bluuebunny/crossref_metadata_2025 huggingface dataset:
How do you measure up? Methods to assess linkage quality
Authors: Ferrante Anna , Boyd James , Randall Sean , Brown Adrian , Semmens James
Published: April 2017

ABSTRACT

ObjectivesRecord linkage is a powerful technique which transforms discrete episode data into longitudinal person-based records. These records enable the construction and analysis of complex pathways of health and disease progression, and service use. Achieving high linkage quality is essential for ensuring the quality and integrity of research based on linked data. The methods used to assess linkage quality will depend on the volume and characteristics of the datasets involved, the processes used for linkage and the additional information available for quality assessment. This paper proposes and evaluates two methods to routinely assess linkage quality.

ApproachLinkage units currently use a range of methods to measure, monitor and improve linkage quality; however, no common approach or standards exist. There is an urgent need to develop “best practices” in evaluating, reporting and benchmarking linkage quality. In assessing linkage quality, of primary interest is in knowing the number of true matches and non-matches identified as links and non-links. Any misclassification of matches within these groups introduces linkage errors. We present efforts to develop sharable methods to measure linkage quality in Australia. This includes a sampling-based method to estimate both precision (accuracy) and recall (sensitivity) following record linkage and a benchmarking method - a transparent and transportable methodology to benchmark the quality of linkages across different operational environments.

ResultsThe sampling-based method achieved estimates of linkage quality that were very close to actual linkage quality metrics. This method presents as a feasible means of accurately estimating matching quality and refining linkages in population level linkage studies. The benchmarking method provides a systematic approach to estimating linkage quality with a set of open and shareable datasets and a set of well-defined, established performance metrics. The method provides an opportunity to benchmark the linkage quality of different record linkage operations. Both methods have the potential to assess the inter-rater reliability of clerical reviews.

ConclusionsBoth methods produce reliable estimates of linkage quality enabling the exchange of information within and between linkage communities. It is important that researchers can assess risk in studies using record linkage techniques. Understanding the impact of linkage quality on research outputs highlights a need for standard methods to routinely measure linkage quality. These two methods provide a good start to the quality process, but it is important to identify standards and good practices in all parts of the linkage process (pre-processing, standardising activities, linkage, grouping and extracting).
Born a bit too early: A study of early planned birth and child development at school age
Authors: Bentley Jason , Roberts Christine , Bowen Jenny , Martin Andrew , Morris Jonathan , Nassar Natasha
Published: April 2017

ABSTRACT

ObjectivesFetal growth and development is a continuum with the optimal time of birth at 39-40 weeks gestation. Internationally, significant changes in clinical practice have seen planned birth (labour induction or pre­labour caesarean section) before 40 completed weeks gestation increase. Fetal brain development accelerates rapidly in the later stages of pregnancy from 32 weeks gestation, making it vulnerable to disruption from even slightly shortened gestation. This study aimed to investigate the association of gestational age and mode of birth with early childhood development.

ApproachThis study utilised a population-based record-linkage of administrative birth, hospital and development data to obtain a cohort of 153,730 live born infants in New South Wales, Australia. These infants were born between 2002 and 2007, with a gestational age of at least 32 weeks and had an early development assessment in their first year of school in 2009 or 2012. Childhood development was assessed in five main domains (physical health and wellbeing, language and cognitive skills, social competence, emotional maturity, and communication skills and general knowledge) with each comprised of a number of sub-domains. Children with a score below the 10th percentile nationally are considered developmentally vulnerable (DV) in that domain. Children who were DV for two or more of the five main domains are classified as developmentally high risk (DHR) and this was the primary study outcome. Robust multivariable Poisson models were used to obtain individual and combined relative risks for gestational age and mode of birth with DHR, adjusted for maternal demographic, socio-economic, perinatal, and child characteristics.

ResultsOverall, 9.6% of children were DHR. The adjusted relative risk (aRR) (95% confidence interval) of being DHR decreased with increasing gestational age (referent: 40 weeks); 32-33 weeks 1.25 (1.08-1.44), 34­-36 weeks 1.26 (1.18-1.34), 37 weeks 1.17 (1.10-1.25), 38 weeks 1.06 (1.01-­1.10), 39 weeks 0.98 (0.94-1.02), 41+ weeks 0.99 (0.94-­1.03) and for planned birth (referent: vaginal birth following spontaneous labour), 1.07 (1.04­-1.11). The aRR for planned birth at 37 weeks was 1.26 (1.18-­1.34) and at 38 weeks 1.13 (1.08-­1.19).

