bluuebunny/crossref_metadata_2025 huggingface dataset:
PATHS Data Resource: A population-based suite of linkable administrative records and metadata for population health research
Authors: Nickel Nathan , Brownell Marni , Chateau Dan , Katz Alan , Burland Elaine
Published: April 2017

ABSTRACT

ObjectivesOur objective was to develop a comprehensive longitudinal data resource, which population health research scientists could use to study the social determinants of child health and health equity.

MethodsThe PATHS Resource was created from data holdings within the Manitoba Population Health Research Data Repository. The Manitoba Health Registry sits at the centre of the Repository and includes information – including a scrambled personal health identification number (PHIN) and date when coverage commenced and expired – on every individual registered with the province’s universal healthcare system. The Repository also includes administrative data spanning several sectors including health, social services, justice, and education. We used individuals’ scrambled PHINs to link children’s administrative records across sectors to build a holistic picture of their health and development. We developed metadata, including routinized SAS algorithms and variable definitions, to ensure consistent operationalization of variables across studies. The longitudinal nature of these data allowed us to construct individual-level health and development trajectories from birth through adolescence for children born from 1984-2014. We used income data from the Canadian Census to develop both indicators of socioeconomic status (average neighbourhood level income) and provincial measures of income inequality (the Gini coefficient).

ResultsThe PATHS Resource includes data on the social determinants of health as well as health and development for children born 1984 to 2014 (n=608,007). We are able to follow children for a median observation period of 15.4 years. Income inequality – measured using the Gini coefficient – increased from 1984 to 2014: 0.16 to 0.21 (p<0.01). The proportion of children born to women from the bottom income quintile (i.e., the poorest 20% of families) also grew from 23.2% in 1984 to 27.2% in 2014 (p<0.01). When we followed children over their life course, we found that they were most likely to experience poverty (measured by family receipt of income assistance) at 2 years of age (p<0.01). Many studies from a variety of researchers have utilized the PATHS metadata to conduct child health and development research, ensuring consistent variable operationalization. These data have been used to identify policy levers for improving child health and reducing health inequalities.

ConclusionA resource such as the PATHS Resource can facilitate research into the health and development of children. Having data on the entire population allows investigators to both monitor trends in health inequities and identify strategies for improving health. Metadata ensure variable consistency and comparability across studies.
Patient-level data linkage across ambulance services and acute trusts: assessing the potential for improving patient care
Authors: Clark Sophie , Damiani Michael , Dorning Holly , Halter Mary , Porter Alison
Published: April 2017

ABSTRACT

ObjectivesThe potential of linked healthcare data to support improvements to the care quality, efficiency and service planning is recognised by the Department of Health and NHS England. UK ambulance services however, have little information regarding the outcome of patients as there is no routine data sharing. An urban ambulance service has previously piloted linked data with one acute trust. The Pre-Hospital Emergency Department Data Linking Project (PHED Data) aims to assess the potential opportunities for and challenges to routinely linking data for several sites. It aims to define what indicator sets can be developed from these linked data, with a view to informing commissioning and improvement of healthcare delivery.

ApproachThe project is a two-year mixed-methods observational study, funded by the Health Foundation, working with six acute trusts of various size and CQC-derived performance, to carry out six work packages.

Work package one uses liaison activities with trust senior staff to negotiate information sharing agreements, and a learning log to enable an economic assessment of the set-up costs.

Work packages two to five analyse ambulance response time, referrals from healthcare professionals, ED mortality, and frequent ED diagnoses, respectively, exploring relationships with ED outcomes, quantitatively. Qualitative analysis will explore, with staff groups, how the findings might influence commissioning and pre-hospital care.

Work package six will examine commissioning decisions and patient care, through interviews with commissioners and performance managers.

ResultsIn work package one, we have successfully negotiated overall research ethics and governance approval, involving in-depth discussion about definitions of identifiable patient data, and protecting against potential re-identification. We have recruited six acute trusts, comprising 13 hospital sites, for whom equivalent data is available. We have been working for six months in liaison with these Trusts to deliver information sharing agreements (three are currently approved) and data transfer, working with research governance, information governance and clinical staff. The processes have varied, with intra-Trust co-dependencies introducing delays and non-linear processes being common.

For work packages two to five, some data analysis will be available for presentation at the conference.

ConclusionThis project has the potential to shed light on the practicalities of data linkage for health service providers who face similar challenges with patient data held in multiple organisations.

