bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying superusers of health services with mental health and addiction problems: Putting the Canadian Institutes of Health Research Strategy for Patient Oriented Research into action
Authors: Quail Jacqueline , Schaefer Joelle , Neudorf Cory , Anderson Maureen , McLeod Valerie , Lafontaine Tania , Pelly Judy , Baetz Marilyn , Baker Margaret , Teare Gary , Muhajarine Nazeem
Published: April 2017

ABSTRACT

ObjectiveWe were funded by Canadian Institutes of Health Research Strategy for Patient Oriented Research (CIHR-SPOR) to use administrative data to identify people with mental health and/or addiction (MHA) problems, and determine characteristics that lead to them becoming a superuser of health services. The aim of the CIHR-SPOR is to ensure that research improves healthcare systems and practices. Their approach is to fundamentally change the ivory tower nature of research to make it more inclusive, user friendly, and timely. An essential component of the CIHR-SPOR is engaging patients as partners at all stages of the research. In this presentation we will describe the challenges, successes, and failures of engaging patients as part of the research team.

ApproachIn Saskatchewan, Canada, we engaged with community programs to determine the ‘on-the-ground’ reality of people living with MHA problems. Our discussions revealed that First Nations and Métis People are highly overrepresented in the MHA patient population. For example, they comprise 15% of the Saskatchewan population yet 70% of patients receiving treatment at a local methadone clinic. For this reason, we are focusing our efforts on engaging with this specific patient population.

ResultsWe encountered a number of barriers to finding patient advisors. First, patient advisors available through institutional programs are not representative of our target population, being more affluent, well-educated, and Caucasian. There are currently no formal avenues to identify and invite members of the target population to join the research team and so we are using personal connections and team building to identify patients advisors. Second, complicated institutional policies on reimbursement for patient advisors impede participation by low income individuals. Third, we are concerned the socioeconomic disparities common in this vulnerable population may impede their full and honest participation as a patient advisor.

ConclusionsPatients and their family members are invaluable members of the research team. However, a power differential often exists between team members and patients due to socioeconomic differences, including race and education. Stigmatized health conditions such as MHA problems can exacerbate the feelings of a power differential. All of these things can prevent genuine involvement by patients. The most important step in successfully involving these untraditional team members is building trust through unconditional acceptance and respect for each individual’s lived experience. Building trust takes time and so patient engagement cannot be expected to follow a rigid schedule.
Constructing episodes of inpatient care: How to define hospital transfer in hospital administrative health data?
Authors: Peng Mingkai , Li Bing , Southern Danielle , Quan Hude
Published: April 2017

ABSTRACT

ObjectivesHospital administrative data creates a separate record for each hospital stay of patients. Treating a hospital transfer as a readmission could lead to biased results in health service research, resource planning, and quality of patient care. This study is to identify the optimal time gaps between two hospitalizations to identify hospital transfer cases.

ApproachThis is a cross-sectional study. We used the hospital discharge abstract database (DAD) in 2013 from Alberta, Canada to define transfer cases. Institution code and transfer indicators of “institution to” and “institution from” are mandatory in Canadian DAD and have high reliability. We defined transfer cases by transfer institution indicators and used it as the reference standard. Different time gaps between two hospitalizations (6, 9, 12 and 24 hours) were used to identify transfer cases. We compared the sensitivity and positive predictive value (PPV) of different transfer case definitions across different categories of sex, age, and location of residences. Readmission rate within 30 days was also compared after the episode of care were defined by combining transfer cases at the different time gaps.

ResultsSensitivity increased with an increase of time gap between two hospitalizations while PPV decreased. Use of ≤ 6 hours lead to low sensitivity for patients under the age of 50 or living in the rural area; Use of ≤ 24 hours lead to low PPV for patients under the age of 50 or living in urban area. Use of ≤ 12 hours overestimated the 30 days readmission rate compared with the reference standard. The time gap of 9 hours between two hospitalizations is the optimal way to identify transfer cases with the sensitivity of 0.97 and the PPV of 0.95.

