysis, and the characteristics of each group. We also directly compare the ‘time-aggregating’ and ‘time-patterned’ based approaches to classifying people, in order to illustrate the differences that methodology can bring to our conceptualisation of repeat housing service use.

Conclusion / ImplicationsThe findings from this study have the potential to inform Wales’ move away from aggregate to individual level national homelessness data collection, by illustrating how individual level data can be used to provide new insights into homelessness. Exploring different conceptualisations of repeated housing-service use can also inform future developments in the measurement of repeat housing problems, and therefore monitor progress in achieving a Wales where homelessness is rare, brief, and non-recurrent.
Shared Priorities, Data and Reporting: Improving Access to Mental Health, Addictions and Home Care Services
Authors: Morris Kathleen , Diverty Brent , Damiano Natalie
Published: December 2020

IntroductionAround the world, the need for mental health, addictions and home care services is growing. Government commitment and collective efforts to bridge data gaps, develop indicators and publicly report results are key elements in Canada’s efforts to improve access.

Objectives and ApproachThis symposium will demonstrate how a coalition of stakeholders united to use real-world data to measure progress and drive change. Each presentation highlights a different aspect of the project with participant interaction, aiming for the Canadian context to spark knowledge exchange across sectors and countries:

Presentation 1 - Coalitions and consensus (10 min.): processes and engagement for successful collaboration between governments, providers, measurement experts and people with lived experiences to select and develop indicators

Facilitated Q&A (5 min.)

Presentation 2 - Standards and data infrastructure (10 min.): new standards to enhance data comparability and strengthened data infrastructure to support measurement and reporting

Facilitated Q&A (5 min.)

Presentation 3 - Indicator development (10 min.): the indicator development cycle, methodological approaches using linked and partial data, and development strategies for new concepts

Facilitated Q&A (5 min.)

Presentation 4 - Public reporting and policy impact (10 min.): describes how public reporting supports sustained commitments and energizes change using targeted tools and messages

Facilitated Q&A (15 min.)

ResultsPublic reporting began in 2019, with 3 new indicators released annually over 4 years. Initial reporting provides a baseline to track improvements, and a starting point for health system planners to learn from peers across Canada. The indicators have been a catalyst to fill important data gaps in emergency and home care services.

Conclusion / ImplicationsThrough shared priorities, coalitions and linked data, information gaps are being filled to drive advancements in access to mental health and addictions services, and home care. Lessons learned in Canada can be adapted internationally to galvanize needed improvements in these sectors.
Application of A Framework to Guide Policy on Healthcare Resource Allocation Decisions to Data Linkage Projects
Authors: Olver Ian
Published: December 2020

IntroductionData linkage of population data sets often across jurisdictions or linking health data sets or health data with non-health data often involves balancing ethical principles such as privacy with beneficence as represented by the public good. Similar ethical dilemmas occur in health resource allocation decisions. The NHMRC have published a framework to guide policy on health resource allocation decisions that could be applied to ensure the justification of data linkage projects that is defensible as in the interest of the public good.

Objectives and ApproachThe four main conditions for legitimacy of policy decisions about access to healthcare in a democracy with a public health system and limited resources wereexamined for their relevance to decisions about the use of public data and linking data sets.

ResultsPublic policy decisions must be defensible and responsive to the interests of those affected. Decision-makers should articulate their reasoning and recommendations so that citizens can judge them. While the context of policy decisions will differ, their legitimacy depends upon (1) the transparency of the reasoning which should be free from conflicts of interest, the basis for decisions recorded and report widely, (2) the accountability of the decision-makers to the wider community, (3) the testability of the evidence used to inform the decision-making, which usually means that it will stand up to independent review and(4) the inclusive recognition of those the decision affects which often requires that the implications for disadvantaged groups are considered, even if they can’t always be accommodated. These conditions are interrelated but ensure that the good of society in general and not just specific dominant groups are accommodated.

