bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Health and Economic Impacts of Structural Energy Performance Investments in Wales: An Evaluation of the Arbed Programme
Authors: Johnson Rhodri , Rodgers Sarah , Watkins Alan , Poortinga Wouter , Grey Charlotte , Jiang Shiyu , Anderson Pippa , Lyons Ronan A 
Published: April 2017

ABSTRACT
ObjectivesOver £68M was invested in a housing regeneration programme (Arbed) in 2010-2011 to improve the energy performance of houses in a number of deprived areas across Wales. Houses within the areas were retrofitted with measures, such as external wall insulation, solar hot water systems, solar panels and air source heat pumps. The study aims to establish whether such interventions improve residents’ physical and mental health, and the economic impact of change in health outcomes.
ApproachAddress and intervention data were collected from 28 administrative centres and anonymously linked within the SAIL (Secure Anonymised Information Linkage) databank. Two comparator groups were created, one consisting of social houses, another from properties situated in the most deprived areas of Wales. Individuals with continuous residence of at least 60 days over a 10 year period were selected to create a dynamic cohort. Data on emergency hospital admissions and primary care records were extracted from SAIL to assess the impact of investments on healthcare utilisation for cardiovascular and respiratory conditions. Healthcare utilisation data also contributed towards an economic evaluation and excess winter admissions analyses.
ResultsThe final dataset comprises data from over half a million people contributing more than 3 million person years of exposure time to build up trends of hospital admissions across the study period. The intervention cohort consists of over 35,000 people, with about 7,000 separate hospital admissions recorded for our primary outcome. The analysis is based on a pooled time series regression using a difference-in-difference approach to estimate the health impact of the programme.
Conclusion Retrospective analysis of such a natural experiment using routinely collected health data is possible using data linkage techniques. Final results will show whether social programmes to improve energy efficiency to homes in fuel poor areas have a wider impact on the healthcare resource utilisation of residents. We discuss the requirement for more standardised, accurate and timely data capture at the time of such economic investments to ensure the future feasibility of such studies.
This project was funded by the National Institute for Health Research, Public Health Research (PHR) program (project number 11/3020/05)(http://www.nets.nihr.ac.uk/projects/phr/11302005).
Linking the Australian Childhood Immunisation Register to population-based perinatal datasets to assess coverage in at-risk groups
Authors: Moore Hannah , McCallum Lisa , Fathima Parveen , De Klerk Nicholas , Liu Bette , Snelling Tom , Blyth Christopher , McIntyre Peter , Gidding Heather 
Published: April 2017

ABSTRACT
ObjectiveImmunisation remains one of the most important public health interventions. However, linkage of population-based immunisation registers to perinatal and health outcome datasets to evaluate immunisation programs is limited. We have conducted the first-ever linkages of immunisation records from Australia’s Childhood Immunisation Register (ACIR) for the purposes of evaluating Australia’s unique national immunisation program. As an initial outcome of our linkage study we present estimates of pneumococcal conjugate vaccine (PCV) coverage for the 3rd dose assessed at 12 months of age in extremely preterm children (gestational age <28 weeks), in whom a funded vaccination program was established in 2001 prior to a universal funded program for all children in 2005.
ApproachIndividual immunisation records from ACIR, hospital admissions, deaths and infectious diseases notifications were linked to perinatal records for a cohort of births from 1996 to 2012 in two Australian states. Three separate data linkage units were involved in the process with varying procedures for linkage. The perinatal datasets were used to identify extremely preterm children.
ResultsThe birth cohort for the study included 1,958,537 live births in New South Wales (1,492,399) and Western Australia (461,620). Linkage weights based on sensitivity and positive predictive value of >99% were used to identify immunisation records from ACIR to link to the birth cohort. A unique scrambled pin on ACIR was used to link immunisation records to birth cohort datasets. The final cohort consisted of 1,954,019 children with 95.5% linking to at least 1 ACIR record from a total of >26.6 million ACIR records. In 2001, coverage of the 3rd dose of PCV in extremely preterm children was 0.9% and increased to 69.1% in 2004 (overall coverage 2001-2004: 25.3%). From 2005 to 2012, coverage increased to an average of 89.7%.
ConclusionThese are the first results of cross-jurisdictional linkages of immunisation records to state-based administrative datasets in Australia. This process has identified some improvements that are needed to streamline future linkage projects of this scale. Linkage of perinatal datasets to ACIR has enabled us to assess the first-ever coverage estimates in specific medically at-risk population subgroups. Future analyses will focus on the predictors and timeliness of vaccination coverage and population based estimates of vaccine effectiveness.
Bespoke linkage of Public Health England laboratory data on gastrointestinal infections to the Clinical Practice Research Datalink: Ethics approval and a study proposal
Authors: Esan Oluwaseun , Fanshawe Thomas , Violato Mara , McCarthy Noel , Perera-Salazar Rafael 
Published: April 2017

