bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linking lab, program, and administrative data to provide comprehensive colorectal cancer screening status of patients to primary care providers in Calgary, Alberta
Authors: Law Jessica , Viczko Jeannine , Hilsden Robert , McKenzie Emily , Watt Mark , Potestio Melissa , McGregor S Elizabeth 
Published: September 2018

IntroductionColorectal cancer (CRC) screening is associated with significant reductions in burden, mortality and cost. Primary care providers in Alberta do not have access to integrated CRC testing histories for patients. Providing this information will support CRC screening among patients at average and high risk, follow-up of abnormal tests, and surveillance.
Objectives and ApproachCalgary Laboratory Services, Colon Cancer Screening Centre, Alberta Cancer Registry, and endoscopy data were linked to create a comprehensive CRC screening history at the patient level. Based on screening histories and the current Clinical Practice Guideline, an algorithm was created to determine CRC screening statuses with the aim of providing accurate screening rates when linked to primary care provider patient panels. Results from the linkage are designed to be incorporated into clinic and EMR workflow processes to support adherence to evidence-based screening recommendations at the point of care.
ResultsA comprehensive assessment of screening status was determined by integrating Fecal Immunochemical Test (FIT) and colonoscopy data. Among a sample cohort, patients were identified as being due for screening with FIT, requiring follow-up for a positive FIT test, or requiring appropriate surveillance for a positive-screen or abnormal colonoscopy findings. A summary report, actionable list, and resources were developed to convey findings. The summary report displayed CRC screening rates for a provider’s panel. The actionable list provided CRC screening statuses for each patient aged 40 to 84 indicating patients due for screening with FIT, for follow-up of positive FIT, or for surveillance colonoscopy. The resources were developed to support quality improvement for colorectal cancer screening for patients.
Conclusion/ImplicationsThe data linkages and algorithm provide comprehensive CRC screening, follow-up, and surveillance information that could support guideline-adherent screening, increase screening rates, reduce duplication or unnecessary testing, and provide primary care providers with timely and robust information to support clinical decisions for individuals inside and outside of the target screening population.
Improving the quality of care pathways for sarcoma patients and the advantages of using WCISU’s (Welsh Cancer Intelligence Surveillance Unit) national cancer registry
Authors: Griffiths Rowena , Akbari Ashley , Huws Dyfed , Lyons Ronan , Rolles Martin , Vass Johanne 
Published: September 2018

IntroductionSoft Tissue Sarcoma (STS) diagnosis is difficult due to its nature and the variability of its occurrence on the body. To improve patient outcomes a better understanding was needed of the care pathways experienced by the patient from initial presentation to final treatment.
Objectives and ApproachSeveral items of information are necessary, within the data, to identify a care pathway. A correct STS diagnosis, a presentation date or first investigation date, a diagnosis date and any subsequent treatment dates. Identifying cases in hospital data, using International Classification of Diseases (ICD10) codes - C40, C41, C47 and C49 - based on cancer site - can miss cases and cause difficulties when trying to distinguish the difference between the investigation and treatment stages. Having access to WCISU’s national cancer registry, proved advantageous and enabled the routine data to be validated.
ResultsAttempts to identify differences between investigative and treatment procedures using the procedure codes available in hospital data was unhelpful due to variations in coding.
However, WCISU’s national cancer registry records all cases of cancer diagnosed in Wales using both ICD10 and International Classification of Diseases for Oncology codes to record cancer morphology. In addition, it records the date of diagnosis and treatment start dates. Using the cancer registry it was possible to cross-check the cases extracted from the hospital data and identify the diagnosis and treatment dates. By matching the treatment dates back to the hospital data it then became possible to analyse the procedure codes to see how many treatments were being delivered, the type of treatment and the periods covered.
Conclusion/ImplicationsOnce accurate diagnosis and treatments dates were identified, it was possible to drill further into the hospital data to see the finer detail of the procedures the patient received. Utilising independent data sources made it possible to develop an enriched view of patient care pathways from diagnosis through to treatment.
Validation of Acute Kidney Injury e-alert system in Wales
Authors: Davies Gareth , Scale Timothy , Akbari Ashley , Chess James , Lyons Ronan 
Published: September 2018