The study also anticipates being able to recommend quality improvement to support the development of new pathways in pre-hospital care.
Data linkage and statistical modelling to provide stratified risk assessment for HAI
Authors: Kavanagh Kim , Pan Jiafeng , Robertson Chris , Bennie Marion , Marwick Charis , McCowan Colin
Published: April 2017

ABSTRACT

ObjectivesThe use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI).

This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making.

ApproachWe demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence.

ResultsIn the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression.

ConclusionDespite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making.
Linked Data Paves the Way to Improved Health Care
Authors: Gaucher Michael , Nielsen Britta , Gapanenko Katerina
Published: April 2017

ABSTRACTObjectivesOur organization links data from health care organizations and health ministries across Canada to create new knowledge. The new knowledge supports system and policy-decision makers in answering questions about the health or healthcare of population groups or healthcare sectors. This presentation provides an overview of recent analytical products that incorporate a variety of approaches to linking data.

ApproachKey success factors in data linkages are the use of standards and robust methodologies. Standardized data can be linked at the individual level across hospital, long-term care, home care and community settings. Expanding knowledge about a specific sector can be achieved by linking physician billing, drug, and financial data. Linking clinical and financial data at the person and/or organization level provides insight into the cost of providing services, laying the foundation for examining efficiency and value for money. Data may also be used in non-traditional ways, for example, using payment data linked to clinical data to better understand how services are provided in various settings and to identify best practices.

ResultsResults from these studies demonstrate how linked data creates new knowledge: 1) Hospital, emergency, and drug data were linked to show the number of emergency visits, hospitalizations, and the use of psychotropic medications by children and youth with mental health disorders are increasing, especially for those with mood and anxiety disorders living in urban areas. 2) Physician billing data was linked with hospital and emergency data to show that increased continuity of care with a family physician is associated with reductions in hospitalizations for people with chronic diseases and reductions in ED visits for people with conditions better managed in primary health care settings. 3) Drug data was linked with clinical assessment (InterRai) data to show that a large proportion of seniors living in long-term care exhibiting severe aggression were not being treated with antipsychotics, suggesting non-drug alternatives were often considered. 4) Financial data was linked with hospital clinical data to calculate an efficiency indicator that can be used at a system level to examine variation, and by individual managers to identify areas for further investigation and manage trends in spending.

ConclusionThis presentation demonstrates how new knowledge created using linked data can inform decision and policy makers, and lead to action within the health system. Several examples are presented to illustrate the approach and types of data being linked.
Combining deterministic and probabilistic matching to reduce data linkage errors in hospital administrative data
Authors: Hagger-Johnson Gareth , Harron Katie , Aldridge Rob , Fu Bo , Setakis Efrosini , Goldstein Harvey , Gilbert Ruth
Published: April 2017

ABSTRACTObjectivesData linkage algorithms are used to link together multiple episodes of care belonging to the same patient. For example, the HESID algorithm is used to generate Hospital Episode Statistics (HES) in England. HESID is a deterministic algorithm, requiring identifiers to agree or disagree at each step. Data linkage errors occur when episodes belonging to two patients are incorrectly linked (a false match) or when episodes belonging to the same patient are not linked (a missed match). This typically occurs because patient identifiers (e.g. NHS number, postcode) contain errors or have missing data. We previously showed that HESID has a low false match rate (0.2%) but a high missed match rate (4.1%) when applied to paediatric intensive care data. This biased the true readmission rate, particularly for some patient groups including ethnic minorities. The aim of our study was to evaluate whether an additional step involving probabilistic matching would lower the missed match rate in HES without increasing the false matched rate.

ApproachWe simulated three datasets having the same characteristics as HES, for three age groups expected to have different levels of postcode stability (at age 0/1, 5/6 and 18/19). We compared the deterministic algorithm to a probabilistic algorithm, and then to a deterministic algorithm with an additional probabilistic step. In sensitivity analyses, we evaluated the algorithms under different data quality scenarios.

ResultsResults show that deterministic followed by probabilistic matching is the best solution for reducing missed matches, particularly in scenarios where errors in patient identifiers are more common.

ConclusionData linkage algorithms need to be evaluated against good quality reference standard data sets. For hospital data in England, the Personal Demographics Service (PDS) could be used to evaluate our approach, because it contains many of the same patient identifiers used in HES. Reducing data linkage error will improve monitoring of hospital activity in England.
Trends in prevalence, incidence, health system use and cost by persons with dementia in Ontario from 2004 and 2013: a population-based study
Authors: Bronskill Susan , Guan Jun , Vermeulen Marian , Yates Erika , Ng Ryan , Wang Xuesong , Walker Jennifer , Maxwell Colleen
Published: April 2017

ABSTRACTObjectiveEfforts to enable persons with dementia to remain at home longer, and to reduce use of costly acute care resources, are at the forefront of policy agendas internationally. Foundational to planning appropriate health system supports is the ongoing, comparable and accurate estimation of the prevalence and incidence of dementia across regions, as well as associated patterns of health services use and cost. Our objective was to explore emerging approaches to using population data in dementia research and demonstrate the policy contribution of the resulting new knowledge.