ConclusionEarly (<39 weeks gestation) planned birth is associated with an increased risk of poor development in children starting school. Given the timing of planned birth is modifiable, delaying birth for an additional week or more may improve child development. Strategies and interventions to inform more judicious decision making, weighing all the risks and benefits for early planned birth are required to ensure optimal child health and development.
The relationship between maternal race-ethnicity, immigrant status and country of birth and the risk of having a child with autism spectrum disorder in Western Australia
Authors: Fairthorne Jenny , De Klerk Nick , Leonard Helen
Published: April 2017

ABSTRACTBackgroundThe risk of autism spectrum disorder (ASD) varies by maternal race-ethnicity, immigrant status and birth region. Women from Asia and East Africa have had higher rates of ASD with ID in their children. We aimed to investigate the odds of ASD with ID in children born in Western Australia (WA) according to maternal race-ethnicity,country of birth and immigrant status.MethodsWe linked state registries and examined the odds of ASD with ID in children born in WA from 1994-2005, by maternal race-ethnicity, country of birth and immigrant status.ResultsCompared to Caucasian non-immigrant women immigrant women were 40% less likely, to have a child with ASD with ID. Women of Asian race-ethnicity from Central and South Asia were slightly more likely than Caucasian non-immigrant women to have a child with ASD with ID but children of women of Asian race-ethnicity from other parts of Asia had about half the odds of having ASD with ID. Black women from East Africa had more than three and a half times the odds of ASD with ID in their children.Conclusions Our results suggest an interaction effect between race-ethnicity, immigrant status and birth region, with Black East African women having highest odds of a child with ASD with ID. Research is implicated on specific risk and protective factors for ASD with ID in the children of immigrant women.
Diseases and Geographic Variation: Are we missing the whole picture?
Authors: John Gareth
Published: April 2017

ABSTRACT

BackgroundEpidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations. However, where an epidemiological analysis relies solely on data from large-scale anonymous administrative data sources, the available geographic information for the individuals concerned relates in general to just one single point in time; for example the places of residence of individual on the day that they were registered with a particular disease, or their places of residence on the day that they were admitted to hospital.

This information may not be sufficient however, especially when considering diseases where there may be a long period of time between an exposure to a particular hazard and the subsequent onset of disease.

MethodA solution to this problem possibly lies within administrative sources of data such as the Welsh Demographic Service (WDS), which is accessible to NHS Wales analysts and users of the SAIL databank in a pseudonymised format. The WDS contains the details of all Welsh residents who have been registered with a GP Practice since 1992, including a full history of changes to their addresses and GP practices. This data can be used to easily ascertain an individual’s address at any point in time, for example on a particular census date, or within a time period that is relative to a particular event, e.g. 10 years prior to disease registration. However, this research will look at how to incorporate all of an individual’s available address information into an epidemiological analysis.

ResultsTwo main approaches will be demonstrated; the first using a “Person Years at Risk” approach which attempts to apportion numerator and denominator according to the number of previous residences, and the second using a Case Control approach, comparing the geographic spread of addresses in the diseased group of patients versus the non-diseased (control) group, with age and gender matched controls also drawn from the WDS.
Early mortality from external causes in Aboriginal mothers: a retrospective cohort study
Authors: Fairthorne Jenny , De Klerk Nick , Leonard Helen
Published: April 2017

ABSTRACTBackground Maternal loss can have a deep-rooted impact on families. Whilst a disproportionate number of Aboriginal women die from potentially preventable causes, no research has investigated mortality in Aboriginal mothers. We aimed to examine the elevated mortality risk in Aboriginal mothers with a focus on external cause. MethodsWe linked data from four state administrative datasets to identify all women who had a child from 1983-2010 in Western Australia and ascertained their Aboriginality, socio-demographic details, and dates and causes of death of all mothers who had died prior to 2011. For Aboriginal mothers and compared to other mothers, we calculated the hazard ratios (HRs) for death by any external cause and each of the sub-categories of accident, suicide and homicide and the corresponding age of their youngest child.Results and discussionCompared to non-Aboriginal mothers and after adjustment, Aboriginal mothers were more likely to die from external cause [HR=4.61(95% CI: 3.7, 5.7)], accident [HR=4.74(95% CI: 3.2, 7.0)], suicide [HR=2.48(95% CI: 1.5, 4.0)] or homicide [HR=11.72(95% CI: 6.5, 21.0)]. For mothers experiencing death, the median age of the youngest child was 4.8 years. Aboriginal mothers are much more likely to die from external causes than non-Aboriginal mothers with the scale of difference ranging from 2.5 times for suicide, to more than eleven times for homicide. Comparisons with the extant literature suggest that the magnitude of ethnic disparities in mortality from external causes is greater among mothers than the female population overall, identifying Aboriginal mothers as a particularly at-risk group in both relative and absolute terms.ConclusionDuring the study period, Aboriginal mothers were more likely to die than other mothers and they usually left young children. Our increased adjusted HRs were only partly explained by socio-demographic circumstances. Further research is required to fully examine the risk factors associated with these potentially preventable deaths and to enable the development of informed health promotion to increase the life chances of Aboriginal mothers and their children.
Dementias Platform UK (DPUK) Portal
Authors: Thompson Simon
Published: April 2017

