OP20 Using cross-sectoral administrative data linkage to understand the health of people experiencing multiple exclusion

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.

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Linked government administrative data: Public or Private?

International Journal for Population Data Science ◽

10.23889/ijpds.v4i3.1173 ◽

2019 ◽

Vol 4 (3) ◽

Author(s):

Joel Stafford

Keyword(s):

Public Good ◽

Communication Networks ◽

Administrative Data ◽

Public Service ◽

Policy Development ◽

Data Linkage ◽

The Public ◽

Government Services ◽

Service Contracts ◽

Public Data

Background with rationaleIt is commonplace in policy discussions concerning administrative data linkage to presuppose that the data referred to is government services data. But this is not always the case. Much of the data public services hold is now collected via intermediaries, such as Non-Government Organisations, operating under service contracts with one or multiple government departments. Nor are these the only administrative data holdings applicable to clients of government services. There are also vast private administrative data holdings – including utility data, and consumer behaviour data. Creating and amending legislation that governs public service practices in this domain is increasingly made complex when private companies partner with governments agencies on policy development and evaluation work. Understanding the concept of public data for public good in light of this expanded sense of administrative data opens the door to deeper questions about the role linked data can play in government decision making. Main aimThe paper problematizes how legislation governing the linking of government administrative data is scoped and discusses how public service work can be affected by the opaque communication networks that increasingly span the public-private sector divide. Methods/ApproachAfter contextualising the challenge of legislating for administrative data linkage in the current work of the Office of the National Data Commissioner (ONDC) in Australia, this paper tests aspects of the proposed legislation against the extent to which it permits the possibility of ‘data laundering’. ResultsThe presentation demonstrates the need for greater sophistication in the specification of data linkage and sharing legislation in service of the public good. Conclusions This paper indicates that contemporary practices governing the linkage of government administrative data holdings is porous to the aims of extra-governmental organisations and may benefit by better incorporating legislative structures that govern private analytical services entities.

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The Office of Management and Budget: The Quarterback of Evidence-Based Policy in the Federal Government

The Annals of the American Academy of Political and Social Science ◽

10.1177/0002716218768440 ◽

2018 ◽

Vol 678 (1) ◽

pp. 112-123

Author(s):

Kathy Stack

Keyword(s):

Federal Government ◽

Social Services ◽

Administrative Data ◽

Data Linkage ◽

Executive Branch ◽

Federal Agencies ◽

Evidence Based ◽

Obama Administration ◽

Evaluation Capacity ◽

White House

During the Obama administration, the White House Office of Management and Budget’s (OMB) leadership helped to initiate and cement evidence-based policymaking reforms across the federal government, particularly in social services programs. Notable accomplishments were in the design of outcome-focused programs that use and build evidence, the strengthening of agency evaluation capacity, and interagency data-linkage projects to harness administrative data. Here, I review those accomplishments and catalog the key assets and tactics that OMB used to help federal agencies increase their use of evidence and innovation. I also assess the shortcomings and limitations of the Obama-era OMB approach and draw conclusions about what could be done in the current or a future administration to further advance evidence-based policymaking in the executive branch. Specifically, I propose that Congress and the administration should work to improve agency evaluation capacity, assess and report on agencies’ progress in using and building evidence, and establish an Intergovernmental Evidence and Innovation Council.

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Assessing Preventable Hospitalisation InDicators (APHID): protocol for a data-linkage study using cohort study and administrative data

BMJ Open ◽

10.1136/bmjopen-2012-002344 ◽

2012 ◽

Vol 2 (6) ◽

pp. e002344 ◽

Cited By ~ 8

Author(s):

Louisa R Jorm ◽

Alastair H Leyland ◽

Fiona M Blyth ◽

Robert F Elliott ◽

Kirsty M A Douglas ◽

...

Keyword(s):

Cohort Study ◽

Administrative Data ◽

Data Linkage ◽

Linkage Study ◽

Preventable Hospitalisation

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Data Resource: population level family justice administrative data with opportunities for data linkage

International Journal for Population Data Science ◽

10.23889/ijpds.v5i1.1339 ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Rhodri David Johnson ◽

Liz Trinder ◽

Simon Thompson ◽

Jon Smart ◽

Alexandra Lee ◽

...