ApproachUsing population-based health administrative data and an algorithm that was validated using electronic medical records, we developed a series of repeated, cross-sectional cohort studies to examine trends in dementia prevalence, incidence and publicly-funded health service use and costs between 2004/05 and 2013/14 among adults aged 65 years and older in Ontario, Canada. Trends in yearly rates of health service use were assessed using regression models for serially correlated data and compared to a 1:1 matched control group based on age, sex, geographic region and comorbidity level.

ResultsOver time, age- and sex-adjusted prevalence of dementia increased by 18.2% (from 63.0 to 74.5 per 1,000 persons; p-value < 0.001) and age- and sex-adjusted incidence decreased slightly (from 18.2 to 17.0 per 1,000 persons; p-value = 0.05). Community-dwelling persons with dementia were more likely than matched controls to be placed in long-term care (11.8% vs. 1.5% in 2013; p<0.001) and use home care (45.8% vs 23.2%; p<0.001) but equally likely to visit family physicians (93.9% vs. 94.8% in 2013) and specialists (87.1% vs. 89.4%). Median costs associated with one year of health system use were $19,468 (interquartile range (IQR) $4,490 to $47,726) for prevalent cases in 2012/13 and $16,549 (IQR $5,070 to $47,899) for incident cases. Long-term care and hospital care accounted for the largest portion of total costs in both groups.

ConclusionThe prevalence of dementia has increased in Ontario, Canada over time and, given slightly declining incidence rates, is likely attributable to improved survival. Surveillance of dementia with health administrative data is a cost-effective tool for describing and monitoring trends in incidence and prevalence over time, and for supporting health system capacity planning. This comparative information is critical to understanding the impact of policy decisions designed to address dementia-related health care needs at a population level.
A suggestion on how to include calendar dates in Bloom Filters
Authors: Stevens Antony
Published: April 2017

ABSTRACT

ObjectiveBloom Filters have been used in a number of studies conducted for the Ministry of Health. They are usually recommended because of the possibility that they may participate in secure protocols for the exchange of data. In our case the speed of the program, once the filters have been prepared, is so high that that itself is sufficient motive for their adoption.

Nevertheless if two calendar dates differ by one character this may merit more attention than a similar difference in personal names. This became evident in a large linkage between mortality records and hospital separations where the patient had died. Higher scores were obtained when the date fields differed by only one character, but when that character represented a year there would no reason to notice the pair. When the character difference was compatible with a difference of a few days this would be more interesting because in studies like the one just cited it would be reasonable to admit differences of a few days or even, perhaps, weeks between the events ( recording of the death of the patient ).ApproachHow then to represent the difference between dates in a Bloom Filter? A date can be represented as a Boolean vector where the day (or week) is set to '1'. It may be represented by several contiguous '1's to admit admissible uncertainty in comparisons. The similarity between two dates can then just be the Dice Coefficient of the corresponding vectors.

ResultBut a vector representing a date may then be very large. It could be as much as 365 bits per year, far more than is usually used for the other fields. The number of logical word comparisons would go up and the program would become slower. Knowing that the admissible range is presented by contiguous '1's means that we can obtain the effect of constructing the Bloom Filter and calculating the Dice Coefficient more directly. Starting with the two dates we can obtain the number of bits that are shared, which will depend on the admissible range. The Dice Coefficient can then be calculated directly without the need to construct the Filter.

ConclusionWe are then left with the decision on how to add the result to the value obtained from the other variables, and this will depend on what importance it is felt the date should have.
Timeliness of recording in the Clinical Practice Research Datalink (CPRD) – an initial step in the implementation of near real-time vaccine safety surveillance
Authors: Leite Andreia , Andrews Nick , Thomas Sara
Published: April 2017

ABSTRACTObjectiveNear real-time vaccine safety surveillance (NRTVSS) using electronic health records (EHR) is an option for post-licensure vaccine safety assessment. NRTVSS requires timely recording of outcomes in the database used. Our study aimed to examine recording delays in the Clinical Practice Research Datalink (CPRD) to inform the feasibility of implementing NRTVSS in England using these data. 