ABSTRACTBackgroundEstablished by the Medical Research Council (MRC) in June 2014, it is a £53 million collaboration between universities and drug companies to transform the best dementia research into the best treatments as quickly as possible.

DPUK will bring together 33 different dementia cohorts as well as imaging, brain bank, genomics and NLP.

MethodTo achieve this DPUK has a large number of work packages developing knowledge, capability and science. Work package 2 - DPUK Data portal has many cross cutting themes but its main aim is to provide a central access mechanism for the researchers to access the diverse datasets in a secure research platform which is delivered using the UK Secure e-Research Platform (UKSeRP).

The Portal supports the work of WP1 Cohort Profiling by providing information capture systems as well as automated publicly accessible summaries and more detailed reporting for DPUK members.

There are obvious sensitives around the sharing of the cohort datasets, especially given the many years of investment and commitment by the cohorts PI. However, many of these cohorts are no longer funded and risk of becoming obsolete and possibly being lost unless they become adopted by a programme such as DPUK. UKSeRP will provide a technical platform far more advanced than is often available to the cohorts and therefore the cohorts will benefit from these additional capabilities. The system leaves data ownership with the cohort PI and the governance structure puts them in the centre of the process and ensures that the cohort data is only used in projects that the cohorts are comfortable with.

The Portal supports the project application and approval process, and supports the governance structure and cohort engagement process. The application process is common regardless of the type of dataset required (phenotypic, genomic, imaging, physical samples)

ConclusionDPUK has extended UKSeRP to add the technical capabilities developed jointly with other work packages, most notably the addition of a fully federated image retrieval and analysis system and the genomics analysis system.

The platform will be fully operational by the end of the year.
The Australian Census Longitudinal Dataset 2006-2016: A Multi-Wave Approach to Maintaining a Longitudinal Dataset
Authors: Kindermann Bindi , Nunan Theresa , Hinde Sarah , Chipperfield James
Published: April 2017

ABSTRACT

ObjectivesThe Australian Census Longitudinal Dataset (ACLD) is a nationally representative longitudinal dataset increasingly used to study Australian social and economic transitions. The ACLD will become an even more powerful resource when it is extended to include linked records from the 2016 Australian Census of Population and Housing.

The ACLD 2006-2011 dataset consists of a 5% representative sample of 2006 Census records and the linked 2011 Census records, and has been a well-received product in Australia as our first longitudinal Census dataset. In the process of extending the dataset to include linked records from the 2016 Census, we are aiming to improve the dataset by addressing issues such as linkage error and the under representation of specific population groups, while also taking advantage of recent changes in data linking methodology and policy that enable us to conduct better quality linking.

ApproachA multi-wave framework will be implemented for maintaining and improving the ACLD sample (as an alternative to the popular top-up sampling approach). Under the multi-wave framework, a sample is selected from each Census and is the beginning of a new wave of the ACLD, to be linked to each following Census. Allowing the sample selection method to be different for each wave enables us to apply learnings from previous waves to selecting a better sample for the next. For example, we could design the 2011 Wave sample to over sample certain population groups which were under represented in the 2006-2011 linked sample. We could also adjust the sample size, or change the method of sample selection in reaction to changing data item response rates.

ResultsInitial analysis suggests the multi-wave approach will enable high quality analysis of transitions in hard to link and previously under represented population groups between 2011 and 2016. While these improvements cannot be retrospectively applied to the sample selected for the 2006 Wave, the linking strategy will also enable unbiased analysis of the Australian longitudinal population from 2006 to 2016.

