bluuebunny/crossref_metadata_2025 huggingface dataset:
Rationale Design, Synthesis, Cytotoxicity Evaluation, and Molecular Docking Studies of 1,3,4-oxadiazole Analogues
Authors: Ahsan Mohamed Jawed , Choupra Arun , Sharma Rakesh Kumar , Jadav Surender Singh , Padmaja Pannala , Hassan Mohd. Zaheen , Al-Tamimi Abdulmalik Bin Saleh , Geesi Mohammed H. , Bakht Mohammed Afroz
Published: April 2017

Background:

1,3,4-Oxadiazole heterocycles possess a broad spectrum of biological activities. They
were reported as potent cytotoxic agents and tubulin inhibitors; hence it is of great interest to explore new
oxadiazoles as cytotoxic agents targeting tubulin polymerization.

Objective:

Two new series of oxadiazoles (5a-h and 12a-h) were synthesized, structurally related to the heterocyclic
linked aryl core of IMC-038525, NSC 776715, and NSC 776716, with further modification by incorporating
methylene linker.

Method:

The 2,5-disubstituted-1,3,4-oxadiazoles (5a-h and 12a-h) were synthesized by refluxing an equimolar
mixture of the intermediates [(4) and (8a-d)] and aromatic aldehydes in water-ethanol system using sodium
bisulphite catalyst. The cytotoxicity evaluation was carried out according to the National Cancer Institute (NCI
US) Protocol, while the tubulin polymerization assay kits from Cytoskeleton ™(bk011p) was used to perform an
in vitro tubulin polymerization assay.

Results:

2-(5-{[(4-Chlorophenyl)amino]methyl}-1,3,4-oxadiazol-2-yl)phenol (5f) and 2-[(2,4-dichlorophenoxy)
methyl]-5-(3,4-dimethoxyphenyl)-1,3,4-oxadiazole (12c) showed maximum cytotoxicity with the mean percent
growth inhibitions (GIs) of 71.56 and 72.68 respectively at 10 µM drug concentrations. Both the compounds (5f
and 12c) showed superior cytotoxicity than clinically prevalent anticancer drugs, Imatinib and Gefitinib in one
dose assay. The compound 12c showed promising results in five dose assay, with GI50 values varies between
1.61 and >100 µM. Furthermore, the compounds, 5f and 12c also inhibited the polymerization of tubulin with,
an IC50 of 2.8 and 2.2 µM, respectively.

Conclusion:

The oxadiazoles reported herein are tubulin inhibitors and cytotoxic agents. These findings will be
helpful in future drug design of more potent tubulin inhibitor cytotoxic agents.
A generalization of power and alternating power sums to any Appell polynomials
Authors: Kim Dae , Kim Taekyun
Published: April 2017

The classical power sum and alternating power sum identities can be stated as
?m,i=0 sn(i)= 1/n+1 (Bn+1(m+1)- Bn+1), ?m,i=0(-1)i sn (i) = 1/2
((-1)m En(m+1) + En), where sn(x)=xn is the simplest possible
Appell polynomial for the Sheffer pair (1,t). The impetus for this research
starts from the question that what if we replace sn(x)=xn by any Appell
polynomial. In this paper, we give a generalization of power and alternating
power sums to any Appell polynomials.
Measuring precision for deterministic and probabilistic record linkage
Authors: Kindermann Bindi , Chipperfield James , Hansen Noel , Rossiter Peter , Wright Jeffrey
Published: April 2017

ABSTRACT

ObjectivesVarious organisations are increasingly linking administrative, survey, and census data to enhance dimensions such as time and breadth or depth of detail. Because a unique person identifier is often not available, records belonging to two different people may be incorrectly linked. Estimating the proportion of links that are correct, called precision, is difficult because, even after clerical review, there will remain some uncertainty about whether a link is in fact correct or incorrect.

This presentation proposes some methods for estimating precision when using either deterministic (rules-based) or probabilistic linkage. These methods are model-based and do not require clerical review. The main uses of these methods are to estimate:

1. Precision during the linking process. This is useful to refine how linkage is carried out, such as the choice of linking variables and weight thresholds.

2. Precision after the files are linked. This provides a useful "quality indicator" of the linked data.

ApproachTwo methods of estimating precision are described:

1. Simulation – the linking process is simulated many times, whether it is probabilistic or deterministic. The key step being the simulation of the agreement pattern between data sets, based on underlying probabilities.

2. An algebraic estimator – this is applicable for deterministic linking only, and provides a quicker way of estimating precision.

Both methods are investigated using two studies: (i) synthetic data (ii) real data (death registrations linked to census data).

ResultsThe estimators perform very well using both the synthetic and real data, even when assumptions about the independence of linking variables are violated. This suggests that the estimators are robust against moderate violations of these assumptions.