ApproachTo examine delays we selected 4 outcomes of interest for NRTVSS: Guillain-Barre syndrome (GBS), Bell’s palsy (BP), optic neuritis (ON), and seizures for the period January 2005 to July 2015. Timeliness of CPRD records was assessed in two ways: 1) Using linked CPRD-hospital episode (HES) data to compare the hospital diagnosis date with the date the record was entered in CPRD (system date), 2) Looking at delays in recording (e.g. due to feedback from specialist referral) in stand-alone CPRD. For the latter the event date was compared with the system date. However, system dates can be changed when practice software is updated or there is mass transfer of a patient’s records. After investigation, we excluded these uninformative system dates by excluding records from patients who had more than 100 records with the system date on the same day.

Results67813 patients were identified in CPRD (GBS:n=1081, BP:n=15835, ON:n=2236, seizures:n=48866), 64527 in HES (GBS:n=1680, BP:n=8468, ON:n=1746, seizures:n=53080) and 14104 in both databases (GBS:n=356, BP:n=1511, ON:n=226, seizures:n=12036). For the CPRD-HES comparison, 11843 patients with a diagnosis of interest both in CPRD and HES were included (GBS:n=321, BP:n=1374, ON:n=190, seizures:n=9976). Of these, the majority had a record in CPRD before or within 1 month of the HES record (GBS:49.5%, BP:83.8%, ON:66.8%, seizures:69.8%). For 6 months the corresponding percentage was more than 85% for all conditions examined (GBS:85.4%, BP:92.9%, ON:90.0%, seizures:86.6%). For stand-alone CPRD 57317 patients were included (GBS:n=972, BP:n=14275, ON:n=1958, seizures:n=40327). The majority had a record within one month of the event date (GBS:67.9%, BP:89.3%, ON:71.8%, seizures:83%). More than 87% of records occurred within 6 months of the event date (GBS:87.9%, BP:94.4%, ON:91.6%, seizures:94.9%).

ConclusionThis work shows that most diagnoses examined were recorded with a delay of ≤30 days, making NRTVSS possible. The distribution of the delays was condition-specific and the weekly delay distribution could be used to adjust for delays in the NRTVSS analysis. CPRD can be a viable data source to use in this kind of analysis; next steps will include trial implementation of the system using these data.
Evaluating the care received by patients with oesophago-gastric (OG) cancer: the richer picture provided by linked datasets
Authors: Brand Christian , Varagunam Mira , Chadwick Georgina , Cromwell David
Published: April 2017

ABSTRACTObjectivesThe national oesophago-gastric cancer audit evaluates the care given to patients with OG cancer diagnosed in England and Wales, identifying areas for local services to improve and so achieve better patient outcomes. While the audit follows a standard approach by collecting a core dataset, it extends this by linking to other national databases in order to (1) reduce the burden of data collection, (2) add data that could not otherwise be obtained, (3) verify the quality of audit data. This presentation will provide an overview of the modern approach to national clinical audits and introduce anticipated future developments.

ApproachThe data collected by hospitals and submitted to the audit is expanded by linkage to radiotherapy data (RTDS), date of death from the Office for National Statistics and administrative hospital admission data (HES). The linked data enables the Audit to: (1) examine whether radiotherapy regimens used in definitive oncology comply with guideline recommendations, using dosage information drawn from RTDS; (2) publish comparative short-term survival figures for NHS trusts and surgeons using ONS date of death; and (3) assess the quality of data submitted from hospitals by comparing coded procedural information in HES and the audit dataset.

ResultsThe analysis of the linked data has led to several important audit findings, namely: (1) Access to RTDS data confirmed reasonable compliance with official guidelines on recommended radiotherapy dosing schedules. (2) Linked mortality data has provided robust and credible estimates of surgical outcomes and is a critical output to NHS trusts and surgeons, as well as the wider public. Our analysis has highlighted a gradual decline in post-operative mortality. (3) Linkage of audit and HES data identified gaps in the recording of endoscopic stenting, with many procedures not being submitted by participating trusts. These diverse results underline the usefulness of linked data in revealing a more complete picture of the care received by OG cancer patients.