Keyword(s):

Administrative Data ◽

Justice System ◽

Data Linkage ◽

Population Level ◽

Family Court ◽

Children And Families ◽

Research Opportunities ◽

Observatory Data ◽

The Family ◽

Outcomes For Children

Introduction Better use of administrative data is essential to enhance understanding about the family justice system, and characteristics and outcomes for children and families. The Nuffield Family Justice Observatory Data Partnership supports this aim through analyses of core family justice datasets. When a child is involved in family court proceedings in Wales, Cafcass Cymru are employed to represent a child’s best interests. This paper provides an overview of the Cafcass Cymru data, and linkage to population level health and other administrative datasets held within the Secure Anonymised Information Linkage (SAIL) Databank. Two data linkage example analyses are described. Further research opportunities are outlined. Methods Cafcass Cymru data was transferred to SAIL using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. Three cohorts were created: all individuals involved in family court applications; all individuals with an ALF allowing subsequent health data linkage; and all individuals with a Residential Anonymised Linking Field (RALF) and Lower Super Output Area (LSOA) enabling area level deprivation analysis. Results Cafcass Cymru data are available containing 12,745 public law applications between 2011 and 2019, with 52,023 applications from 2005 to 2019 for private law. The overall match rate was 80%, with variations observed by time, law type, roles, gender and age. Forty per cent had hospital inpatient admissions 2 years prior or after application receipt at Cafcass Cymru, of which 27% were for emergency admissions; 54% had an emergency department attendance and 61% an outpatient appointment during the same period. Individuals involved in public or private law applications were more likely to reside in deprived areas. Conclusion The Nuffield Family Justice Observatory Data Partnership will enhance research opportunities to better understand the family justice system and outcomes for children and families. Population level Cafcass Cymru data can be accessed through the SAIL Databank. Forthcoming data acquisition will also facilitate further analyses and insight.

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Detecting Care Homes: Using Residential Anonymous Linking Fields to Identify Care Homes in Administrative Data

International Journal for Population Data Science ◽

10.23889/ijpds.v3i2.483 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

Joe Hollinghurst ◽

Ashley Akbari ◽

Richard Fry ◽

Sarah Rodgers

Keyword(s):

Infectious Disease ◽

Antibiotic Resistance ◽

Administrative Data ◽

Vulnerable Populations ◽

Data Linkage ◽

Care Home ◽

Healthcare Research ◽

Sufficient Accuracy ◽

Rate Of Change ◽

Care Homes

BackgroundDemographic profiling is an important aspect of anonymised healthcare research used to identify populations of interest. Typically, administrative data is used in conjunction with patient registers to create cohorts, but it can be a time consuming process. ObjectivesWe aim to create and apply a method of identifying care homes using existing administrative data. We also aim to test the accuracy of our method by comparing the results to a pseudonymised national care home registry. This will allow us to prove whether proxy methods may be of sufficient accuracy for data linkage research in the future. Methods (including data)Our method uses quantifiable characteristics from longitudinal data to identify potential care homes. This includes the number and age of occupants, current residence and rate of change of occupancy. ConclusionsThis method is a reproducible process that would be of particular benefit for projects where a registry is not available, or where time or cost would limit the availability. This method can also be generalised to any communal establishment, where often the identification of vulnerable populations (antibiotic resistance, infectious disease etc.) is particularly beneficial.