ConclusionThe proposed estimators of precision are a very useful addition to the record linkage tool kit, providing methodical, faster, and cheaper alternatives to many present strategies that rely on clerical review. Estimates of precision are useful in the planning, process, and analysis of record linkage activities.
How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources
Authors: Glock Barbara , Endel Florian , Endel Gottfried , Sandholzer Klaudia , Popper Niki , Rinner Christoph , Duftschmid Georg , Filzmoser Peter , Mert Mehmet Can , Holl Jürgen , Wagner-Pinter Michael
Published: April 2017

ABSTRACT

ObjectivesIn healthcare it is crucial to have a fundamental knowledge of the burden of diseases within the population. Therefore we aimed to develop an Atlas of Epidemiology to gain better insight on the epidemiological situation. Based on primary and secondary health care data, we aimed to present results in interactive charts and maps, comprehensible to experts and the general public. The atlas builds a framework for rapid deployment of new data and results in a reproducible and efficient way. As a first use case three methods based on two different databases for the estimation of diabetes prevalence in Austria are compared.

ApproachDatasources: (i) reimbursement data 2006/2007 (GAP-DRG); (ii) national routine health survey (ATHIS) for 2006/2007. Methods for diabetes prevalence estimation: 1) ATC-ICD statistically relates pseudonymized data on medications to data on diagnoses from hospitalizations and sick leaves. 2) With the method Experts, medical experts assign specific medications to diabetes diagnoses. Patients with these medications are identified together with hospitalized diabetes diagnosed patients in GAP-DRG. 3) In ATHIS a sample of 15.000 persons was questioned if they a) ever had diabetes and b) were treated against diabetes in the last 12 months. Results are projected onto the Austrian population. Patients are divided by 10-year age-classes, gender and state. For the publicly online framework, implemented in html and javascript, pre-processed data in different granularity is required and used.

ResultsMaps of Austria represent the prevalence of diabetes for each method and granularity level. The difference of the methods can be seen by clicking on the next map. For different age-classes (resp. different gender) the three methods can be compared directly within a bar chart. The technology for a rapid deployment of new data is now developed. For the use case first results have already been presented to decision makers, and feedback has been incorporated.

ConclusionBesides depicting disease prevalence, the atlas of epidemiology also allows to visualize health care service data and results of simulation models in a fast and efficient way, which is important for decision makers. Soon the results of the ATC-ICD project on the prevalence of different diseases based on ICD9 diagnoses and medication data will be published in an aggregated form. This project is part of the K-Project dexhelpp in COMET – Competence Centers for Excellent Technologies that is funded by BMVIT, BMWGJ and transacted by FFG.
Financial wellbeing of older workers following injury – research utilising Statistics New Zealand’s Integrated Data Infrastructure
Authors: Davie Gabrielle , Lilley Rebbecca
Published: April 2017

ABSTRACT

Objectives1. To undertake epidemiological analyses of New Zealand’s newly available linked longitudinal administrative data on financial wellbeing outcomes in older workers to understand the implications of injury in an ageing workforce 2. To gain an understanding of Statistics NZ’s Integrated Data Infrastructure (IDI) to inform the future use of the IDI specifically for health research

ApproachThe impact of injury on the income of older workers was quantified by comparing financial well-being outcomes for injured and non-injured older workers over a 3 year period using IDI linked records on: i) injury claims; ii) personal taxation records; and iii) benefit receipt.

Older workers were identified as all individuals aged 45 years and over in 2009 who had employment related tax payments in the previous year. Anyone with injury prior to 2009 for which earnings-related compensation was still being received in 2009 was excluded. Those that had an entitlement claim accepted by NZ’s Accident Compensation Corporation (ACC) for an injury that occurred in 2009 were defined as injured older workers, the remainder were considered non-injured. Date of injury was used as the reference date from which to calculate financial outcomes for injured older workers; non-injured workers were randomly assigned a date in 2009. Financial wellbeing, assessed by total earnings (income from wages and salary, employment related compensation from ACC and benefit receipt), was compared between injured and non-injured older workers at 1, 2 and 3 years following the reference date. Potential explanatory variables, such as gender, region of residency, and previous income/earnings received, was examined in multi-variable analyses.

ResultsFrom individually linked records in the IDI, considerable data management was required to identify those that met the criteria for injured and non-injured older workers. Preliminary analyses indicate total earnings at all 3 time periods differ between older workers that are injured and those that aren’t. Breakdown of total earnings by source of income sheds light on some of these differences. Observed differences are not consistent between each 5-year age group.