ConclusionLinked data has become integral for the successful implementation of a national clinical audit, supplementing the core dataset with information that would otherwise be unavailable or difficult and costly to collect. Linkage enhances the audit’s ability to assess health services’ compliance with professional standards and to give service providers the opportunity to benchmark their performance. Future developments are expected to expand on this by including primary care service use before diagnosis.
Creating a platform for collaborative genomic research
Authors: Smithson Mark , Frost James , Williams Julie , Simms Rebecca , Majounie Elisa , Kitchen Christine , Badarinarayan Nandini , Moreton Andrew , Denning Nicola , Bannister Christian
Published: April 2017

ABSTRACT

ObjectivesThe objective was to deliver a platform to accelerate collaborative research into the genetics of dementia. The project is part of a larger effort to establish a platform for research incorporating components related to epidemiological data, imaging, wearable technology and tissue banking with initiatives to link these different data sets.

The genomics platform aimed to address a number of challenges encountered by researchers:

Enabling patient-level linkage between genomic and other datasets

Selection of study cohorts using genotypic and phenotypic characteristics

Collaborating with other groups to perform large-scale meta analysis

Supporting repeatable analysis workflows

ApproachThe genomics platform was developed using a mixture of open source, commercial and bespoke components developed for this project. It is designed to securely handle data and scale to cope with increasing data volumes as well as the quantity and complexity of research undertaken.

It is located in a data centre where researchers can access, explore and analyse the data in a secure environment addressing data security and privacy concerns.

The platform integrates with a supercomputer allowing complex analysis of data to be undertaken easily using novel code or predefined workflows.

The platform was designed and developed through close collaboration with a prominent academic research team.

ResultsWe have created a collaborative genomics informatics platform that provides efficient and intuitive selection of study cohorts from multiple data sources, with potentially varying data formats and types, based on both genotypic and phenotypic characteristics. Researchers can combine cohorts definitions using set operations and publish these definitions allowing other researchers to apply them to other datasets.

There is an internet accessible portal which allows researchers to share results of their analysis and support meta analysis of this research. A sophisticated search engine allows these results to be found based on genetic information or annotations. This portal accelerates meta-analysis, an intrinsically collaborative endeavour in genomics research.

There is also close integration with HPC (High Performance Computing) resources for computationally expensive tasks. The platform is underpinned by analytical workflows that not only accelerate and enable reproducible research, but reduce the technical barriers to its use.

The platform enables patient-level linkage with other non-genomics data sources through integration with other research platforms.

ConclusionThe developed genomics informatics platform provides a step-change in this type of genetic research, accelerating reproducible collaborative research across multiple disparate organisations and data sources, of varying type and complexity.
Midlife and late-life blood pressure and vascular dementia: a population based observational study
Authors: Peng Mingkai , Chen Guanmin , Tange Karen , Campbell Norman , Smith Eric , Faris Peter , Quan Hude
Published: April 2017

ABSTRACT

ObjectiveHypertension and dementia are common disorders in the elderly. The objective of this study is to investigate the association of blood pressure in midlife and late life with the risk of vascular dementia (VaD) using United Kingdom (UK) primary care data.

ApproachWe conducted a retrospective population-based cohort study using The Health Improvement Network (THIN). Two independent study cohorts were created: 1) midlife: individuals aged 60 to 65 with at least 1 blood pressure measurement between the age of 60 to 65; 2) late-life: individuals aged 70 to 75 with at least 1 blood pressure measurements between the age of 70 to 75. The baseline blood pressure for midlife and late-life cohort were categorized into four levels: normal, prehypertension, stage 1, and stage 2 hypertension. Cases of VaD and other risk factors, such as stroke, diabetes, were identified using the Read codes listed in the Quality and Outcome Framework- National Health Science (QOF-NHS). Descriptive statistics were used to compare the demographics and clinical conditions for patients in different blood pressure levels. we used the proportional subdistribution hazard model was used to estimate the association between blood pressure levels with risk of VaD while treating death as a competing risk. Multiple imputation method was used to impute the missing values for the variables of smoking status and Body mass index (BMI) categories

ResultsIn total, there were 265 897 patients (65.1% with stage 1 and 2 hypertension) in the midlife and 211 909 patients (76.3% with stage 1 and 2 hypertension) in the late life cohort, respectively. For midlife cohort, the risk of VaD increased with higher BP levels after adjusting the potential risk confounding factors with a hazard ratio (HR) of 1.15(95% confidence interval 0.88, 1.51) for prehypertension, 1.32(1.01, 1.73) for stage 1 hypertension, 1.33(1.01, 1.73) for stage 2 hypertension; for late-life cohort, after adjusting all the confounding factors, there was no statistically significant association between the risk of VaD and BP levels (HR 1.09 [95% confidence interval 0.86, 1.39] for prehypertension, 1.17(0.92, 1.48) for stage 1 hypertension, and 1.11(0.88, 1.42) for stage 2 hypertension).