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Measuring morbidity and mortality attributable to rheumatic heart disease in Fiji: a prototype study of disease burden in a developing country by data linkage

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.385 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Tom Parks ◽

Joseph Kado ◽

Isimeli Tukana ◽

Andrew Steer

Keyword(s):

Information System ◽

Developing Countries ◽

Heart Disease ◽

Developing Country ◽

Administrative Data ◽

Patient Information ◽

Disease Burden ◽

Posterior Probability ◽

Data Linkage ◽

Morbidity And Mortality

ABSTRACT ObjectivesRheumatic heart disease remains a major public health concern in developing countries. Motivated by the lack of up-to-date epidemiologic data from endemic settings, we sought to quantity morbidity and mortality attributable the condition in Fiji, a middle-income country where a high prevalence has consistently been reported. Having resolved to undertake the analysis using the existing routine clinical and administrative data at our disposal, we first set out to develop a data linkage procedure robust to the inherent limitations of data from low resource settings. ApproachRecords were available from four sources: an electronic patient information system, a database of death certificates, a disease control register, and echocardiography clinic registers. All referred to 2008-2012. Throughout the design and calibration process we used 1,406 known duplications in the patient information system from which we calculated the sensitivity and specificity. After cleaning, standardisation and preliminary blocking, we categorised identifiers including names, dates and demographics into agreement, partial agreement, disagreement or missing, accounting for issues such as out of order or misspelt names. After concentrating true matches by further blocking, we estimated match and nonmatch probabilities using expectation maximisation under the Fellegi-Sunter model of record linkage. We then derived the posterior match probability taking into consideration the size of block and prior information about the probability a match be present given the demographics of the individual concerned. In its final configuration, with record pairs considered a match if they achieved a posterior probability of over 50%, our procedure identified the known duplications with sensitivity of 91.4% and specificity of 99.9%. ResultsHaving identified 2,619 cases from the 1,773,999 records available, we used the linked data to make population-based estimates of prevalence using capture-recapture analyses and cause-specific mortality using relative survival methods, the first such estimates for a developing country. Moreover, in sensitivity analyses, we found that changing posterior probability threshold above which record pairs were considered a match had limited impact on the results. ConclusionAlthough data linkage is widely used for epidemiologic research in high-income settings, its application to developing countries has been limited. We developed and validated a data linkage procedure that can be used to turn largely unstudied routine clinical and administrative data into robust estimates of disease burden. With the growing availability of computerized data, we propose our approach has strong potential to assist the production of disease burden statistics in developing countries where civil registration systems are weak.

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Evolving population data linkage services to transform large-scale biobanking services

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.862 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Katie Irvine ◽

Vivienna Ong ◽

Simon Cooper ◽

Sarah Thackway

Keyword(s):

Longitudinal Data ◽

Health System ◽

Administrative Data ◽

Large Scale ◽

Data Linkage ◽

Service Providers ◽

Population Data ◽

Research Infrastructure ◽

Fast Tracking ◽

Linked Administrative Data

IntroductionMany population data linkage centres have been established to provide a mechanism for making linked administrative data available to approved third parties within robust governance frameworks. While current models support a wide variety of research, modifications are required for linked administrative data to better position biobanking research infrastructure. Objectives and ApproachWe have sought to reconfigure population data linkage services to enhance the value of a newly established state-of-the art population and disease biobank embedded within a state based pathology network, equipped with robotic technology, with the capacity to store and process more than 3 million samples from participants consenting to data linkage and future unspecified research. ResultsThree data service streams have been developed: longitudinal data linkage, cohort management and targeted recruitment. Traditional infrastructure for population data linkage will support the longitudinal data linkage stream, making data and biospecimens available for research, without direct patient identifiers. Technical and governance changes are necessary to enable the rapid release of contemporaneous patient and health system data for cohort management and recruitment purposes. The cohort management stream seeks to significantly reduce the manual follow-up of administrative data. The newly developed targeted recruitment service will leverage on the jurisdictional data holdings and structure of the health system and pathology network, to identify optimal sites and service providers for patient recruitment at scale, in an expedited manner. Conclusion/ImplicationsModest changes to population data infrastructure have significant potential to enhance biobank research infrastructure. By fast tracking biospecimen accrual for diseases of population subgroups of strategic importance, this new service is intended to promote biobank viability, accelerate the pace of clinical trials recruitment and improve patient access to trials.