ConclusionsWe recommend the use of a time gap of up to 9 hours between two hospitalizations to define hospital transfer in inpatient databases. This validated definition provides a foundation for research in health service and for outcomes such as readmission.
Record linkage augments cancer ascertainment in HIV cohorts in South Africa
Authors: Sengayi Mazvita , Spörri Adrian , Rohner Eliane , Vinikoor Michael , Prozesky Hans , Boulle Andrew , Egger Matthias , Bohlius Julia
Published: April 2017

ABSTRACT

BackgroundSub-Saharan Africa is the region most heavily affected by the HIV/AIDS epidemic. HIV increases the risk of developing cancer but the ascertainment of cancers in patients attending antiretroviral therapy (ART) treatment programs might be incomplete. To estimate the under-ascertainment of cancer we compared incidence rates of AIDS-defining cancers in South African HIV cohorts with and without cancer case ascertainment through record linkage with the National Cancer Registry.

MethodsWe used the data of adult (≥16 years) HIV-positive persons receiving care between 2004 and 2011 at one of four ART programs in South Africa. These programs collaborate with the International Epidemiologic Databases to Evaluate AIDS Southern Africa (www.iedea-sa.org) and collected data for AIDS-defining cancers but not for other cancers. To improve cancer ascertainment we probabilistically linked patient records (using first name, surname, age, and gender) from two HIV cohorts with the cancer records of the South African National Cancer Registry. We calculated incidence rates per 100,000 person-years after starting ART for the AIDS-defining cancers, i.e. Kaposi sarcoma (KS), invasive cervical cancer (ICC) and non-Hodgkin lymphoma (NHL). We compared incidence rates before and after inclusion of record linkage identified cancer cases using the attributable fraction of cancers identified with 95% confidence intervals (CI).

ResultsA total of 49,207 adults starting ART in South Africa were included. 65% of patients were female, median age at starting ART was 35 years (interquartile range 30-41 years). We identified a total of 471 incident cancer cases. With record linkage the incidence increased from 81 to 292 for KS, from 1 to 119 for NHL and 12 to 497 for ICC per 100,000 person-years. The attributable fraction of cancers identified was 72% (95% CI 63-79%) for KS, 98% (95% CI 94-99%) for NHL and 98% (95% CI 95-99%) for ICC.

ConclusionAscertainment of cancer in HIV program data in African settings is incomplete. This case study has shown that probabilistic record linkage to cancer registries is both feasible and essential for cancer ascertainment in HIV cohorts in South Africa.
A Validation of the Health and Social Costs of Asthma Using Questionnaire Data
Authors: Brookman Alison , Davies Gwyneth
Published: April 2017

ABSTRACT

ObjectivesCost-burden analyses usually lack a wider societal perspective. Through the creation of a robust resource use measure (RUM), data gaps on societal costs will be addressed, allowing more accurate estimates of the cost burden of asthma in the UK.

ApproachA systematic review, applying an established checklist and data extraction tool, will identify and evaluate those existing methods and measures of generating resource use data. These data, incorporated with focus group data, will inform the design of a new RUM. To be distributed to 2000 participants, randomly selected from GP practices (Swansea & Edinburgh), the Swansea data will be linked and validated with their record held in SAIL (Secure Anonymised Information Linkage). A nested cost burden analysis will be applied, generating an estimate of relevant societal costs.

Preliminary resultsA preliminary search of the ‘PubMED’ database generated 181 papers that met the inclusion criteria. Initial analysis shows that a societal perspective is usually lacking from RUMs. A critical appraisal of these measures has identified significant variance in both the quality and content of the questionnaires. Focus groups highlighted a number of cost elements that are not, as yet, discussed in the literature.

ConclusionSocietal cost factors are not adequately captured within existing tools, our findings will inform the development of a new RUM which will be piloted and validated according to best practice guidelines. Capturing the societal costs of asthma will allow more accurate estimates of the total costs of asthma in the UK.
Incremental Interactive Record Linkage using Human Intelligence Tasks (HITs)
Authors: Kum Hye-Chung
Published: April 2017

ABSTRACTObjectiveWhen analyzing population data, there is a need to link data about organizations. One challenge in linking organization level data is that unlike a person, there can be many definitions for an entity. For example, for hospitals, depending on the dataset, an entity might represent any one of the following similar but different semantic types:(1) physical units, (2) billing units (3) legal units, (5) licensed units, or (5) reporting units. How these different entities relate to each other can be complex such as one billing unit can span across many physical units or multiple billing units can exist for one physical unit. Thus, linking organization level data requires human involvement to sort through these issues in heterogeneous data sources to make informed decisions on the messy data. We design and evaluate a general framework for a hybrid Human-Machine process for ongoing integration and cleaning of hospital level data when no common identifiers exist such that we highlight the decisions that need human judgement and document and track the full processes to ensure reproducibility. Such ongoing integration is often called incremental record linkage (RL).