ABSTRACT
ObjectivesGastrointestinal (GI) infections are common. Most infections are self-limiting, with some people developing long-term sequelae following their GI infection. Currently, the surveillance of GI infections such as Campylobacter and Salmonella is primarily based on laboratory data held by Public Health England (PHE). Information on symptoms severity, treatment of infection and subsequent complications is not captured by this data source.
Electronic Health Records (EHR) provide a platform to assemble cohorts for long-term follow-up at relatively low costs. Record linkage of existing EHR provides a powerful resource to estimate the burden of complications following gastrointestinal Infections (GI) and the associated risk factors such as treatment of the primary GI infection.
A range of ethical and data governance approvals processes, motivated to protect patients and patient data, are required to allow use of routine and linked data for research. The aim of this project is to establish a bespoke linkage of PHE laboratory data on Campylobacter, non-typhoidal Salmonella (NTS) and verocytotoxin producing Escherichia coli (VTEC) to the Clinical Practice Research Datalink (CPRD, primary care records), hospital records and other secondary datasets in order to estimate the burden of complications following GI infections.
ApproachLinkage of laboratory data, primary care data, hospital records, deaths and deprivation index were proposed following an initial data completeness check of laboratory data on Campylobacter spp., NTS and VTEC over a ten year (2004-2014) period. Linkage plans were discussed with all relevant organisations. Finalised study protocols were completed and application for approval for linkage was submitted to the Independent Scientific Advisory Committee (ISAC) and the NHS Research Ethics Committee. An additional application was sent to the Confidential Advisory Group (CAG) to comply with section 251 of the UK NHS Act 2006 on accessing patient identifiable information.
ResultsISAC approval for CPRD linkage is dependent on CAG approval. Completion of data linkage will allow the measurement of complications following GI infections to be estimated more accurately than is possible using routine, unlinked population-level data. It allows the implementation of designs such as the case-crossover design for the investigation of patient-level risk factors such as use of antibiotics and their association with complications.
ConclusionsBespoke record linkage to CPRD can allow the measurement of risk of long-term sequelae following acute infections. Considerable time should be built into research timelines to allow completion of all ethical, governance and data access procedures.
Determining breast cancer recurrence following completion of active treatment: A novel approach using linked administrative health data
Authors: Preen David , Kemp-Casey Anna , Roughead Elizabeth , Lopez Derrick , Bulsara Max , Boyle Frances , Saunders Christobel 
Published: April 2017

ABSTRACTObjectivesAlthough outcomes for the majority of women diagnosed with primary breast cancer are good, with five-year survival exceeding 90%, some women will experience cancer recurrence and ultimately die from the disease. It is important for patients, clinicians and health service planners to know the risk of recurrence once initial treatment for primary breast cancer is completed. However, none of Australia’s State or Territory cancer registries routinely report on cancer recurrence which could be used to evaluate this issue. To address this absence of direct reporting, we aimed to determine the incidence of cancer recurrence in Australian clinical practice after completion of treatment for primary breast cancer, using a range of linked health data sources.
ApproachWe performed a retrospective cohort study using linked health data from New South Wales (NSW), Australia. Data were linked from six data collections: i) Cancer Registry, ii) Admitted Patient Data Collection, iii) Pharmaceutical Benefits Scheme claims, iv) Medicare (outpatient) claims, v) Death Registry; and the vi) NSW 45 and Up Study. We identified 2416 women diagnosed with primary invasive breast cancer during 2003-2008 in NSW who had not had a recurrence by 18 months post-diagnosis. Unit-level hospital, pharmacy and outpatient claims were used to identify services indicative of recurrence. Incidence of recurrence was calculated and multivariate Cox regression used to identify baseline and active treatment characteristics predictive of cancer recurrence up to six years post-diagnosis.
ResultsA total of 217 women (9.0%) had a hospital, pharmacy or outpatient claim indicating breast cancer recurrence between 18 months and six years post-diagnosis. Overall annual cumulative incidence of recurrence was 3.3%. Recurrence was significantly higher for women with node-positive (4.8% vs. 2.5% annually, adjHR=1.7, 95%CI=1.3-2.3) or hormone receptor-negative (3.8% vs. 3.1% annually, adjHR=1.3, 95%CI=1.0-1.7) tumours. Women with tumours >2cm at diagnosis were more likely to experience recurrence within six years compared with those with a smaller initial tumour (4.8% vs. 2.7 annually, adjHR=1.5; 95%CI=1.1-2.0).
ConclusionsWomen with breast cancer in this Australian cohort experienced recurrence at 3.3%pa in the years following completion of treatment. Those at greatest risk of recurrence were women with node-positive or hormone-receptor negative tumours, or tumours >2cm at initial diagnosis, consistent with international findings. This method for ascertaining breast cancer recurrence can be used to assess population-level changes over time and to investigate the impact of specific treatments on outcomes in the absence of available Cancer Registry data.
Neonatal and infant readmissions for late preterm and early term babies in Ontario and England: a cohort study using linked population-level healthcare data
Authors: Harron Katie , Gilbert Ruth , Guttmann Astrid , Cromwell David , Van der Meulen Jan 
Published: April 2017