Conclusion / ImplicationsIt these principles are applied to decisions about data linkage projects they have clear applicability in society accepting data linkage projects having balanced the good against the ethical risks involved.
Defining Populations Within Linked Administrative Data
Authors: Wijnberg Duncan
Published: December 2020

This presentation will outline methods developed by the ABS to define populations within linked administrative data. The methods developed use a combination of both direct and indirect signals (referred to as ‘signs of life’) to infer whether individuals are in a particular population at a given point in time. Data from multiple government sources are used.

IntroductionLinked administrative datasets hold significant potential to unlock new insights and understanding about different populations of interest. A key challenge is to create a research dataset that is properly representative of the population of interest at a given point in time. Without a representative population to serve as the basis of analysis, research outcomes are harder to interpret and compare against those from other populations. This has flow on consequences for any research findings derived from linked admin data.

Objectives and ApproachThis project sought to develop methods to define a representative Australian population from the Multi Agency Data Integration Project (MADIP) data asset. The core of the asset is created through a three-way linkage between Australian Medicare, Social Security and Taxation datasets. Together, these datasets have very high coverage of the Australian population and enable high quality linkage of other datasets into the asset. A ‘signs of life’ approach was used that sought to distinguish a representative population at a given point in time from the MADIP asset.

ResultsAn experimental representative population has been developed from the linkage spine that closely approximates the national age distribution and other breakdowns of the ABS’s Estimated Resident Population.

Conclusion / ImplicationsThis work demonstrates one approach that can be used to derive useful analytical populations from linked datasets with overly exhaustive scopes.
Sepsis within 30 days following elective surgeries in NSW public hospitals, January 2015-December 2017
Authors: Marashi-Pour Sadaf , Watson Diane E
Published: December 2020

IntroductionPostsurgical sepsis is a common complication, accounting for one-third of all sepsis cases and associated with higher risk of mortality.

Objectives and ApproachThe Bureau of Health Information (BHI) in New South Wales (NSW), Australia produces independent reports and information about the performance of the healthcare system. Our report investigates the rate of sepsis within 30-days following surgery, and explores variation in risk standardised postoperative sepsis across NSW public hospitals. The study cohort and outcome definitions were drawn from existing international patient safety indicators, modified for appropriate use with Australian linked hospital and mortality data to identify patients transferred, or readmitted to any NSW public or private hospital with postoperative sepsis. Fine and Gray competing risks regression models were used to calculate risk-adjusted rates, and funnel plots were used to identify outliers.

ResultsRate of 30-day postoperative sepsis was 0.9% among 66,143 adult elective surgeries that met the inclusion criteria during January 2015 to December 2017. Results showed around 33% of post-operative sepsis were identified as occurring during either a transfer or readmission to hospital. Hospital-level unadjusted rates of postsurgical sepsis ranged from 0% to 2% across more than 80 NSW public hospitals. The majority of hospitals (94%) had postoperative sepsis results lower or no different than expected.

Conclusion/ImplicationsWe explored postoperative sepsis to help understanding of sepsis risk during the follow up period. Results showed a considerable number of postoperative sepsis cases would not have been identified without the use of linked data. Results on hospital risk standardised 30-day postoperative sepsis may prove useful as a screening tool for quality improvement in this area.
Hospital level risk-standardised all-cause readmission and returns to acute care following hospitalisations for eight clinical conditions
Authors: Marashi-Pour Sadaf , Watson Diane E
Published: December 2020

IntroductionMeasures that assess how healthcare affects health outcomes make an important contribution to efforts to inform local efforts to improve care for patients in NSW.

Objectives and ApproachThe Bureau of Health Information (BHI) in New South Wales, Australia produces independent reports and information about the performance of the healthcare system. Our reports on readmission and returns to acute care following hospitalisation from eight clinical conditions investigated variation across more than 70 NSW public hospitals, with information available for 2000 to 2018.