ConclusionsThe outcomes of injury are multi-faceted. The impact of injury on the financial well-being of older workers is concerning given NZ’s comprehensive universal injury compensation and welfare systems. The ability to use individually-linked longitudinal administrative records from Government agencies made this research possible, although using the IDI was not without its challenges.
Linking the Diagnostic Imaging Dataset (DID) to cancer registration data – improving understanding of diagnostic imaging in lung and ovarian cancer
Authors: Fry Anna , Careniro Isabella , Kennedy David , Bentley Abigail , Luck Gemma , Peake Mick , Ormiston-Smith Nick , Ohuma Ella
Published: April 2017

ABSTRACT

ObjectivesTo link the Diagnostic Imaging Dataset (DID) to cancer registration records to allow investigation of imaging performed in patients diagnosed with cancer and its relation to patient pathways and outcomes for lung and ovarian cancer patients diagnosed in England in 2013.

ApproachAll available DID data from April 2012 until July 2015 were joined with registry data for all patients diagnosed with lung cancer in 2013, extracted from Public Health England’s tumour-level cancer records. Records were joined on NHS number and date of birth for individuals aged 15-99, with a non-provisional tumour record and with only one lung cancer diagnosis.

One tumour can be linked to many imaging records. Because DID data are not limited to cancer-associated imaging, variables were created to flag imaging records that are likely to be related to the lung cancer diagnosis.

Lists of imaging procedure (SNOMED) codes considered to be related to the cancer diagnosis were created in consultation with clinicians. Imaging records that took place in the 3 months prior to diagnosis and were on the list of relevant procedure codes were flagged as relevant records.

The same method was replicated for ovarian cancer.

Results34,780 patients, each with only one lung tumour diagnosed in 2013, were joined with 502,600 DID records. The aforementioned flagging procedure resulted in 52,429 relevant DID records.

5,911 patients, each with only one ovarian tumour diagnosed in 2013, were joined with 74,425 DID records. The aforementioned flagging procedure resulted in 3,830 relevant DID records.

The resulting linkage has highlighted issues with potential missing imaging data and this is being explored and will be reported upon.

ConclusionThis is the first time linkage of DID and cancer registration data has taken place. The newly linked dataset will enable researchers to explore the imaging dataset further, with the potential to deepen understanding of issues such as imaging usage and intervals in imaging delivery.

Funding sourcesCancer Research UK.
Characteristics associated with residential aged care, respite and transitional aged care placement for older people following an injury-related hospitalisation in Australia
Authors: Mitchell Rebecca , Harvey Lara , Draper Brian , Brodaty Henry , Close Jacqui
Published: April 2017

Objective: This study examines characteristics associated with permanent residential aged care (RAC), respite RAC and transitional care (TC) placement for older individuals following an injury-related hospitalisation.

Method: A retrospective analysis of individuals aged ≥65 years who had an injury-related hospitalisation and who had a linked record in RAC, TC or activities of daily living (ADL) data between 1 July 2008 and 30 June 2013 in New South Wales, Australia. Comorbidities were identified using diagnosis classifications and a 1-year lookback period. All hospital episodes of care related to the injury were linked to form a period of care. Both new and existing admissions to RAC were examined. Multinominal logistic regression was used to examine the factors associated with new admissions to permanent RAC, respite RAC and TC compared to return to the community.

Results: Of 191,301 injury-related hospitalisations, 41,085 (21.5%) individuals either returned or were new admissions to permanent (87.2%) or respite (12.8%) RAC and 3,218 (1.7%) individuals were admitted to TC. There were 3,864, 4,314 and 2,630 new admissions to permanent RAC, respite RAC and TC, respectively. Of the injury hospitalisations, 70,796 (37.0%) individuals had an ADL assessment. Compared to individuals who returned to the community, individuals newly admitted to permanent RAC were four times as likely to have dementia (OR: 4.36; 95%CI 4.15-4.57), those admitted to respite RAC were twice as likely to have dementia (OR: 2.37; 95%CI 2.21-2.54) and people admitted to TC people were less likely to have dementia (OR: 0.60; 95%CI 0.53-0.68). Individuals with shoulder and upper arm injuries were twice as likely (OR: 2.31; 95%CI 1.98-2.68) and individuals with knee and lower leg injuries were one and a half times as likely (OR: 1.87; 95%CI 1.60-2.18) to be admitted to TC. Overall, individuals who were admitted to permanent or respite RAC had a higher likelihood of experiencing limitations associated with their physical, cognitive or social abilities, with individuals admitted to TC having a higher likelihood of having limitations maintaining personal hygiene and mobility compared to individuals returning to the community.

Conclusion: An understanding of the profile of which older individuals are using RAC (permanent or respite) or TC services can usefully inform current and future aged care service use.
Measuring cumulative anticholinergic medicines burden in older Australian women
Authors: Parkinson Lynne , Magin Parker , Lo Tkt , Byles Julie , Moorin Rachael
Published: April 2017

ABSTRACT

ObjectivesAnticholinergic medicines burden is common, can have negative impacts, and is problematic to identify. Many medicines used by older women have anticholinergic effects. Importantly for older women, where multimorbidity and use of multiple medicines is common, even when anticholinergic effect of an individual medicine is small, the anticholinergic effects of multiple medicines may be additive, constituting cumulative anticholinergic burden. This study describes medicines contributing to and predictors of anticholinergic burden among community-dwelling older Australian women.