IntroductionAcute Kidney Injury (AKI) is a serious condition associated with increased mortality/morbidity and occurs in approximately 20% of hospital admissions. A Welsh AKI alert (e-alert) system has been in place since 2014, aiming to improve patient outcomes and care standards through early recognition of AKI using a retrospective dataset.
Objectives and ApproachThe e-alert algorithm was re-produced in SQL (structured query language) and applied to serum creatinine (SCr) values for patients from pathology departments in Abertawe Bro Morgannwg health board (ABMUHB) 2011-2014, held in the Secure Anonymised Information Linkage (SAIL) databank. The algorithm utilises ratios between current SCr value (C1) to lowest SCr value within the previous 7 days (RV1), SCr median values from the previous 8-365 days (RV2), and lowest SCr within 48hrs (D). >50% increase in RV1 or RV2 or >26µmol/L above D triggers an alert. Using a renal dataset to create a timeline we created a temporal AKI cohort.
Results2,407,590 SCr tests were performed on adult patients with 2,077,493 of these coming from people in the local area who were not on renal replacement during the time-period. The average ABMUHB population for 2011-2014 was 520,293 (2011: 517,981; 2012: 519,481; 2013: 520,710; 2014: 523,001). 85,272 (4.1%) of these tests triggered alerts for AKI. The incidence per 100,000 population of AKI for 2011-2014 were 1767, 1723, 1717, 1660 (average 1,717). The first AKI episodes per year for 2011-2014 respectively were stage 1 (least severe): 78.9%, 79.3%, 79.3%, 79.4% (average 79.2%); stage 2: 13.3%, 13.7%, 13.1%, 13.7% (average 13.5%); stage 3 (most severe): 7.8%, 7.0%, 7.6%, 6.9% (average 7.3%).
Conclusion/ImplicationsThe AKI e-alert algorithm can be effectively reproduced using standard query language. The AKI findings in this population are comparable to others published. The use of a renal dataset using both records of renal replacement timeline and individual dialysis session may identify and rectify where alerts have not been generated.
Postpartum Hemorrhage: Differences in Definition, Data and Incidence
Authors: Walther Diana , Halfon Patricia , Desseauve David , Vial Yvan , Burnand Bernard , Le Pogam Marie Annick 
Published: September 2018

IntroductionPostpartum hemorrhage (PPH) remains a major cause of morbidity and mortality worldwide. Geo-temporal comparisons of in-hospital PPH incidence remain a challenge due to differences in definition, data quality and the absence of accurate, validated indicators.
Objectives and ApproachTo compare the incidence of PPH using different definitions to assess the need for a validated indicator. Singleton births from 2014-2016 at Lausanne University Hospital, Switzerland, were included. PPH was defined based on 1) clinical diagnosis using International Classification of Diseases (ICD-10-GM) PPH diagnostic codes, 2) volume of blood loss ≥500ml for vaginal births and ≥1000ml for cesareans 3) peripartum Hb change >2g/dl in vaginal births and ≥4g/dl in cesareans and 4) fulfillment of criteria from definition one, two or three. Data were extracted from hospital discharge data and linked with electronic health records.
ResultsThere were 2529, 2660 and 2715 singleton births in 2014, 2015 and 2016, respectively, 28.8% were cesareans. Peripartum change in Hb was available for 17% of births. The incidence (95% CI) of PPH in 2014, 2015 and 2016 was, respectively: 1)6.0% (5.1, 7.0), 6.3% (5.4, 7.3) and 7.9% (6.9, 9.0) based on diagnostic codes; 2)7.9% (6.8, 9.0), 7.1% (6.2, 8.2) and 7.2% (6.3, 8.3) based on blood loss volumes; 3)2.4% (1.8, 3.1), 2.7% (2.1, 3.4) and 3.5% (2.9, 4.3) based on change in Hb; 4)11.3% (10.1, 12.6), 10.4% (9.3, 11.6) and 11.0% (9.9, 12.3) based on the combined definition. Differences in PPH incidence by year between definitions one and four, two and four and three and four were all statistically significant (McNemar p-values
Conclusion/ImplicationsIncidence varied widely according to definition and data availability, not to mention data quality. Our results highlight the need for a validated PPH indicator to enable monitoring. Future prospects include the validation of a diagnostic code based PPH indicator aided by text mining in electronic health records.
High quality linked data for stroke obtained using non-government clinical registry and routinely collected hospital and death data
Authors: Kilkenny Monique , Kim Joosup , Andrew Nadine , Sundararajan Vijaya , Thrift Amanda , Katzenellenbogen Judith , Flack Felicity , Gattellari Melina , Boyd James , Anderson Phil , Lannin Natasha , Sipthorp Mark , Chen Ying , Johnston Trisha 
Published: September 2018