ConclusionThe application of the multi-wave framework will enable the production of a high quality ACLD 2006-2016 product with minimal bias and improved sample representativeness, which can be used to explore changes in Australian society between the 2006, 2011 and 2016 Censuses. The flexibility of the multi-wave framework will allow continued improvement to the form and function of the ACLD in future waves.
Estimating the population effect of maternal alcohol use disorders on the educational achievement of children
Authors: Hafekost Katherine , Johnson Sarah , Bower Carol , Semmens James , O'Leary Colleen
Published: April 2017

ABSTRACTObjectivesPrevious research has identified that heavy maternal alcohol use impacts on a child’s health and development including poor cognitive and educational outcomes. However, very few studies have used objective measures of heavy alcohol-use and standardised school-based measures. Further, the magnitude of the effect of heavy maternal alcohol use on the educational outcomes of children in Australia is unknown. The primary aim of the project was to examine the association between in-utero and childhood exposure to maternal alcohol use disorder, which provides a proxy for heavy alcohol use, and children’s educational outcomes.

ApproachThis Western Australian population cohort study made use of linked administrative data to compare the educational outcomes of a cohort of exposed children born between 1989 and 2007 whose mother had an alcohol related diagnosis recorded on health datasets, with children whose mother did not have a diagnosis. The exposed cohort of mothers was frequency matched on maternal age within Indigenous status, and year of child’s birth with a comparison cohort of mothers without an alcohol-related diagnosis. Records were linked with education records up to 2011, which included the results of standardised state and national testing for children (ages 8-14), and school attendance data (ages 6-18). Mixed multivariate models were used to examine the relationship between exposure, and timing of exposure in relation to pregnancy, and the risk of failure to meet educational benchmarks for reading, writing, spelling, numeracy and school attendance. Separate analyses were run for Indigenous and non-Indigenous children.

ResultsA higher proportion of both Indigenous and non-Indigenous exposed children failed to reach minimum standards for all domains of testing compared to those in the unexposed cohort. The risk of failure in the exposed cohort remained significant with adjustment for a set of known confounders and there was no consistent relationship between timing of exposure and academic performance.

ConclusionThis project provides a unique view of how maternal alcohol use disorders affect a child’s educational outcomes. The use of linked administrative data overcomes the use of retrospective recall of past behaviour, and self-reports of drinking patterns which may be considered socially unacceptable. Results of this project indicate that children whose mothers have an alcohol use disorder are academically at risk. These results suggest that routine monitoring of maternal alcohol use, early identification of at-risk children and intervention at both the school and family level may assist vulnerable children to reach academic benchmarks.
Developing population segments with different levels of complexity and primary health care needs: An analysis using health administrative data in British Columbia, Canada
Authors: Langton Julia , Wong Sabrina , Peterson Sandra , McGrail Kim
Published: April 2017

ABSTRACTObjectivesPopulation subgroups can be been used organize health services and understand the quality of health care. Most commonly, populations are have been by specific diseases (e.g., health care received by diabetes patients), patient age (e.g., elderly populations), or life-stage (e.g., end-of-life care). However, these subgroups may not adequately capture the complexity and/or health care needs of different patient groups (e.g., multi-morbidity, frail elderly). Our objective is to use health administrative data to develop population segments based on patients’ primary health care needs. 

ApproachOur development process occurred in three stages. First, we examined examples of population segmentation in the peer reviewed and grey literature to develop principles for our population segments. Second, we held a workshop with primary care patients, decision-makers, clinicians and researchers to seek their input on important considerations for the population segments. Third, we used health administrative data (physician claims, hospitalisations) to develop population segments for the British Columbia (BC, Canada) population. Segments were based on diagnosis codes over a two year period; for each segment we examined health care use and costs, overall and by service type, in 2014-15. ResultsWe designed our segments to be mutually exclusive, capture the vast majority of people who use primary care services, and range from healthy patients (fewer primary care needs) to more complex patients (more extensive needs). Stakeholders were supportive of population segmentation approach and suggested incorporating patient vulnerability and primary care involvement such that segments would range from patients whose needs could be fully met in primary care to those who require additional services such as specialists/acute care. Our first iteration includes three segments: stable (≤1 chronic condition, needs met by primary care); multi-morbid (≥2 chronic conditions, needs mostly met by primary care); and complex (≤1 chronic condition and presence of a health care event associated with the management of this condition suggesting the patients’ needs not fully met by primary care). ConclusionWe developed population segments designed to account for patient complexity and primary health care needs; as such, segments provide more information than traditional indices of morbidity burden based on counts of chronic conditions. These segments will be used to report information on the quality of primary care. We plan to include conduct validation studies using additional variables (e.g, socio-economic factors, level of vulnerability from patient surveys) so that segments more accurately represent the level of complexity and patients’ primary health care needs.
Wait times and patterns of care in the colorectal cancer diagnostic interval
Authors: Webber Colleen , Flemming Jennifer , Birtwhistle Richard , Rosenberg Mark , Groome Patti
Published: April 2017

ABSTRACTObjectiveThere is concern that patients are waiting too long to be diagnosed with colorectal cancer (CRC) after presenting to the healthcare system. A prolonged time from first presentation to diagnosis, also known as the diagnostic interval, may be harmful to patients and indicate problems with the delivery of healthcare. The purpose of this study is to measure the length of the CRC diagnostic interval and describe variations in care that patients receive within the interval.