ApproachRetrospective longitudinal analysis of data from the Australian Longitudinal Study on Women’s Health linked to Pharmaceutical Benefits Scheme medicines data from 1 January 2008 to 30 December 2010; for 3694 women born in 1921–1926.Anticholinergic medicines were assigned anticholinergic potency levels 0 to 3, according to the Anticholinergic Drug Scale. Anticholinergic Drug Scale ratings for all medicines used by each woman were summed across each six months to give an Anticholinergic Drug Scale score. Commonly used medicines were identified for women with high ADS scores (defined as 75th percentile of scores). Predictors of high ADS scores were analysed using generalised estimating equations. 

ResultsDuring 2008-2010, 1126 (59.9%) of women used at least one anticholinergic medicine. Median Anticholinergic Drug Scale score was 4. Most anticholinergic medicines used by women who had a high anticholinergic burden (Anticholinergic Drug Scale score > 9) had a low anticholinergic potency (Anticholinergic Drug Scale level 1). Increasing age, cardiovascular disease, and number of other medicines used were predictive of a higher anticholinergic burden.

ConclusionHigh anticholinergic medicines burden in this group was driven by use of multiple lower anticholinergic potency medicines rather than use of higher potency medicines. While we might expect that doctors would readily identify anticholinergic burden risk for those using high potency medicines, they may be less likely to identify this risk for users of multiple low potency anticholinergic medicines. The paper will also discuss how GPs view these findings, and how to translate them into the prescribing setting.
Design and evaluation of probabilistic record linkage methods supporting the Brazilian 100-million cohort initiative.
Authors: Pita Robespierre , Pinto Clicia , Barreto Marcos , Sena Samila , Fiaccone Rosemeire , Amorim Leila , Barreto Mauricio
Published: April 2017

ABSTRACTBackground and aimsA cooperation Brazil-UK was set in mid-2013 aiming at to build a huge cohort comprised by individuals registered in CadastroÚnico (CADU), a socioeconomic database used in social programmes of the Brazilian government. Epidemiologists and statisticians wish to assess the impact of Bolsa Família (PBF), a conditional cash transfer programme, on the incidence of several diseases (tuberculosis, leprosy, HIV etc). The cohort must contain all individuals who received at least one payment from PBF between 2007 and 2012, which results in a 100-million records according to our preliminary analysis. These individuals must be probabilistically linked with databases from the Unified Health System (SUS), such as hospitalization (SIH), notifiable diseases (SINAN), mortality (SIM), live births (SINASC), to produce data marts (domain-specific data) to the proposed studies. Within this cooperation, our first goal was to design and evaluate probabilistic methods to routine link the cohort, PBF, and SUS outcomes.

ApproachWe implemented two probabilistic linkage methods: a full probabilistic, based on the Dice similarity (Sorensen index) of Bloom filters; and an hybrid approach, based on rules to deterministic and probabilistic matching. We performed linkages involving CADU (2011 extraction) and SUS outcomes (SIH, SINAN, and SIM) with samples from 3 states (Sergipe, Santa Catarina and Bahia) with an increasing size (from 1,447,512 to 12,036,010).

ResultsUsing a Dice between 0.90 and 0.92, our methods retrieved more than 95% of true positive pairs amongst the linked pairs. For Sergipe, we obtained as : <23,22>, <315,300>, <32,32>, respectively for SIH, SINAN, and SIM. For Bahia: <771,593>, <2677,2626>, <208,207>. Another linkage between CADU (1,447,512 records) and SINAN (624 records), for tuberculosis in Sergipe, returned 397 (full probabilistic) and 311 (hybrid) linked pairs, being 306 and 300 true positives. Another execution considering CADU (1,988,599 records) and SINAN (2,094 records), for tuberculosis in Santa Catarina, returned 791 (full probabilistic) and 500 (hybrid) linked pairs, with 667 and 472 true positives. Linking CADU (1.685,697 records) and SIM, for mortality of children under-4, returned 18 linked pairs, all of them true positives, for a Dice between 0.90 and 0.92 and with 100% of sensitivity, specificity, and positive predictive value.