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Using cross-sectoral data linkage to understand the health of people experiencing multiple exclusion

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.052 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

E Tweed ◽

A Leyland ◽

D Morrison ◽

S V Katikireddi

Keyword(s):

Administrative Data ◽

Data Linkage ◽

Support Service ◽

Service Planning ◽

Policy And Practice ◽

Opioid Substitution Therapy ◽

Case Register ◽

Mortality And Morbidity ◽

Individual Level ◽

Future Policy

Abstract Background People affected by the intersection of homelessness, drug use, and/or serious mental illness have high rates of mortality and morbidity. However, they are often missed from routine information sources on population health, such as surveys and censuses. In many countries, administrative data are available which could help address this knowledge gap. We created a novel virtual cohort using cross-sectoral data linkage in order to inform policy and practice responses to these co-occurring issues. Methods Individual-level data from local authority homelessness services (HL), opioid substitution therapy dispensing (OST), and a psychosis case register (PSY) in Glasgow, Scotland between 2011-15 were confidentially linked to National Health Service records, using a mix of probabilistic and deterministic linkage. A de-identified dataset was made available to researchers through a secure analysis platform. Demographic characteristics associated with different exposure combinations were analysed using descriptive statistics. Results Linkage created a cohort of 24,767 unique individuals with any one of the experiences of interest between 2011-15. Preliminary results suggest that 89.2% of the cohort had one experience; 10.6% two; and 0.2% all three. The most common combination was HL & OST (n = 2,150; 8.7%), with other combinations much less frequent (HL & PSY, n = 279, 1.1%; OST & PSY, n = 188, 0.8%; HL & OST & PSY, n = 51, 0.2%). The odds of male gender increased with number of exposures (2 exposures, OR 2.1, 95% CI 1.9-2.2; 3 exposures, OR 4.1, 95% CI 2.3-7.2), but there was little difference in age. Work is ongoing to incorporate into the cohort additional datasets on criminal justice involvement. Lessons Administrative data linkage is a feasible approach to understanding the health of people affected by multiple exclusionary processes, but requires robust and timely governance. Our initiative can support service planning and evaluation of future policy or service changes. Key messages We describe the creation and characteristics of a novel virtual cohort of people affected by multiple exclusionary processes, using record linkage of administrative datasets. Cross-sectoral linkage has international potential for enhancing public health intelligence, especially for population groups who may be missed from surveys and censuses.

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Primary care EMR and administrative data linkage in Alberta, Canada: describing the suitability for hypertension surveillance

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100161 ◽

2020 ◽

Vol 27 (3) ◽

pp. e100161

Author(s):

Stephanie Garies ◽

Erik Youngson ◽

Boglarka Soos ◽

Brian Forst ◽

Kimberley Duerksen ◽

...

Keyword(s):

Primary Care ◽

Administrative Data ◽

Rural Areas ◽

Data Linkage ◽

Population Level ◽

Primary Care Providers ◽

Sex Characteristics ◽

Care Providers ◽

Surveillance Network ◽

Discharge Database

ObjectiveTo describe the process for linking electronic medical record (EMR) and administrative data in Alberta and examine the advantages and limitations of utilising linked data for hypertension surveillance.MethodsDe-identified EMR data from 323 primary care providers contributing to the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Alberta were used. Mapping files from each contributing provider were generated from their EMR to facilitate linkage to administrative data within the provincial health data warehouse. Deterministic linkage was conducted using valid personal healthcare number (PHN) with age and/or sex. Characteristics of patients and providers in the linked cohort were compared with population-level sources. Criteria used to define hypertension in both sources were examined.ResultsData were successfully linked for 6307 hypertensive patients (96.2% of eligible patients) from 49 contributing providers. Non-linkages from invalid PHN (n=246) occurred more for deceased patients and those with fewer primary care encounters, with differences due to type of EMR and patient EMR status. The linked cohort had more patients who were female, >60 years and residing in rural areas compared to the provincial healthcare registry. Family physicians were more often female and medically trained in Canada compared to all physicians in Alberta. Most patients (>97%) had ≥1 record in the registry, pharmacy, emergency/ambulatory care and claims databases; 44.3% had ≥1 record in the hospital discharge database.ConclusionEMR-administrative data linkage has the potential to enhance hypertension surveillance. The current linkage process in Alberta is limited and subject to selection bias. Processes to address these deficiencies are under way.

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