ApproachThis is a population-based, cross-sectional study of CRC patients diagnosed in Ontario, Canada between 2009 and 2012 using data from the Institute for Clinical Evaluative Sciences (ICES). The diagnostic interval will be measured using physician billing, hospital discharge, emergency room and registry data. Patients’ healthcare encounters in the 18 months before diagnosis will be analyzed using control charts to identify the earliest cancer-related encounter. The diagnostic interval will be defined as the date of this first relevant healthcare encounter to the CRC diagnosis date. Cluster analysis will be used to identify and characterize groups of patients with similar diagnostic intervals, based on the care received within the interval. Analyses will examine factors associated with the length of the diagnostic interval and care received within the diagnostic interval.

Results Analyses for this project are ongoing and will be complete by August 2016. Results from this study will describe the length of the CRC diagnostic interval and relevant sub-intervals, and variations in these intervals according to patient and clinical characteristics. Results will describe the care that patients received within the interval, including the number and types of tests received and physicians involved in the interval, and whether the care received in the interval was associated with how long patients wait for diagnosis.

ConclusionThe findings from this study will advance our understanding of the CRC diagnostic interval. The control chart methodology used to identify CRC-related healthcare encounters from administrative health data is an improvement on previous research that has used arbitrary time periods and encounters which likely underestimate the length of the diagnostic interval. The cluster analysis method is a novel approach to characterizing the diagnostic interval that will identify common patterns of care and diagnostic pathways using administrative health data. This study will provide population-level estimates of how long patients are waiting to be diagnosed with CRC and provide an understanding of how patterns of care influence the length of the diagnostic interval.
Multiple operating models for data linkage: A privacy positive
Authors: Irvine Katrina , Hollis Stephanie
Published: April 2017

ABSTRACTObjectivesThe data linkage landscape is rapidly evolving and a variety of operating models are used across dedicated data linkage centres. We have reviewed various operating models and redesigned the technical operating models for our multi-jurisdictional data linkage system to better manage new privacy risks, support data custodian participation and improve access timeframes for end users.

ApproachA steering committee with key data custodians was formed to oversee the review and assessment of different operating models, development of local system requirements and conceptual design of new operating models. The development process included consultation with researchers, data custodians and our data linkage community reference group, independent privacy and security impact assessment and review and approval by relevant data custodians and ethics committees.

ResultsCritical design issues for data custodians included the separation principle, management of re-identification risk, data integrity, communication with researchers, researcher access and ongoing sustainability. In particular, data custodians desired the flexibility of multiple operating models to enable their participation in data linkage studies and to accommodate specific features of their data collections. A solution that offers cascading levels of data storage and handling on behalf of data custodians has been developed and supported as “privacy positive” in formal Privacy Impact Assessment.

Different operating models can have significant implications for end user timeframes, operational efficiency and linkage quality. Published evidence on comparative outcomes from different models is scant, but the available literature has substantially assisted with our analyses and stakeholder communication around the risk-benefit of new models and the expected realisation of public benefit.

ConclusionOur data linkage centre will implement new operating models with cascading levels of data handling on behalf of custodians. Sharing or publication of empirical evidence on timeframes, efficiency and quality can provide useful inputs in the design of new operating models and assist with the development of stakeholder and public confidence.
First steps in demonstrating an impact on health service use of the Welsh Government Supporting People homelessness prevention programme using linked administrative data
Authors: Lowe Sarah , McGinn Laura , Heaven Martin , Griffiths Lucie , Bailey Kerry
Published: April 2017