ConclusionDue to the absence of gold standards, we use samples with increasing sizes and manual review when adequate. Our results are quite accurate, although obtained with an unique extraction of CADU. We are starting to run linkages with the entire cohort.
PEAC Events: identification of Predictable, Expensive, Avoidable, and Cardinal events within a learning health system.
Authors: Whyatt David , Yap Matthew , Tuson Matthew , Kok Mei Ruu , Turlach Berwin , Boruff Bryan , Geelhoed Elizabeth , Vickery Alistair
Published: April 2017

ABSTRACT

ObjectivesThe objectives of this project are to identify patients that can be recruited into specific interventions and the optimisation of the delivery of such interventions, in order to improve access to health services, equity of service delivery, and patient outcomes.ApproachThe entire linked Western Australian Data Collections from 2002-2015 (including population-wide hospital admissions, emergency department presentations, cancer registry records, mental health care, maternity records, and mortality records) were examined. To identify patients at risk, a definition of a ‘PEAC’ event was developed. This acronym reflects the following criteria for such events. First, the event should ‘predictable’, i.e. either able to be easily predicted, or able to predict subsequent poor patient outcomes (for example, death). Second, the event should be ‘expensive’, i.e. be associated with significantly increased levels of individual healthcare utilisation and/or mortality. Third, the impact of the event should be ‘avoidable’, i.e. an evidence-based intervention should exist that may delay or completely avoid the event, or reduce its sequelae. Fourth, the event should be ‘cardinal’, which in this context indicates that the event should be clearly and unambiguously defined and recognisable, and specific enough to assign an effective intervention. Once PEAC events were identified, geospatial and predictive modelling of future events were then used to inform clinical service delivery, alongside appropriate return-on-investment analysis to support intervention. Finally, the entire process was embedded within a learning health system, linking research, policy, and practice, to drive ongoing improvement.ResultsExemplar PEAC events will be described, including hospital admission events associated with chronic disease, mental health, and dental/oral health. The predictability of such events in individuals using statistical models fitted to the available administrative datasets will be presented, along with the sequelae of such events in terms of healthcare use and mortality. The optimisation of delivering interventions targeting PEAC events will be described, along with the process of translating findings into policy and practice within the context of a learning health system.ConclusionThe identification of PEAC events allows for targeted delivery of healthcare interventions in a manner that not only optimises access, equity, and outcomes, but also permits ongoing improvement of the health system.
Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System
Authors: Nangle Clifford , McTaggart Stuart , MacLeod Margaret , Caldwell Jackie , Bennie Marion
Published: April 2017

ABSTRACT

ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume.

The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness.

ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage.

ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively.

ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.
Social outcomes associated with alcohol-related diagnoses: a population-based analysis using linked administrative data
Authors: Nickel Nathan , MacWilliam Leonard , Eukema Oke , Prior Heather , Nepon Joshua
Published: April 2017

ABSTRACT

ObjectiveThe objective of this population health research is to identify the social burden associated with having an alcohol related diagnosis.

MethodsWe used linkable population-based administrative data files held in the Population Health Research Data Repository to conduct our research, fiscal years 1990/91 to 2014/15. Data came from several domains including health, social services, justice, and the Canadian Census. We used ICD-9/10-CA codes from the hospital abstract database, medical claims data, and prescription drug data to identify individuals with an alcohol-related diagnosis. Individuals’ socioeconomic status was determined using neighbourhood-level income data from the Canadian Census. We matched, 3:1, diagnosed cases to individuals in our repository using on age, sex, income, and community area. We linked cases and matches to administrative data from justice and social services to identify social outcomes associated with having an alcohol-related diagnosis. Outcomes included receipt of income assistance, residence in publically funded social housing, having a child apprehended by child and family protective services, having a charge for driving under the influence recorded in the justice data, and having a charge for domestic violence recorded in the justice data. We modelled rates using generalized estimating equations from 5 years before date of diagnosis to a maximum of 20 years after date of diagnosis. Models tested for significant differences in rates between cases and matches both before and after diagnosis; as well, we tested for time trends in rates both before and after diagnosis.

ResultsWe identified 52,991 individuals with an alcohol related diagnosis between 1990/91 and 2014/15: 34,145 males and 18,846 females. 80.3% of cases had a mental-health related alcohol diagnosis. Diagnoses followed a socioeconomic gradient with the greatest number of cases coming from low-income neighbourhoods. Cases had a significant spike in rates from one year before to one year after diagnosis date, compared with matches, across all indicators. When we followed individuals for 20 years after diagnosis, we found a significantly elevated rate of social service use and involvement with the justice system, across all outcomes, for all years.

ConclusionReceiving an alcohol-related diagnosis is associated with subsequent increased use of social services and contacts with the justice system. Upstream efforts to reduce alcohol-related diagnoses may result in reduced use of social services and justice contacts.
Secure Privacy Preserving Record Linkage of Large Databases by Modified Bloom Filter Encodings
Authors: Schnell Rainer , Borgs Christian
Published: April 2017

ABSTRACTObjectiveIn most European settings, record linkage across different institutions has to be based on personal identifiers such as names, birthday or place of birth. To protect the privacy of research subjects, the identifiers have to be encrypted. In practice, these identifiers show error rates up to 20% per identifier, therefore linking on encrypted identifiers usually implies the loss of large subsets of the databases. In many applications, this loss of cases is related to variables of interest for the subject matter of the study. Therefore, this kind of record-linkage will generate biased estimates.