Conclusionour study show high blood pressure in midlife is a significant risk for vascular dementia in the late-life. Given the strength and consistency of this evidence, greater effort should be placed for early diagnosis of hypertension and tight control of blood pressure for hypertensive patients in the prevention of vascular dementia.
Methodological research priorities for data sciences: Report from The International Methodology Consortium for Coded Health Information (IMECCHI)
Authors: Jolley Rachel , Southern Danielle , Quan Hude , Ghali William , Burnand Bernard
Published: April 2017

ABSTRACT

ObjectivesThe vast amount of data produced by healthcare systems both structured and unstructured, termed ‘Big Data’ have the potential to improve the quality of healthcare through supporting a wide range of medical and healthcare functions, including clinical decision support, disease surveillance, and population health management. As the field of big data in healthcare is rapidly expanding, methodology to understand and analyze thereby enhancing and optimizing the use of this data is needed. We present priorities determined for future work in this area.

ApproachAn international collaboration of health services researchers who aim to promote the methodological development and use of coded health information to promote quality of care and quality health policy decisions known as IMECCHI –proposes areas of development and future priorities for use of big data in healthcare. Thematic areas were determined through discussion of potential projects related to the use and evaluation of both structured /codeable and unstructured health information, during a recent meeting in October 2015

ResultsSeveral themes were identified. The top priorities included: 1) electronic medical record data exploration and utilization; 2) developing common data models and multimodal /multi-source databases from disparate sources development; 3) data quality assessment including developing indicators, automated logic checks and international comparisons; 4) the translation of ICD-10 to ICD-11 through field-testing 5) Exploration of non-physician produced/coded data; and 6) Patient safety and quality measure development.

ConclusionsA list of expert views on critical international priorities for future methodological research relating to big data in healthcare were determined. The consortium's members welcome contacts from investigators involved in research using health data, especially in cross-jurisdictional collaborative studies.
Do Individuals with Depression and Comorbid Medical Conditions Receive Poorer Mental Health Care than those with Depression Alone?
Authors: Puyat Joseph , Kazanjian Arminee
Published: April 2017

ABSTRACT

IntroductionThe prevalence of depression is 2 to 3 times higher in individuals with comorbid medical condition (CMC) than in the general population. When untreated, depression results in increased mortality, higher health care costs, greater functional disability, decreased quality of life and lower adherence to treatment regimens for the CMC. Currently, studies that examined whether depression care in those with CMC is better or worse compared to those with depression alone show inconsistent results. Furthermore, studies that compare depression care in those with specific CMCs are scarce. Improving knowledge base in this area will enable health care systems to better allocate limited resources to clinical population that need them most.

Aims1) To estimate disparities in depression care in those with one or more CMCs, and 2) to examine if depression care patterns in those with CMCs have been impacted by recent provincial policies.

MethodWe retrospectively examined data from physician claims, hospital separations, vital statistics, and insurance plan registries. Using this linked data, we identified dynamic cohorts of individuals with depression and CMCs in 2005 and 2012, based on the earliest date of depression diagnosis. Each cohort had exactly 12 months of lookback period for case ascertainment and 12 months of follow-up for tracking depression care patterns. The following indicators were tracked: 1) receipt of any psychological therapy, 2) receipt of any antidepressants (AD), 3) receipt of any depression treatment, 4) number of GP visits, 5) GP continuity of care index for all visits, 6) GP continuity of care for mental health visits, 6) counts of psychological therapy sessions, 7) proportion of days covered for AD, and 8) continuous medication gap for AD. Disparities in depression care were examined used generalized linear regression models.

Results and ConclusionsUse of depression care in individuals with one or more CMC were higher across most indicators we examined except for AD initiation and GP visits for mental health reasons. In specific CMCs like cerebrovascular disease and diabetes, depression care in some areas were lower. Overall, depression care patterns seemed unchanged after the introduction of relevant provincial policies, although improvements over time appeared to have been made in certain areas.
The utility of historical electoral roll records and their effect on the association of regularity of GP contact and potentially preventable diabetic hospitalisations
Authors: Moorin Rachael , Youens David
Published: April 2017