ABSTRACTBackgroundBabies born late preterm (34-36 weeks gestation) or early term (37-38 weeks) are at increased risk of unplanned readmissions compared with full-term babies. We examined differences in neonatal and infant outcomes in England and Ontario.
MethodsLinked maternity-baby hospitalisation data were extracted from two universal healthcare systems, Ontario (n=702,565; 2005-2013) and England (n=1,165,375; 2011-2013). We modelled rates of unplanned readmissions within 30-days post-discharge of delivery, and readmissions, emergency department (ED) visits, deaths and total inpatient days within 12-months post-discharge, adjusting for neonatal, maternal and delivery factors.
ResultsThe median newborn length of stay was 4 and 5 days in Ontario and England respectively for late preterm babies, and 2 days in both countries for early term babies. Early neonatal readmissions were lower in Ontario: 4.8% of early term and 7.2% of late preterm babies compared with 8.3% and 11.4% respectively in England (p<0.05). Within 12-months post-discharge, 9.6% of early term and 13.5% of late preterm babies were readmitted in Ontario compared with 24.0% and 30.5% in England (p<0.05); total inpatient days per 100 babies were 36.5 for early term and 61.9 for late preterm (Ontario) compared with 62.7 and 107.6 (England). Infant mortality (0.1-0.4%) and ED visits (40-44%) were similar between countries.
ConclusionsUnplanned readmissions and total inpatient stay are significantly higher in England than Ontario for early term / late preterm babies, despite similar ED attendances and lengths of newborn stay. Further investigation of differences in healthcare practices between countries should evaluate access to paediatric primary care and thresholds for admission.
Clinical Validation of the UKMS Register Minimal Dataset utilising Natural Language Processing
Authors: Middleton Rod , Akbari Ashley , Lockhart-Jones Hazel , Jones Jemma , Owen Charlotte , Hughes Stella , Gain Richard , Ford David 
Published: April 2017

ABSTRACT
ObjectivesThe UK MS Register is a research project that aims to capture real world data about living with Multiple Sclerosis(MS) in the UK. Launched in 2011, identified data sources were: Directly from People with MS (PwMS) via the internet, from NHS treatment centers via ‘traditional’ database capture and by linkage to routine datasets from the SAIL databank.
Data received from the NHS, though ‘gold standard’ in terms of diagnosis, is dependent on clinical staff finding both time and information to enter into a clinical system. System implementations across the NHS are variable, as is clinical time. Therefore, we looked to other complementary methodologies.
ApproachThe Clix enrich natural language processing (NLP) software was chosen to see if it could capture a portion of the MS Register minimum clinical dataset, the software matches clinical phrases against SNOMED-CT.40 letters, from 2 NHS Trusts, from 28 patients were loaded. The letters were a mix of MS patients with differing disease subtypes and were dictated by Neurologists, Specialist General Practitioners and MS Specialist Nurses. 20 of the letters were in docx format and 20 as PDF.
The letters were parsed by a domain expert for clinical content, scored by data item for sensitivity and specificity. Next the output from the software was scored by another researcher to see if the 12 relevant clinical concepts from the Register dataset had been elicited. Lastly a ruleset was created to look for particular clinical concepts and scored in the same way.
ResultsOf the 40 letters one failed to load, the rest were analysed for the specific data items. Date related items were clearly challenging, with only 7% of appointment dates being matched and 22% for date of diagnosis. MS Type (93.3%) and EDSS score (93.75%) were well recognised, additionally symptoms of MS that would be poorly reported in traditional databases were recognised, with fatigue being well highlighted (78.5%) and gait and walking issues (68.7%)
Of concern, were a number of false positive results in DMT’s with 15% patients being identified as being on a DMT when this was just being ‘considered’.
ConclusionThe NLP pathway could be extremely useful for obtaining hard to capture clinical data for the Register. Further work is needed to reduce errors, even with the current minimal configuration, it's possible to ascertain MS Type, functional score of MS, current medication and potentially disabling symptomology within the condition.
Data linkage of routinely collected electronic health records to characterise risk factors associated with antibiotic resistance in urinary isolates: an exemplar of NHS Scotland’s Infection Intelligence Platform
Authors: Fletcher Eilidh , Kavanagh Kim , Malcolm William , Wiuff Camilla , Reid Nicholas , Deshpande Ashutosh 
Published: April 2017