Readmissions included both returns to acute care from non-acute inpatient settings and readmissions to any public or private hospital within 30-days or 60-days following hospital discharge. Risk standardised readmission ratios (RSRR) were calculated following linkage of admitted patient and mortality data, to also account for competing risk of death. Hospital outliers were identified using funnel plots. A sensitivity analysis explored the effect of residence in an aged care facility.

ResultsIn the most recent period, the unadjusted rate of readmission ranged from 10% for ischaemic stroke to 22% for congestive heart failure with rates remaining relatively stable over time for most of the condition. The proportion of readmissions that were returns to acute care from a non-acute hospitalisation ranged from 2% for chronic obstructive pulmonary disease (COPD) to 42% for hip fracture surgery. Across all eight conditions majority of hospitals had results lower or no different than expected. Number of hospitals with higher than expected readmissions ranged from none for total knee replacement to six for COPD. Taking into account residence in an aged care facility did not impact the results in a meaningful way.

Conclusion/ImplicationsThe RSRR method compares a hospital’s readmissions given its case-mix with an average NSW hospital with the same case-mix. In NSW, this method is a screening tool, identifying areas of excellence and of potential improvement.
Evaluating PPRL Vs Clear Text Linkage with Real-World Data
Authors: Jarrett Michael , Hills Brent , Zhao Yinshan , Brown Adrian , Randall Sean , Boyd James , Ferrante Anna , McGrail Kimberlyn
Published: December 2020

IntroductionPrivacy-preserving Record Linkage (PPRL) is a record linkage technique that can increase the security of personal information. PPRL uses techniques of either hashing identifiers (where exact matches are required) or Blooming identifiers (where partial matches are of interest before they are provided for linkage.

Objectives and ApproachWe use LinXmart software to evaluate performance of PPRL linkage compared to linkage using clear text identifiers. The test linkage dataset is one that is routinely linked (N=2,672,257) at our linkage centre. The population spine (N=8,440,442) includes a record for every person who has resided in British Columbia, Canada over the past 30 years. Weights were determined using LinXmart’s implementation of the Expectation Maximization (EM) algorithm. For both linkages, accepted links were the highest-weighted candidate link with a weight above the threshold suggested by EM estimation. We compare linkage rates and quality and differences in weight and threshold estimations between clear-text and PPRL linkages

ResultsClear-text and PPRL methods resulted in 97% and 90% linkage rates, respectively. Approximately 67% of records in the linked datasets contained a nominally unique ID. Records with a unique ID linked at higher rates (>99% for both clear-text and PPRL) while the linkage rate for records missing the ID differed substantially (92% /70% for clear-text/PPRL). Comparing PPRL linkage to the clear-text linkage, we obtain F-measures of 0.99 and 0.80 for records with and without the unique ID, respectively.

Conclusion / ImplicationsLinkage performance may be attributable to differences in comparison operators between the two methods. Bloomed fields compared with Dice coefficient allow for partial matching but may not be as sensitive as clear-text string comparisons. Numerical comparisons in PPRL are exact matches while clear-text comparisons allow for more sophisticated matching. Further refinements in PPRL are being explored to improve these results.
Child Protection Inequalities in New Zealand: Exploring Relationships with Social Gradient Using National Linked Administrative Datasets
Authors: Davie Gabrielle , Keddell Emily , Barson Dave
Published: December 2020