ABSTRACTObjectivesCorrectly ascertaining person-time at risk is paramount to longitudinal studies of health services research and relies on the ability to track the status of individuals throughout the study. Administrative health data contain limited information on where study subjects live in the time between episodes of health service use. Accurate ascertainment of person-time at risk is important particularly when it varies differentially across exposure groups or covariates. Historical electoral roll data allows better specification of person-time and also provides longitudinal information on geographic location facilitating the inclusion of accessibility and socio-economic status longitudinally. This study evaluated the utility of Australian historical electoral roll data to capture place of residence throughout the time line of a whole of population longitudinal cohort study over 20 years to better ascertain person-time at risk and changes in socio-economic status (SES).ApproachThe association between regularity of GP contact and potentially preventable hospitalisations (PPHs) in WA was modelled using person-level linked data where the time at risk and socio-economic status for both the exposure (regularity of GP contact) and outcome (PPHs) was assumed to be constant throughout the follow up until death. The analysis was then repeated incorporating historical (longitudinal) electoral roll data. These data partitioned follow up time and socio-economic status according to location of residence within the State to better characterise access and SES and included removal (out-of-State/country migration) and re-enrolment records.ResultsSubstantial differences were found in the number of people at risk (46,625 (13%) of people were never at risk) and person-time at risk (reduction of 473,708 (22%) person-years at risk) when cross-sectional electoral roll and health administrative data were augmented using historical electoral roll data. Substantial changes in residential postcode (up to 25 changes) were observed and these impacted on the accessibility and SES classification across the duration of the study. These changes significantly impacted the magnitude of the relationship between GP contacts on PPHs determined by models.ConclusionsCurrently cross-sectional electoral roll data are available to researchers in WA solely for the purposes of identifying and characterising a cohort at baseline. However these data are longitudinal and contain important information that improve analyses. Information on their utility is important so as to leverage their availability from the Australian Electoral Commission.
Child Injury: Does home matter?
Authors: Cameron Cate , Osborne Jodie , Davey Tamzyn , Spinks Anneliese , Sipe Neil , McClure Rod
Published: April 2017

ABSTRACTObjectivesTo examine the relationship between home risk and hospital treated injury in Australian children up to five years of age. ApproachWomen with children between two and four years of age enrolled in an Australian longitudinal birth cohort study, Environments for Healthy Living (EFHL) between 2009 and 2011, were invited to complete a Home Injury Prevention survey. A total home risk score (HRS) was calculated and linked to EFHL baseline demographic survey and perinatal data. The child’s injury related state-wide hospital emergency department and admissions data was extracted from the child’s date of birth until 31 December 2013. Multiple imputation methods were used to impute incomplete multivariate exposure factors. Zero-inflated poisson regression was used to estimate crude and adjusted rate ratios (RRs) between exposure (HRS quartiles) and outcome (count of injury related episodes of care) for child participants, accounting for individual person-years of exposure time.ResultsData from 566 child participants were included with outcomes data available for 97% of the cohort. Using data linkage of administrative health records with multiple survey data sources, we found an inverse relationship between home risk and child injury, with children living in homes with the least injury risk (based on the absence of hazardous structural features of the home and safe practices reported) having 1.90 times the injury rate of children living in high risk homes (95% CI 1.15-3.14). Whilst this appears counter-intuitive, families in the lowest risk homes were more likely to be socio-economically disadvantaged than families in the highest risk homes (more sole parents, lower maternal education levels, younger maternal age and lower income). After adjusting for demographic and socio-economic factors, the relationship between home risk and injury was no longer significant (p>0.05). Discussion and conclusionsThrough the use of comprehensive linkage of five years of state-wide administrative health data, combined with individual survey data methods, this study overcame the most significant threat to longitudinal research – loss to follow-up. Our results demonstrated that children in socio-economically deprived families have higher rates of injury, despite living in a physical environment that contains substantially fewer injury risks than their less deprived counterparts. These findings support continued efforts to implement societal-wide, long term policy and practice changes to address the socioeconomic differentials in child health outcomes.
Development of data-driven framework for automatically identifying patient cohorts from linked electronic health records
Authors: Fernández-Gutiérrez Fabiola , Kennedy Jonathan , Cooksey Roxanne , Atkinson Mark , Choy Ernest , Brophy Sinead , Zhou Shang-Ming
Published: April 2017

ABSTRACTObjectives 1) To develop a fully data-driven framework for automatically identifying patients with a condition from routine electronic primary care records; 2) to identify informative codes (risk factors) of arthropathy conditions in primary care records that can accurately predict a diagnosis of the conditions in secondary care records.

ApproachThis study linked routine primary and secondary care records in Wales, UK held in the SAIL (Secured Anonymised Information Linkage) databank, in which the secondary care records were used as golden standard. As such, we proposed to use machine learning techniques to extract patient information and identify cohorts with a condition from the large and high-dimensional linked dataset using the following phases: data preparation, performed in the machine learning context fashion; pre-selection of initial features, ranking and selecting features into a meaningful subset by using feature selection methods; and identification algorithm development which incorporates mechanisms of tackling the imbalanced nature of the data. This data-driven framework was then validated on an independent dataset, and compared with existing algorithm which had been developed using expert clinician knowledge for arthropathy conditions.