IntroductionRecent advances in data linkage infrastructure in Australia mean that data can be linked based on various identifiers across datasets. In a first for Australia, we tested the feasibility of linking data between a clinical quality disease registry with Australian and state government health data across multiple jurisdictions.
Objectives and ApproachTo determine whether high quality linked data for stroke can be obtained using a non-government managed registry (Australian Stroke Clinical Registry, AuSCR), national death registry data (Australian government), and hospital admission and emergency presentation data (state governments) to assess the accuracy of consistent variables across the different datasets. We used a cohort design with probabilistic data linkage to merge patient-level records. Descriptive statistics presented for matching concordance and Cohen’s kappa for concordance across demographic variables. The sensitivity and specificity of in-hospital deaths collected in the AuSCR was assessed against national death registrations.
ResultsThere were 16,214 registrants in the study cohort. Their identifiers in the AuSCR from 2009-2013 were linked with death, emergency department and hospital discharge data from April 2004 to December 2016. In total, 99% of the AuSCR registrants were linked to one or more datasets; 98\% were linked with emergency presentation (80%) and/or admission (95%) data. Linkage to national death registrations identified 4,183 death; 1440 of these were identified as in-hospital deaths in both data sets demonstrating that in-hospital death classification in AuSCR had a 98.7% sensitivity and 99.6% specificity. Concordance between common demographic variables was excellent (kappa 0.84 for aboriginal status and kappa 0.99 for sex).
Conclusion/ImplicationsThe majority of AuSCR registrants were accurately linked to the Australian and state government datasets. Linkage quality was excellent and there was high concordance between common variables. The ability to reliably merge the datasets assures future comprehensive analyses of stroke care, ongoing health care resource utilisation and patient outcomes.
Using family physician Electronic Medical Record data to measure the pathways of cancer care
Authors: Jaakkimainen Liisa , Crampton Noah , Pinzaru V. Bogdan , DelGiudice Lisa , Tu Karen 
Published: September 2018

IntroductionGaps in care have been identified along the disease pathway for specific cancers. However, no real-world data exists to identify wait times along these cancer pathways. Secondary use of family physician (FP) electronic medical record data (EMR) can augment existing health administrative data in measuring steps in the care pathways.
Objectives and ApproachWe used FP EMR data to identify care pathways for lung cancer and breast cancer patients from the description of symptoms, to the initiation of investigations, referrals to specialty care and the receipt of specific treatments (surgery, chemotherapy, radiation treatment). Data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at the Institute for Clinical Evaluative Sciences (ICES) was used to identify a cohort of lung cancer and breast cancer patients. Data abstractors examined the FP EMR notes to identify pre-diagnostic symptoms, pre-diagnostic radiological test, biopsy results, oncology and surgical specialist referrals and post-diagnostic surgical and oncological consultations.
ResultsTo date, abstractors have reviewed the FP EMR notes for 300 lung cancer patient and 1200 breast cancer patients. Abstractors identified an index date where there was documentation of the first abnormal test result and/or a FP progress note documenting a “suspicious” or “concerning” sign or symptom. For both lung cancer and breast cancer patients, a pre-diagnostic index date was identified in 88.5% of FP EMR notes. For lung cancer patients 66.7% based were based on abnormal chest x-rays and for breast cancer patients 81.1% were based on abnormal mammograms. Pre-diagnostic symptoms were identified in 62.1% of FP EMR notes and 81.6% had post-diagnostic consultation notes. Wait times from the index date to seeing an oncological specialist were less than four weeks for all patients.
Conclusion/ImplicationsWe are able to use information from FP EMRs linked to health administrative data to identify pre-diagnostic care received by patients prior to their cancer diagnosis. This information can be used to identify care gaps and measure wait times in receiving cancer care from a patient’s perspective.
The impact of primary care management and comorbidities of COPD on length of hospital stay
Authors: Smith Kendal , Al Sallakh Mohammad , Akbari Ashley , Davies Gwyneth 
Published: September 2018