ABSTRACT
ObjectivesUrinary tract infections (UTIs) are amongst the most common infections treated in community and hospital settings. Initial antibiotic treatment of UTI is usually empirical, that is, where the prescriber has no definitive information on the organism or its antibiotic sensitivity.. Overall the prevalence of antimicrobial resistance is increasing, and specifically so for antibiotics commonly used for UTI.
By linking NHS surveillance data to routinely collected administrative health data this study aims to investigate risk factors for antibiotic resistance in urine samples
ApproachAll positive urine samples included in the “Surveillance of Antimicrobial Resistance in Urinary Isolates in Scotland” dataset in the period from January 2012 to June 2015 (all NHS Health Boards in Scotland submit susceptibility data for up to eleven antibiotics on 400 positive urinary samples per quarter) were analysed. Cases were assigned a resistance status of Sensitive, Resistant or Multi-drug resistant based on the antibiotic susceptibility data recorded.
Using the NHS Scotland Infection Intelligence Platform all cases were linked to national coverage data: (i) hospital discharge data to create the Charlson score for comorbidity and (ii) patient-level community prescribing data to measure cumulative antibiotic exposure (number of defined daily doses) in the 3 months prior to infection. Risk factors associated with the infection susceptibility to antibiotics were assessed using multivariable multinomial logistic regression.
Results40,984 positive urine samples were examined. Overall 29.0% were sensitive, 48.1% resistant and 22.9% multi drug resistant. Around a third of the cases (33.9%) had no antibiotic prescribing in the 3 months prior to infection. Age, care home residence and increasing comorbidity were both found to be associated with resistance and multidrug resistance. Cumulative antibiotic exposure had a clear dose-response effect. Those with 1-7DDDs were 1.2 times (95% CI: 1.11-1.29) more likely to have a multidrug resistant infection (compared to a sensitive infection) rising to 7.45 times (95% CI: 95% CI 6.45-8.6) for 29+ DDDs. Similar dose response held for resistant infection but at a lesser scale (1-7DDDs OR=1.36 (95% CI: 1.2-1.5) rising to OR=3.04 (95% CI: 2.38-3.89).
ConclusionA clear effect of cumulative antibiotic exposure in the community and multidrug resistance in UTI cases has been demonstrated. Such quantification is key to ensuring and supporting robust antimicrobial stewardship policy and will form the evidence base for development of prescribing decision support tools for more patient centred treatment of UTI.
Information Management at a Health Services Research Organization in Toronto, Ontario, Canada: Moving from Identifiable Data to Coded Data
Authors: Thurairasu Lisa , Chong Nelson 
Published: April 2017

ABSTRACTObjectivesA health services research organization in Toronto, Ontario, Canada conducts population-based research to improve the health of Canadians in seven main areas: (1) cancer, (2) cardiovascular disease, (3) chronic disease and pharmacology, (4) health system planning and evaluation, (5) kidney, dialysis and transplantation, (6) mental health and addictions, and (7) primary care and population health. The Information Management (IM) team within the Data Quality and Information Management (DQIM) department at our non-profit organization is an integral component for upholding privacy and confidentiality policies and procedures while facilitating quality research using different types of data such as health administrative, third-party, primary data collection, and electronic medical records (EMR).
MethodsThe IM team is responsible for receiving data, encoding direct personal identifiers, screening for unnecessary identifiers, performing probabilistic data linkage when necessary, importing the data to the Research Analytics Environment (a client/server Linux-based system), and destroying the data according to the terms stipulated in the executed data sharing agreement. The purpose of the presentation is to detail the above steps of processing data to protect individuals’ identities yet preserve the usefulness of carrying out research. The presentation will include aspects from importing data into SAS to storage and encoding of personal identifiers to probabilistic data linkage, which involves maximizing linkage with other datasets at the organization. Linking data at the organization involves the encryption or encoding of health card numbers to “Key Numbers.”
ResultsThe processing practices used at the organization comply with Canadian privacy laws such as the Personal Health Information Protection Act (PHIPA) as well as organizational policies and Research Ethics Board approvals. The approaches used to conceal individual identities yet allow linkage to various data sources can be modelled by other health agencies, ministries, and non-health related organizations that work with sensitive data but face challenges in maintaining both privacy and research quality. Our organization strives to make processing as efficient as possible and create maximum linkability to the various data sources in house while upholding privacy and confidentiality.