IntroductionContact with child protection systems are a key site of the expression of social inequalities. ‘Child welfare inequalities’ by deprivation have been documented in a number of countries including the United Kingdom. The size and nature of this relationship is sparse in the New Zealand system context.
Objectives and ApproachThe integration of data routinely collected by NZ’s Ministry for Children into Statistics NZ’s Integrated Data Infrastructure (IDI) provided the opportunity to examine how child protection data relates to deprivation and location. Anonymised person-level data in the IDI was used to obtain a population-based retrospective sample of children estimated to be resident in NZ in 2013/2014 and less than 17 years of age. Using pre-linked data in the IDI, all children with at least one of three child protection outcomes in 2013/2014 were ascertained.
Deprivation was assessed using a small-area level national index derived from census data. The most recent residential address recorded in the IDI was used to assign deprivation deciles to each child based on the small area in which they lived.
ResultsOf 1,016,928 children, 13,851 had had at least one substantiation of interest, the least serious of the outcomes considered. Compared to children living in the least deprived quintile of small areas, children in the most deprived quintile had 13.1 times (95%CI for Incidence Rate Ratio 12.0, 14.4) the rate of substantiation, 18.0 times (95%CI 15.4, 20.8) the rate of a family group conference, and 6.5 times (95%CI 5.6, 7.6) their rate of placement in foster care.
Conclusion / ImplicationsA marked relationship was observed between small-area deprivation and child protection contact. Action is needed to address the causes of deprivation, provide services that respond to families living in poverty, and examine carefully the interactions between demand and supply of services across deprivation levels.
Supporting Transition from Out of Home Care to Adulthood: Linked Data Is Central to The Compass Social Impact Bond
Authors: Williams Sharon , Sipthorp Mark , Ivkovic Nicholas , Inglis Alexander
Published: December 2020

The COMPASS program supports young people to successfully transition from Out of Home Care to adulthood. COMPASS is a social impact bond partnership between Victorian government, not-for-profit organisations and investors. COMPASS uses linked data in every phase from design, to implementation, to outcome measurement.

IntroductionStudies demonstrates that young care leavers experience significantly poorer outcomes than their peers. COMPASS is a preventative program progressivelyproviding 200 care leavers with access to housing and individualised support. The Centre for Victorian Data Linkage (CVDL) developed the Victorian Linkage Map (VLM) in 2016, linking 20 plus health and human services datasets with births and deaths data. Linked data provides a critical evidence base for COMPASS.

Objectives and ApproachThe presentation describes application of linked data for COMPASS design, implementation and measurement. Linked data cohort analysis of post-care service use of 6000 young people informed the program design. Linked data also provides the basis for a stratification tool to measure the complexity profile of referrals. Health, housing and justice payable outcomes are measured by comparing relative performance of participants with a matched control group using linked data.

ResultsImplementation of COMPASS has highlighted the value of linked data in service design and measurement of payable outcomes. It has also highlighted the challenges of using linked data in a real-world environment, including the need for thorough documentation and testing of specifications, calculations and processes.

Conclusion / ImplicationsThe use of linked data for COMPASS provides a model for evidence-based service design and tests the use of linked data for robust and sustainable outcome measurement. The lessons from COMPASS are applicable to other social impact bonds and service implementation and outcome measurement more broadly.
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort
Authors: Barr Margo , Welberry Heidi , Finch Julie , Blunden Lou Anne
Published: December 2020

IntroductionIt is well established that mental illness is associated with poor physical health and chronic diseases possibly related to high levels of risk factors such as smoking, physical inactivity, poor diet and alcohol consumption. There is also evidence to suggest that having a mental illness may be related to poorer management of chronic diseases. Health service providers wanted to investigate this in Central and Eastern Sydney (CES).

Objectives and ApproachThe purpose of this study was to scope the feasibility of identifying a mental illness cohort using CES residents from the 45 and Up Study (n=30,049) linked to administrative health datasets. These included Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) provided by Services Australia and hospital admissions provided by the NSW Centre for Health Record Linkage. We then compared differences in the identified cohort size, characteristics and the 8-year mortality rates.

ResultsUsing only hospitalisation data, 6% of the CES cohort were identified as having a mental illness, compared to 17% using MBS data only, 26% PBS data only, 35%using both the MBS and PBS data and 36% using all of the data sources. Crude mortality was 58% in those identified in the hospitalisation data, 27% based on the PBS data,10% using MBS data, 21% using the MBS and PBS data and 23% based on all combined sources.