IntroductionHealth Resource Groups (HRGs) are bundles of care that absorb similar financial resources. Variations in hospital length of stay (LOS) of patients with the same admission diagnosis and assigned HRG may reflect differences in pre-admission primary care services received by patients.
Objectives and ApproachWe investigated whether within HRGs, variations of LOS of chronic obstructive pulmonary disease (COPD) admissions were associated with differences in COPD management services received in primary care. Individual-level primary and secondary care data from the Secure Anonymised Information Linkage (SAIL) databank of Wales was used for admissions in 2015 with high-volume (n>30) HRGs. Effects of selected COPD primary care quality surrogate measures and comorbidities on LOS were analysed using a linear regression model adjusted for a modified Charlson co-morbidity index. The effect of completed pulmonary rehabilitation (PR) was analysed in a separate dataset for the Cwm Taf Health Board.
ResultsWe included 77,791 COPD patients, with mean age of 73.4 [SD=11.0], and 51.6% of which were males. Patients who were referred for a PR course prior to admission stayed 0.58 less days (95% CI = [0.18, 0.98], p<0.01), while those who completed a PR course stayed 0.76 less days ([0.25, 1.27], p<0.01). Non-significant associations were found where female patients stayed 0.34 days longer than males ([-0.01, 0.68], p=0.05), patients given flu vaccination stayed 0.39 less days ([-0.07, 0.86], p=0.10), and patients with anxiety diagnosis stayed 0.43 more days ([-0.03, 0.88], p=0.06).
Conclusion/ImplicationsLOS for COPD could potentially be reduced with further targeted services provided in primary care. Countries using HRGs with access to linked primary and secondary care data can unlock care insights, enabling live monitoring of the effectiveness of primary care interventions. This approach can be also used for other conditions.
NHS Scotland Public Benefit and Privacy Panel (PBPP) – Does a Proportionate Governance Review Work?
Authors: Morris Carole , Grey Ashley 
Published: September 2018

IntroductionSince its’ inception in 2015, the NHS Scotland Public Benefit and Privacy Panel (PBPP) has approved over 200 applications for access to data. The PBPP are accountable to the public and must demonstrate their assessment of applications for data use in terms of envisaged public benefit and potential privacy risks.
Objectives and ApproachIn 2017 the first annual audit took place. The purpose of the audit exercise was twofold; establish that the governance process is robust and that proportionate governance criteria are the correct measurement tool. The full PBPP committee reviewed a random selection of 10 applications approved at Tier 1 between January 2016 and December 2016.
Committee members were split into groups and sent paperwork relating to the application. A review record was completed covering the questions within the proportionate governance criteria. Review records were sent to the PBPP Panel Manager for collation and an audit record compiled for each application.
ResultsApplications were identified where a discrepancy existed between the Tier 1 decision and the PBPP Committee audit review. These audit records were tabled for discussion at a workshop involving a subgroup of PBPP Committee and Tier 1 panel members. From the ten applications that were randomly selected, six were consistently reviewed by both the Tier 1 and Tier 2 Committee with no referral points identified from the either Tier. 4 were identified for discussion in a workshop including representatives from both Tiers. During the discussion it was agreed that 2 out of the 4 should have triggered a further review by Tier 2 but that the decision to approve all 4 applications would have remained.
Conclusion/ImplicationsThis suggests that both Tiers have a sound understanding of the proportionate governance criteria and that for the majority of applications this is being interpreted uniformly and that the audit process is required to ensure this is maintained going forward.
Clinically driven analysis reveals gene-socioeconomic status interaction influencing periodontal disease in the electronic health record-linked Generation Scotland: Scottish Family Health Study (GS: SFHS) cohort.
Authors: Bermingham Mairead L , Campbell Archie , Porteous David , Walls Angus 
Published: September 2018