ABSTRACT

ObjectivesTo assess the feasibility of using linked administrative data to deliver a quantitative component to the evaluation of the national homelessness prevention programme in Wales, Supporting People. Specifically, to establish whether any impact of Supporting People on the use of health services could be demonstrated. Approach: Data on the Supporting People programme was acquired from two pathfinder Local Authorities in Wales and anonymously linked to the routine health records held in the SAIL Databank at Swansea University. Any bias in linking rates was examined and health events analysed for the period 12 months before and 12 months after the point when homelessness prevention support began (the ‘crisis point’). The study evaluated a range of possible control groups that could be created using administrative data and undertook some preliminary comparative analysis. ResultsThe results of the study are due to be published on 10th March 2016 so detailed results will be available for August. We will briefly explore the unique contribution of data linking to researching the hard-to-reach and/or vulnerable groups supported by the intervention (e.g. people with substance misuse problems, women experiencing domestic violence and people with a criminal offending history) and the challenges of delivering a quantitative evaluation using linked administrative data. Findings will be reported on linking rates including any potential areas where particular service groups were relatively less well represented. Patterns of the following health events will be reported around the ‘crisis point’: i) number of days when GP events occurred; ii) number of A&E visits and iii) number of emergency hospital admissions. Interpretations are made of consistent patterns of health events across different service user subgroups. The GP diagnosis codes, GP prescribing codes, reasons for visiting A&E and reasons for emergency hospital admissions that showed the greatest decrease after the intervention will be reported. A brief discussion will be presented of the suitability of different control groups and the findings of the preliminary comparative analysis. ConclusionsUsing primary research methods, demonstrating the impact of a homelessness prevention programme on outcomes like health service use represents an insuperable challenge. The use of data linking data delivers a challenging but potentially robust method to demonstrate impact and the feasibility study was able to take the first steps towards demonstrating improved health outcomes for individuals.
Adherence and persistence of subcutaneous biologics delivered through homecare services to a cohort of patients with rheumatoid arthritis
Authors: Alvarez-Madrazo Samantha , Kavanagh Kim , Semple Yvonne , Lannigan Norman , Milne Kevin , Godman Brian , Maciel Almeida Alessandra , Acurcio Francisco Assis , Siebert Stefan , Bennie Marion
Published: April 2017

ABSTRACTObjectivesBiologics are a class of medicines recommended by UK and international guidelines for treatment of patients with rheumatoid arthritis (RA) who are unresponsive, intolerant or who have contraindications to conventional therapies. Biologics are often supplied in the UK to patients in their home by external healthcare providers commissioned by the National Health Service (NHS). Patient adherence and persistence to biologics has not been assessed within NHS Scotland.The aim of this study was to evaluate adherence and persistence patterns of subcutaneous biologics delivered by homecare services to patients with rheumatoid arthritis in a Scottish NHS Board.

ApproachA population-based cohort study was conducted using records from patients receiving medication through healthcare delivery companies in the NHS Greater Glasgow and Clyde health board. RA patients with deliveries between 2012- 2015 were linked via their Community Health Index number to their prescriptions in primary care, hospital admissions and death records. For this study those with at least two deliveries of biologics and who did not switch biologic treatment were included. Length of therapy was determined from first delivery to last day treatment was covered. Adherence rate referring to the supply received for the whole study period was evaluated through Medication Refill Adherence (MRA). Persistence from first delivery to discontinuation was estimated using survival analysis and a drug–free interval of 90 days.

ResultsA total of 434 patients with RA received a subcutaneous biologic. The mean age at first delivery was 54.7 (SD 13.4) years and 78% were female. From this cohort, 322 had at least 2 deliveries of the same biologic. The overall median length of treatment was 387.5 days (IQR 544.5); the longest for certolizumab pegol users with a median of 511.5 days (IQR 629.2) and the shortest for tocilizumab (median of 187 days, IQR 90.5). The adherence throughout the study had a mean MRA 79.73% (SD 29.95) and was similar across the different biologics. Persistence at follow-up decreased with time from 81% of the patients receiving a biologic at 6 months, to 67% after 1 year and 65% after two years. Adalimumab, certolizumab pegol, etanercept and golimumab users had higher persistence rates.

ConclusionLength of treatment and persistence, but not adherence, are influenced by the type of subcutaneous biologic received by the RA patients in this cohort.Future research will gain a better insight into the factors influencing adherence and persistence to these medicines.
Secure provision of unconsented radiological imaging for research
Authors: Mcallister Gordon
Published: April 2017

ABSTRACT

Objectives

Design and implement an architecture for managing unconsented DICOM imaging

Maintain sufficient data to define research cohorts when data quality is unknown

Perform project-level linkage and extraction into a Safe Haven (SH) environment

Extract large image volumes for multiple projects with limited storage constraints

Provide applications for an imaging research workflow within the SH environment

Serve as a prototype for the Farr/NHS Scotland project to create a research dataset from Scotland’s national PACS

ApproachThe software architecture builds on the Research Data Management Platform (RDMP) developed at Dundee’s Health Informatics Centre (HIC) within Farr@Dundee. The RDMP provides core services common to loading any dataset, with configuration and extensibility points for dataset-specific implementations. This architecture augments the RDMP with scalable micro-services performing peripheral functions.