Conclusion / ImplicationsWe decided that the most appropriate option was to include the MBS, PBS and hospitalisation data to identify the mental illness cohort. This cohort will now be used to examine difference in the management of chronic disease, such as care plans and cycles of care, between those who do and do not have a mental illness.
Guidelines for Data Linkage in Pharmacoepidemiology: Assessing Feasibility, Evaluating Quality and Transparent Reporting
Authors: Pratt Nicole , Chun Danielle , Davis Kourtney , Hammill Brad , Hampp Christian , Mack Christina , Meyer Anne-Marie , Raman Sudha , Rivera Donna , Setoguchi Soko , Stürmer Til , Lund Jennifer
Published: December 2020

IntroductionIncreasingly in pharmacoepidemiology, linking is required to enrich analytic data to more accurately define study populations, enable adjustment for confounding, and improve capture of health outcomes. When creating such novel linked datasets, researchers should consider their suitability to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections exist. Additionally, while the RECORD-PE guidelines assist in the reporting of studies using observational health data specific to pharmacoepidemiology, they do not address the unique requirements for transparent evaluation and reporting of the data linkage process.

Objectives and ApproachWe aimed to 1) provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiological research and 2) generate a checklist with specific recommendations to assist researchers in providing clear and transparent assessment of the linkage process. To develop these guidelines, a working group comprised of members of the International Society of harmacoepidemiology was formed. Recommendations were open for comment by Society members and endorsed by the Society.

ResultsGuidance for feasibility assessment was categorized into five domains: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. A checklist for evaluation and reporting of data-linkage processes covered five domains including; (1) data sources; (2) linkage variables; (3) linkage methods; (4) linkage results; and (5) linkage evaluation, including validation and verification of the resulting linked data.

Conclusion/ImplicationsOur guidelines for data linkage feasibility assessment and reporting can be used to inform the design of sustainable linked data resources and for transparent communication of linkage processes. Together, these guidelines will help various stakeholders to critically assess the potential for bias in research based on linked data and help generate actionable evidence.
Enhancing Joint Replacement Outcomes Through Registry Linkage with National Health Administrative Data in Australia
Authors: Duszynski Katherine , Graves Stephen E , Pratt Nicole , Inacio Maria , De Steiger Richard , Harris Ian , Ackerman Ilana , Jorm Louisa , Lorimer Michelle , Gulyani Aarti
Published: December 2020

IntroductionMonitoring of joint replacement (JR) data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) has reduced revision rates and improved surgical practice. Outcome assessment post-arthroplasty is limited however, to revision (reoperation) surgery and mortality outcomes. The AOANJRR National Data Linkage project seeks to broaden the scope of outcomes investigation in Australia by linking registry and health administrative datasets.

Objectives and ApproachUsing linked registry and administrative data, the project seeks to describe and quantify national/regional trends and variation in major complications (infection, dislocation, arthrofibrosis, chronic pain, venous thromboembolism, cardiac events), malignancy and health service utilisation (readmissions, emergency encounters and inpatient rehabilitation) following hip, knee and shoulder joint replacement surgery. Evidence will be generated on how these outcomes are associated with and vary according to patient, surgical, implant, hospital and pharmacological factors.

As Australia lacks a national identifier, seven linkage agencies are probabilistically linking AOANJRR hip, knee and shoulder replacement data (1999-2017) with 20 datasets. Datasets include government-subsidised health services, procedural and prescription data. Hospital separations and emergency attendance data from Australia’s eight jurisdictions together with national cancer registry and rehabilitation service data are also planned for linkage. Linked data are maintained in a secure remote access computing environment.

ResultsTo date, national Medicare Benefits Schedule, Pharmaceutical Benefits Scheme and the Australian Cancer Database data have been linked with >900,000 AOANJRR patients, representing 607.6 million health service records (1999-2018), 467.7 million prescriptions (2002-2018) and 184,000 cancer records, respectively. Remaining linked data will be available in mid-2020. Some initial summary results across a selected range of studies will be presented.

Conclusion / ImplicationsThis national data-linkage program will identify areas for improvement in joint replacement surgery and modifiable risk factors contributing to poor patient outcomes.