ResultsRheumatoid arthritis (RA) and ankylosing spondylitis (AS) were used to demonstrate the feasibility of this framework. Linking primary care records with the secondary care rheumatology clinical system, we collected 9,657 patients with 1,484 RA patients and 204 AS patients. The proposed framework identified various compact subsets of informative features (risk factors) from 43,100 potential Read codes. Applying to an independent test data, this framework achieved the classification accuracy and positive predictive values (PPVs) of 86.19% and 88.46% respectively for RA and 99.23 % and 97.75% respectively for AS, which are comparable with the performance of clinical knowledge-based method - the accuracy of 85.85%, the PPV of 85.28% for RA and the accuracy of 97.86% , the PPV of 95.65% for AS.

ConclusionThe proposed data-driven framework provides a rapid and cost-effective way of reliably identifying patients with a medical condition from primary care data. It performed as well as the clinically derived algorithm. This framework does not intend to substitute clinical expertise, instead it provides an decision support tool for clinicians during their decision process, in particular selection of patients for clinical trials.
Bibliometric Analysis to Scan and Scrape New Datasets: It’s all about that BASS
Authors: Tingay Karen , Perkins Brian , Anastasiou Athanasios
Published: April 2017

ABSTRACTObjectivesThe main objective of this poster is to present a pilot project in determining emerging population health themes and identifying key research-enabling datasets ahead of time.

At present, large-scale databanks, such as the Secure Anonymised Information Linkage (SAIL) Databank at Swansea University Medical School, already manage large quantities of health and administrative linked datasets. While these datasets are valuable for research purposes, complementary datasets may be required by collaborating researchers to answer detailed population health research questions. Dataset acquisition can take several years, which is a serious delay to a project with time-limited funding.

The ability to pre-emptively acquire datasets so that these are ready for use before a researcher requests them would obviously be beneficial. However, a recent study conducted by the Farr Cipher team at Swansea University identified over 800 health and administrative datasets in Wales alone.

With limited resources such as available funding and time, which of these datasets is worth its effort in acquiring?

ApproachBibliometrics has long been a means of measuring the impact of papers on the wider academic community. Lately, the focus of analyses has been extended to include the topics, authorship and citations of the publications. Existing bibliometric data mining techniques suggest that it is possible to identify emerging topic trends and through this assist in prioritising dataset identification and acquisition.

The project explored mining available literature through bibliometric analysis in order to predict emerging trends and through these identify potentially relevant and valuable datasets for acquisition on behalf of the Dementias Platform UK (DPUK). Literature searches were conducted for papers published on the topic of “dementia” over the last 20 years. Additional keywords and topics were extracted to identify emerging areas of research and clinical interest. These were then compared against an existing list of over 800 Welsh datasets currently not held in SAIL.

ResultsResults focus on:

Using bibliometric methods in the context of DPUK cohort publications

Identifying emerging trends in the field of dementia research. 

Identifying and prioritising datasets which might be useful for the SAIL Databank to acquire
Multi-Objective Techno-Economic Assessment of Real Life Hydrocarbon Facility Real Power Loss and Power Factor Optimization Using Improved Strength Pareto and Differential Evolutionary Algorithms
Authors: M.  T.  Al-Hajri  , M. A. Abido  , M. K. Darwish
Published: April 2017

In this paper, a techno-economic assessment of a real life hydrocarbon facility electrical system real power loss and grid connection power factor optimization is presented. This optimization was attained by using the Improved Strength Pareto Evolutionary Algorithm (SPEA2) and the Differential Evolutionary Algorithm (DEA). The study is the first of its kind as none of the previous studies were conducted in the context of a real life hydrocarbon facility’s electrical system. The hydrocarbon facility’s electrical system examined in the study, consists of 275 buses, two gas turbine generators, two steam turbine generators, and large synchronous motors, with both rotational and static loads. For the real life hydrocarbon facility, the performance of the SPEA2 and the DEA were benchmarked in the course of optimizing two competing objectives - power loss and grid connection power factor. The problem was articulated as a constrained nonlinear problem. The constraints were all real values reflecting the system equipment and components’ limitations. The results obtained from the research show the efficiency and prospects of the proposed research in solving the described multiple objectives of the study case. Also addressed in this study the annual cost avoidance, due to the study objectives’ optimization, based on real fuel value.