bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.