ApproachAccurate linkage in big data requires well-defined tasks that need automatic or human processing. In the human computer interaction (HCI) field, Human Intelligence Tasks (HITs) are defined as micro tasks requiring human judgment and are often used in designing crowdsourcing systems. We designed HITs for linking organization level data and embed them into automatic deterministic linkage algorithms that supports interactive stepwise RL. The hybrid system is a framework for reproducible incremental RL.

ResultsWe illustrate this framework by integrating four databases of hospitals in Texas from 2008 to 2014(N=664). The IDs used in the databases are the Texas Provider ID, the National Provider ID, the Medicare ID, and the Facility ID. We link the databases using provider name, including dba (i.e., doing business as), addresses, and phone numbers. Similarities in hospital names and addresses and the dynamic nature of hospital attributes over time make it impossible to build a fully automated linkage system for hospitals. Using our system to iteratively standardize and clean the data, we linked the hospitals with 100% precision using HITs that required confirming 79 approximate linkages and manually linking 28 hospitals.

ConclusionEffective software that can support the interactive and iterative process of RL with well-designed HITs can streamline the linkage processes to support high quality replicable research using big data.
Chronic Disease Case Definitions for Electronic Medical Records: A Canadian Validation Study
Authors: Lix Lisa , Singer Alexander , Katz Alan , Yogendran Marina , Al-Azazi Saeed
Published: April 2017

ABSTRACTObjectivesCanadians are investing heavily in electronic medical records (EMRs) to inform primary care practice improvements. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a national practice-based network that has enrolled more than one million patients to date. Accurate CPCSSN EMR data are essential for unbiased research about chronic disease prevention and management. The study purpose was to test the accuracy of chronic disease case definitions in EMR data from one CPCSSN site.

ApproachThis study linked CPCSSN EMR data, hospital records, physician billing claims, prescription drug records, and population registration files for the province of Manitoba. Individuals who had at least one encounter with a CPCSSN practice between 1998 and 2012, were at least 18 years of age, and had a minimum of two years of healthcare coverage before and after the study index date were included. Separate cohorts were defined for the following chronic diseases: chronic obstructive pulmonary disease (COPD), depression, diabetes, hypertension, and osteoarthritis. Validated case definitions based on diagnoses in physician and hospital records and prescription drug data were used estimate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and kappa of each EMR chronic disease case definition.

ResultsMore than 74,000 individuals were included in each cohort, except for COPD which had 51,000. Approximately half of each cohort was comprised of urban residents. The average age ranged from 45.9 years for individuals with depression to 65.3 years for individuals with COPD. Hypertension had the highest prevalence (22.0%) in EMR data followed by depression (14.6%). Estimates of agreement (i.e., kappa) for EMR and administrative data ranged from 0.47 for COPD to 0.58 for diabetes. Sensitivity of the EMR data was lowest for COPD (37.4%; 95% CI 36.0-38.8) and highest for diabetes (57.6%; 95% confidence interval [CI] 56.6-58.6). PPV estimates were lowest for osteoarthritis (66.9%; 95% CI 66.0-67.8) and highest for hypertension (78.3%; 95% CI 77.7-78.9). Specificity estimates were consistently above 90% and NPV estimates were always greater than 80%. Validity estimates for the EMR case definitions were associated with demographic and comorbidity characteristics of the study cohorts.