These problems gave rise to techniques of Privacy Preserving Record Linkage (PPRL). Many different PPRL techniques have been suggested within the last 10 years, very few of them are suitable for practical applications with large database containing millions of records as they are typical for administrative or medical databases. One proven technique for PPRL for large scale applications is PPRL based on Bloom filters.MethodUsing appropriate parameter settings, Bloom filter approaches show linkage results comparable to linkage based on unencrypted identifiers. Furthermore, this approach has been used in real-world settings with data sets containing up to 100 Million records. By the application of suitable blocking strategies, linking can be done in reasonable time.ResultHowever, Bloom filters have been subject of cryptographic attacks. Previous research has shown that the straight application of Bloom filters has a nonzero re-identification risk. We will present new results on recently developed techniques to defy all known attacks on PPRL Bloom filters. These computationally simple algorithms modify the identifiers by different cryptographic diffusion techniques. The presentation will demonstrate these new algorithms and show their performance concerning precision, recall and re-identification risk on large databases.
A process-driven platform to manage datasets for research
Authors: McAllister Gordon
Published: April 2017

ABSTRACT

Objectives

Accumulate, manage and control shared access to research data; 

Transform and maintain transformation state information about research data; 

Analyse and investigate data in related sets using open and bespoke tools; 

Publish extracted data to a secure safe haven environment.

ApproachThe Research Data Management Platform (RDMP) is a set of data structures and processes, sharing a core Catalogue, to manage electronic health records, genomic data and imaging data throughout their lifecycle from identification and acquisition to safe disposal or archival and retention in secured Safe Havens (SH).

The architecture components of the RDMP consist of the Catalogue and five internal processes: Data Load, Catalogue Management, Data Quality, Data Summary, and Data Extraction. These are designed to enforce rigorous information governance standards relevant to the processing and anonymisation of personal identifiable data. The Catalogue serves as the single ‘source of truth’ about the datasets which all RDMP processes consult. This facilitates repeatable, reliable and auditable operations on the data.

The novelty of the RDMP is that it dynamically and seamlessly captures and preserves data transformation processes along with the primary research data to promote reuse and curation of continuously accruing research data repositories in a secure SH environment. Thus, the RDMP brings transparency and reproducibility that benefits research programmes in a way that archival of static data objects does not.

ResultsThe RDMP has been in production use since July 1st 2014. There are 107 datasets configured in the Catalogue, with up to 67 dataset extractions for each of 48 research projects. It has provided data for 32 high-impact journal papers published in the last year.

Improvements in turnaround time:

Research project data provision reduced from six months to two weeks;

Data loading reduced from two days to a few hours; 

Research query response reduced from days to within a day, due to improved and standardised metadata catalogue

ConclusionThe RDMP is a key component in automating the regular release of datasets and rationalising dataset changes over time to ensure reliable delivery of extracts to research projects. The tools and processes comprising the RDMP not only fulfil the RDM requirements of researchers, but also support seamless collaboration of data cleaning, data transformation, data summarisation and data quality assessment activities by different research groups.
Tomorrow’s Health Data Scientists in the Making: 2 Years of Training & Capacity Building Activities with the Farr Institute
Authors: Anastasiou Athanasios , Moulton Georgina , McCowan Colin , Taylor Paul , Goddard Catharine
Published: April 2017

ABSTRACT

ObjectivesThe poster will showcase the Training & Capacity Building Programme (TCBP) established by the Farr network that is available to PhD students and Early Career Researchers (ECRs) both from within and outside the network.

ApproachThe authors are using a mixed-methods approach to present the current state of the art that has motivated the structure of the TCBP and how the programme has been received by students, academic and industry leaders.

Since 2014, the Farr network education leads have worked on a unique education programme that aims to equip students with key professional and methodological skills required by the roles they are likely to take in their future careers.

The content of the programme has been informed by current research in the fields of data linkage and analysis of big datasets as well as the immediate experience of researchers, and healthcare and industry leads working actively in the field.

We have focussed on developing a community of practice through the provision of an environment that has enabled students to share experiences and knowledge and to start building their own networks of collaboration across the Farr centres.

ResultsThe Farr network of PhD students comprises of approximately 66 students from a variety of backgrounds including bioinformatics, computer science, epidemiology, mathematics, psychology and statistics.

The Farr network hosted 2 main events in 2015 that provided the foundation for 34 PhD students to come together to develop skills in written and oral communication, Public and Patient Involvement (PPI), problem-solving, requirements gathering, data visualisation and spatial data analysis.