IntroductionHeritability (proportion of trait variation attributable to genetic factors) is not a fixed property. It can vary across different social settings and environments. Exploration of gene-environment interaction has been limited by lack of large sample sizes. Biobanks linked to electronic health records pose a solution to this sample size problem.
Objectives and ApproachSocial inequalities in periodontal health have been well documented in the dental scientific literature. However, gene-socioeconomic status interaction has yet to be examined. We identified 2,192 cases and 11,525 controls from linked electronic periodontal treatment records within the Generation Scotland: Scottish Family Health Study (GS: SFHS) (www.generationscotland.org). The measure of socioeconomic status used was the Scottish Index of Multiple Deprivation. The objective of this study was to investigate the gene-socioeconomic status interaction within this data. A reaction norm model was used to evaluate the presence of a gene-socioeconomic status interaction in the statistical software ASReml.
ResultsWe estimated the heritability of periodontal disease at 10.42% (95% confidence interval 5.97-14.88%). Socioeconomic status modified the heritability of periodontal disease. The heritability of was 13.37%, 0.14% and 11.70% in areas of high, moderate and low deprivation respectively; indicating the occurrence of a gene-socioeconomic status interaction with periodontal disease. These results indicate that socioeconomic status explains a large portion of genetic variation in periodontal disease risk. This information suggests that effective intervention and prevention programs for periodontal disease should involve socioeconomic aspects in their planning, implementations and evaluation. For instance, interventions targeted to reduce smoking in more deprived subjects with a genetic predisposition to periodontal disease could enhance the effect of health promotion strategies in reducing risk.
Conclusion/ImplicationsThis study presents contemporary evidence in a large population based cohort that gene-socioeconomic interaction leads to the progression of periodontal disease. This information may lead to the development of better preventative strategies for clinical dentistry.
A Regional Collaboration of Health (ARCH): Using health survey and linked routine data to understand wellbeing
Authors: Torabi Fatemeh , Akbari Ashley , Lyons Jane , Castagnet Mathilde , Lyons Ronan 
Published: September 2018

IntroductionMonitoring social wellbeing and its relationship to health service utilisation by means of appropriate measurement tools can potentially provide a complementary view for influencing service development. Aspects of wellbeing have been collected in the Welsh Health Survey (WHS) while routine health data captures health service utilisation.
Objectives and ApproachWHS was used to link self-reported wellbeing to health outcomes, prior to linking to routinely collected data. Initially, a measure for personal wellbeing was developed using the four personal wellbeing questions defined by The Office of National Statistics (ONS), included in national surveys from 2011 onward. We conducted regression analysis to identify potential predictors of personal wellbeing scores our model included self-reported lifestyle behaviour, self-reported health, education, work, household and general demographics. Links to primary care, hospital and emergency department datasets are being developed to provide insight into the relationship between wellbeing, multi-morbidity and health service utilisation.
ResultsFour wellbeing questions had similar scoring patterns across age groups which is different to most health indicators that tend to show a marked health decline with increasing age. There is a difference between mean wellbeing score for males and females. Our finding showed that self-reported of ‘excellent’ or ‘very good’ general health has the largest positive effect on wellbeing while positive viewpoint on self-health has the second largest effect on our model. In addition, being retired from a paid job, eating at least one or 5+ portion of fruit and vegetables and giving up smoking have positive impact on population wellbeing. In contrast, not being able to work, intermediate household occupancy, consuming alcohol in last 12-months, having long-standing illness, showed a negative impact on wellbeing.
Conclusion/ImplicationsThis project established robust methodology on utilizing survey and routine health data for monitoring and evaluation purposes. We also evaluated the linkability of survey data The latest release of National Survey for Wales (NSW) will cover a combination of self-reported health measures and aims for a higher linkage consent rate.