Images are sourced from the local PACS server in Ninewells Hospital and cached securely within HIC using an implementation for the RDMP with a custom server to query/retrieve data.

Data stored in the catalogue should be anonymous, according to the Scottish SH model. The imaging dataset is poorly understood, with several potentially identifiable free-text fields which may contain information required for defining suitable research cohorts. The load process only permits verified metadata fields into the anonymised catalogue; a Mongo database stores other data for later analysis, should a field subsequently be required for cohort definition.

A DICOM extraction implementation is provided, using DICOM Confidential for anonymisation and a project-specific remapping of DICOM GUIDs.

Two provisioning methods have been designed. A basic copy when sufficient storage is available, and a more sophisticated method using a custom filesystem to provide separate project-specific views onto shared image files.

ResultsA full end-to-end solution has been developed, from initial caching through to provisioning anonymised images. Two imaging cohorts have been loaded, one with over 5000 studies. NHS Tayside CT and MR data since 2008 is currently being loaded.

Two projects have had anonymised extracts released using the ‘copy’ method. The custom filesystem method has been developed and tested with limited amounts of data.

This work has highlighted anonymisation, cohort creation and SH issues which require further exploration.

ConclusionA production system for securely providing linked DICOM imaging to researchers has been implemented, serving as a testbed for a national system which will provide a unique population-level resource for researchers.
Cautionary accounts in the use of health equity measures from linkable administrative data in population health intervention research
Authors: Nickel Nathan , Chateau Dan , Brownell Marni , Katz Alan , Burland Elaine
Published: April 2017

ABSTRACT

ObjectivesThere is increased interest in identifying strategies the reduce health inequities. With this focus, population health scientists have applied equity measures first developed in other disciplines to health equity research. The objective of this study is to illustrate the application of these measures in research using linkable administrative databases. This presentation will provide a brief description of some commonly-used equity measures and issues investigators face when applying them in their own health equity research.

MethodsAnalyses focused on children born in Manitoba, 1984 to 2014. We used linkable administrative data from health, social services, and education to develop indicators of health and the social determinants of health. Income data from the Canadian Census were used to stratify children by socioeconomic status. Our study considered the distribution of several child outcomes: breastfeeding initiation, mortality, complete immunization rates at age 2, Grade 9 completion, and high school completion. We examined several measures often used to capture income-related health inequities: rate ratios and rate differences comparing children from high-income neighbourhoods with children from low-income neighbourhoods; the concentration index which quantifies the equity in the distribution of outcomes across the entire socioeconomic gradient; and the relative and absolute indices of inequality which compare the most advantaged individuals with the least advantaged individuals in the population while accounting for the distribution of health across the population.

ResultsWhen these measures are applied to health equity, they can be affected by factors not initially considered by investigators. The application of Concentration measures using health outcomes that are frequently dichotomized, and the prevalence of the health outcome can affect the degree of inequity that is possible, with highly prevalent outcomes showing very little divergence from the line of equity. Comparing concentration measures to the inequality indices can produce contradictory and seemingly incompatible results. Sample selection that alters the distribution of income from the population can also change the apparent equity of health outcomes. These matters are complicated when monitoring changes in health equity, over time.

ConclusionsSummary measures of equity can be useful but come with limitations that need to be considered when interpreting and applying study findings. We offer some suggestions to consider when applying these measures in health equity research.
The Canadian Statistical Demographic Database Research Project: Exploring Potential Use of Administrative Data to Support the Canadian Census Program
Authors: Cloutier Sylvain
Published: April 2017

ABSTRACT

ObjectiveStatistics Canada initiated the Canadian Statistical Demographic Database (CSDD) research project to determine if and how administrative data could be used to support the Canadian Census Program.

The project’s goal is to create a census spine from administrative data sources. The CSDD’s current scope is limited to basic information (name, sex, birth date and usual place of residence) for all Canadians.

