bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.