Formal feedback collected from each event suggests that the programme is received well by students who see it as a set of activities that complement their specialised PhD related education. Feedback from supervisors and future employers also suggests that this programme has facilitated the development of skills that are useful for their PhD and employment.

ConclusionThe Farr network has laid the foundation for the community of practice around the analysis of real-world health datasets across the UK, and will continue to cultivate this through the enrichment of the programme.
Patient Engagement as a Component of a Learning Healthcare System: A case study using small area rate variation research in Nova Scotia, Canada
Authors: Levy Adrian , Kephart George , Dowling Laura , Dyer Jonathan , Burge Frederick , Atherton Frank , MacKenzie Adrian , Anne Leslie , Krause Juergen , McDonald Ted
Published: April 2017

ABSTRACTObjectivesThe objective was to develop a framework for incorporating patient engagement into administrative health database research. In an examination of variation in high-cost health service use using administrative data, patient experience was incorporated as an additional source of knowledge to inform evidence-informed policy making in a learning healthcare system framework.MethodThe study described variation in the rate of high-cost use by area within Nova Scotia, Canada, and isolated local factors contributing to the rate of high-cost use to inform targeted intervention development. Regression analysis was used to determine where the rate of high-cost use was driven by known contributors, such as demographics or disease patterns. Peer-leaders from provincial chronic disease management programs (Patient Navigators) were recruited as study team members. They were invited to help describe their collective experience of patient-based factors that may contribute to high-cost use, including access to care and multi-morbidity in their regions.ResultsThe outcome of this ‘proxy’ patient engagement was measured by the extent to which the input from the Patient Navigators influenced study protocol, interpretation of results and communication of findings. The patient voice helped describe the extent of variation, contextualize the findings, and suggested additional contributory factors not revealed by the analysis of administrative health data. For example, the Patient Navigators described regional discrepancies in available services for managing chronic disease and variation in the approach to discharge planning. In this way, the patient experience was incorporated to attempt to explain rates of high-cost use in areas that could not be explained by known contributors. Further, patient experience with travel distance to receive care and alternate levels of care helped to generate questions for future research.ConclusionPatient experience is an invaluable input into health research that contributes to health system planning. This research incorporated ‘proxy’ patient experience to produce evidence to inform targeted interventions aimed at reducing the rate of high cost-users. The identified areas where focused interventions or reforms could yield material benefits for efficient delivery of health care.
Validating Earnings in the German National Educational Panel Study – Do Interviewers Have an Impact on Measurement Accuracy?
Authors: Antoni Manfred , Vicari Basha , Bela Daniel
Published: April 2017

ABSTRACTObjectivesWe investigate characteristics of respondents and interviewers influencing the accurateness of reported income by comparing survey data with administrative data. Questions on sensitive topics like respondents' income often produce relatively high rates of item nonresponse or measurement error. In this context several analyses have been done on item nonresponse, but little is known about accuracy of reporting. Existing evidence shows that it is unpleasant for respondents to report very low or very high income. In presence of an interviewer income questions might produce incorrect responses due to social desirability bias. On the other hand side interviewers can create a trustful atmosphere in which respondents give more accurate answers.

ApproachUsing linked survey and administrative data we are able to measure the extent of deviation between reported and recorded incomes and explore the influence of respondent and interviewer characteristics on it. The starting point for the linkage is data from the German National Educational Panel Study (NEPS), Starting Cohort 6, which surveys adults from birth cohorts 1944 to 1986. More than 90% of the respondents consented to a linkage of their survey information with administrative data from the German Federal Employment Agency. These longitudinal earnings data are highly reliable as they are based on mandatory notifications of employers to the social security system.We include interviewer and respondent characteristics as well as their interactions into our model to estimate their respective impact on the incidence and size of any bias in reported incomes. This allows us to control for latent interviewer traits that might have influenced the respondent's answering behavior during each interview of a given interviewer.

ResultsThe average deviation of reported from administrative earnings is relatively small (less than 10% of median earnings). Descriptive evidence shows only small variation of deviation across subgroups. Most importantly, female respondents show higher report accuracy. Multivariate results hint at a negligible influence of interviewer characteristics. The major predictors for deviation in respondents' characteristics are their sex, their absolute monthly personal income, their educational level and being born abroad.