MethodTwo 2011 CSDD prototypes were built using and linking hundreds of administrative files obtained mainly from other federal departments. Extensive pre-processing activities must take place prior to linkage to remove duplicates and standardize file variables. Given that Canadians do not possess a single unique identifier, administrative files were linked using record linkage methods; key matching variables were identified, validated and used to perform the linkage. This work led to the development of auxiliary files, which serve specific purposes related to the CSDD development. They also provide useful linkage keys to other Statistics Canada statistical programs.ResultsThe outcome of the CSDD is determined by comparing it to two references. First, comparisons were done at the aggregate level (Canadian, provincial and sub-provincial levels) by contrasting the results with Demography Division’s official population estimates for the 2011 Census. The CSDD was also compared with the 2011 Census of Population’s Response Database (RDB), which allows for analysis at the micro (record) level. The RDB contains non-imputed data on name, sex, birth date and usual place of residence as provided by individual census respondents. Comparisons with the RDB have allowed us to address the question, “Does the CSDD put the right person at the same address as the 2011 Census does?”

Results are promising. At the aggregate level, the CSDD compares well with the demographic estimates for the 2011 Census at the national, provincial/territorial and some urban area levels. At the micro level, the CSDD contains more individuals than the RDB. Improvements are needed with regards to its ability to place persons accurately in rural areas due to the lack of good residential addresses in administrative data files.

Initial results led to the planning of new CSDD prototypes, this time for 2016, in line with the 2016 Census of Population.ConclusionThe presentation will give an overview of the methods and principles behind the construction of the CSDD. Basic analytical results will present areas of strength and weakness. Lessons learned and upcoming challenges along with their proposed solutions will complete the presentation.
Transforming children's futures: the challenges and benefits of using data linkage to improve the planning of children's services within Scotland
Authors: Waugh Harriet , Anand Gita , Anderson Simon , De Wit Isabella
Published: April 2017

ABSTRACT

BackgroundRealigning Children’s Services is a programme which aims to improve the commissioning and planning of children’s services through the use of evidence and implementation science. A key focus of this is an ambitious data linkage project which links two sets of survey data with local administrative data from Education and Social Work in local Community Planning Partnerships (CPPs) within Scotland. The initial tranche for the programme worked with three CPPs in 2015/16 and has involved work at a national and local level.

AimsThe data linkage component to the project has been closely aligned to local need and interest with continued collaboration with professionals in local areas from strategic managers to schools. The data resulting from the linkage will allow for a series of reports tailored to local priorities and will also inform a data visualisation tool which will help professionals to look closely at the data. It has also prompted discussion and reflection within the local areas around what data is currently used in each locality, how evidence can inform their planning and what gaps exist within their knowledge base which could be filled through the data linkage.

ApproachContribution Analysis was used as a framework to evaluate the effectiveness of the Realigning Children’s Services programme. This allowed a focus on far reaching outcome measures alongside process measures through a mix of qualitative and quantitative data. Data from this evaluation will inform future linkage projects and evaluate the use of linked data in strategic planning in children’s services.

ConclusionThe Realigning Children’s Services programme has shown that data linkage within a children and families context can be complex and ethically challenging. However it also offers professionals access to critical data which can otherwise go overlooked. This presentation will discuss these challenges and consider how this work has been used to help local areas improve their planning of their services for children and families within a multidisciplinary setting.
Can We and Should We Produce a model for Emergency Admissions derived from Primary Care Data?
Authors: Bailey Kerry
Published: April 2017

ABSTRACT

BackgroundSeveral Risk Prediction models exist for predicting emergency admissions. One exists derived solely from primary care data but this is only available in England. All the models currently available have limitations. The aim was to utilise the SAIL databank to develop a predictive model/tool using only primary care data that has comparable accuracy to the currently used risk stratification tools but overcomes the limitations of the current models available.

ApproachA multidimensional approach was taken including literature reviews and qualitative interviews to the model to situate the statistical model. The need was identified to develop the contextual background in order to situate the statistical model development and understand the need for an improved model. Stakeholder groups were identified – patients, managers, General Practitioners and policy makers – and purposive sampling was used to obtain breadth and depth of representation into the subject area. Interviews were carried out in two stages initially to identify key themes and the breadth of the interest in stakeholder and influenced hypothesis generation. The second stage was to provide depth and clarity on specific themes with a more structured interview schedule. Results of the qualitave work triangulated with literature reviews influenced the statistical model build.

DiscussionThe statistical challenge of building Risk Prediction models well documented. Understanding the shortcoming of current models as perceived by stakeholders is necessary to produce a model of added value which performs statistically similar however has a different functionality. The qualitative work that was carried out to situate this model added value to the models development but also raised some interesting ethical issues. The validity and performance of the models although important is only one aspect that influences whether GPs would use a risk prediction model and how acceptable it is to stakeholders. None of the patients interviewed in this thought that their data was, or would be without their knowledge, used in risk prediction models yet all were patients at practices where this was happening or had morbidities which would have necessitated the use of several in primary care.