ConclusionAlthough the average measurement accuracy is rather high, there are some differences in deviations by subgroups. The impact of these deviations depends on the research question at hand. Research with a strong focus on the respondent’s earnings, e.g. when using them as a dependent variable, should use the linked data rather than only the NEPS survey data.
Probabilistic Linkage of Two National Databases to Study Emergency Care and Hospital Readmission in Children who Survive Traumatic Injury
Authors: Bennett Tellen D. , Dewitt Peter E. , Juarez-Colunga Elizabeth , Cook Lawrence J.
Published: April 2017

ABSTRACT

ObjectivesTrauma and, in particular, traumatic brain injury (TBI) cause enormous morbidity and mortality worldwide. Children who survive trauma have many health burdens, but how often those burdens lead to emergency care or hospital readmission is unknown. Readmissions occur frequently in adult TBI survivors: approximately 21-28% within one year. No single existing U.S. database contains the necessary variables to study readmissions of children hospitalized after trauma. We previously demonstrated that probabilistic linkage with Markov chain Monte Carlo parameter refinement could accurately link records of children with severe TBI without using protected health information. That work was presented at the IHDLN Conference in 2014. The objectives of this study are to 1) expand the previous linkage to include children with any type of trauma and 2) to determine the 1-year rates of Emergency Department (ED) visit and hospital readmission in children with TBI versus other trauma.

ApproachUsing the algorithm we reported previously, we linked the records of children hospitalized after trauma during 2007-2012 in the U.S. National Trauma Data Bank (NTDB) with the Pediatric Health Information System (PHIS) database. 

The NTDB contains the Glasgow Coma Scale (GCS), necessary to categorize TBI. The PHIS database contains a unique identifier for each patient at each hospital, making longitudinal studies possible. Probabilistic linkage was performed using LinkSolv (Strategic Matching, Inc., Morrisonville, NY). We identified ED visits and hospital readmissions of the index cohort in the PHIS database through March 31, 2015.

ResultsAmong children hospitalized after acute trauma for >= 2 days, we found that 83% of NTDB records linked accurately. Of the children hospitalized at 28 U.S. hospitals in 19 states, 49,477 were discharged alive; 15,438 had TBI. This represents approximately 10% of U.S. pediatric TBI hospitalizations during that period. The median age was 8 years and 64% were male. The 1-year hospital readmission rate was higher (12.2%, 283/2,306) in those with both TBI and other injuries than

those with isolated TBI (8.5%, 1,120/13,132) or non-TBI injuries (8.1%, 2,790/34,039, p < 0.01 across groups). The 1-year ED visit rate differed only modestly between those three groups (14.1% versus 15.4% versus 14.2%, p < 0.01).

ConclusionsThis study leverages two linked national datasets to investigate emergency care and hospital readmission in children who survive trauma. Multivariable analyses are ongoing and will be completed by August 2016. We will generate hypotheses about potentially modifiable causes of emergency care and readmission.
Probabilistic Record Linkage (PRL) advances between Surveillance Data and HES datasets using the Expectation-Maximization (EM) algorithm
Authors: Panagiotopoulos Nikolaos , Minaji Mehdi , Pebody Richard
Published: April 2017

ABSTRACT

ObjectivesThis study shows how to improve PRL when using a limited number of personal identifiable information (PIIs) available in HES such as NHS number, DOB, Hospital Number, Gender and Postcode. The focus is on new approaches in categorisation of m-u probabilities for fields related to Postcode. In addition, a brief outline of our procedure to generate an appropriate training dataset will be presented as well as reasons for splitting HES dataset into single and multi-admissions, and proposed techniques to avoid computational cost.

ApproachPIIs such as DOB and Postcode can be categorised in order to increase the number of potential comparison vectors which is necessary in order to deal with the multi-million size of the HES dataset. We propose a multinomial approach for comparison between postcodes both for agreement and disagreement case scenarios, based on geographical information and a similarity score resulting from a Probit model. In the absence of a ‘gold-standard’ dataset, generating an appropriate training set is possible by combining two-way Deterministic Linkage (DL) and simple random sampling. Splitting HES into single and multi-admissions is recommended for reasons related to computational performance and demographic differences of the underlying population. Selection of the lower and upper thresholds regarding linkage weights can be done in a practical way by using conditional Normal mixture modelling, or more formally by using sampling techniques based on Fellegi-Sunter decision rule. We show how the latter can be further improved avoiding the computational cost by introducing appropriate mapping functions.

ResultsWe show the advantages of PRL over DL between Surveillance Laboratory Flu Data and HES. Preliminary results indicate an additional 10% of matches for < 1% expected false positives. EM convergence problems when treating missing values as separate outcome category will be discussed comparing results with the suggested re-normalisation approach in the bibliography.

ConclusionPRL between Laboratory Surveillance Data and Hospital Episode Statistics (HES) is a common practice and performed in a number of epidemiological studies conducted at Public Health England (PHE). Therefore, improving linkage will subsequently benefit those studies. Furthermore, this study proposes new approaches for Postcode comparison, and the utilisation of mapping functions to avoid the computational cost of the Fellegi-Sunter decision rule. These approaches could be used in a wide range of applications. Finally, we suggest a structural way of handling the HES dataset in order to achieve high efficiency and robustness of linkage.