ConclusionsValidity of EMR data, when compared to administrative health data, for ascertaining five different chronic diseases was fair to good; it varied with the disease under investigation. Further research is needed to identify methods for improving the accuracy of chronic disease case definitions in EMR data.
Linking AWPS and CARIS data to estimate perinatal mortality in Wales due to congenital anomalies
Authors: Rolfe Kim , Chakraborty Mallinath , Tucker David
Published: April 2017

ABSTRACTObjectivesThe All Wales Perinatal Survey (AWPS) collects mortality data on perinatal, neonatal and infant mortality in Wales up to a year of age. The Congenital Anomaly Register and Information Service (CARIS) collects clinical data on all congenital anomalies in foeti, infants and children in Wales. This was the first attempt to link the AWPS and CARIS databases.ApproachThe AWPS database coded stillbirths, terminations and deaths by the clinic-pathologic classification until 2012, assigning each case a survey number. The CARIS database started collecting data from 1998. Thus, the common period between 1998 and 2012 (15 years) was chosen for this study. Using CARIS as the main dataset, AWPS data were matched against the CARIS database using NHS numbers, along with soft linking on date of birth, maternal date of birth, and postcode. The unique AWPS survey number and coding was then added to the matched record within the CARIS dataset. This final ‘linked’ dataset was used to look at trend data.

ResultsBetween 1998 and 2012 a total of 20,574 cases were recorded in the CARIS database. After excluding cases where no gestation or date of death were recorded, and including deaths between >19 weeks’ gestation up to 365 days after birth, there were 1306 foetal losses/terminations, 231 stillbirths, and 662 livebirths (total 2,199) records left in the CARIS database who died with congenital anomalies. In the comparable period, using the same criteria, there were 6,909 records identified in the AWPS database. Of those, 1,765 had a recorded lethal congenital anomaly. By using the above criteria, a total of 2,039 cases in the CARIS database were linked to the AWPS database. 241 cases in the CARIS database and 151 cases in the AWPS database could not be linked by the above methods.Analysing the CARIS cases alone showed that consistently more than 80% of foeti are live-born, and survive. Chromosomal (22%), cardiovascular (21%) and brain (15%) anomalies are the most common congenital anomalies in the linked cases, which contribute to infant mortality. This pattern has remained largely unchanged over the study period. ConclusionUsing NHS numbers and soft linking, the majority of CARIS cases were linked to AWPS cases. Although both these databases should closely reflect each other, missing cases suggest there is scope for improvement in data collection. Sharing of data could also improve the quality of both databases.
Observational Data Exploration Via Online Tool For For Drugs and Cancer Risk
Authors: Iqbal Usman , Nguyen Phung Anh , Syed-Abdul Shabbir , Jian Wen-Shan , Li Yu-Chuan Jack
Published: April 2017

ABSTRACTObjectiveRapid change in health information technology system had dramatically increased health data accumulated. We aimed to develop an online informatics tool in order to evaluate the risk of drugs for cancer by utilizing medical big data.

Data SourceWe use the Taiwan’s National Health Insurance Database that has provided a huge data which covered all health information including characteristics and all drug information i.e. prescriptions, etc. of 23 million Taiwanese population. Front-end development: Web-based interface was developed by using PHP package and Javascript. In addition, we included the guidelines of evidence based medicine (EBM) level 3 for observational study such as cohort, case-control, and/or case serial self-control in order to support users interact with system. Back-end development: A package of Apache, MySQL & PHP was used to build the serve-side of the system. We integrated the Elasticsearch API5 to our system in order to search and analyze data immediately. The example of data transform to person-level from Taiwan NHI database is shown in Box 1. After then, we also integrated the analytics package (ie. R package) to perform the statistical analysis to a given study.

This online analytical tool has capability to massively explore and visualize big data for long term use drugs and cancers through OMOSC system which will help to do mass online studies for long term use drugs and cancer risk. It would help to direct the health care professionals with lack of datamining skills to lead the study. The constructed online system would generate automatically case and controls by utilizing large databases for long term drug exposures and cancer risk. ResultsThe results are shown in odds ratio (OR) and if selected some confounding factors then could also get adjusted odds ratio (AOR) for risk estimation with 95% Confidence Intervals (CI). We used SAS statistical software on the same dataset to validate the OMOSC system results. It could help to do massive online studies which will saves time and cost effective.

ConclusionSince the clinical trials are impossible to conduct due to cultural, cost, ethical, political or social obstacles. Therefore, this kind of research model would play an important role in health care industry by providing an excellent opportunities for solving the technological, informatics, and organizational issues towards other broad domains of drugs evaluation by utilizing large-scale databases.
Linkage of health care claims data and apps data: The ADVOCATE oral health care dashboard
Authors: Gabel Frank , O’hanlon Karen , Brankin Pamela , Bryce Robert , Trescher Anna-Lena , Haux Christian , Whelton Helen , Van der Heijden Geert , Listl Stefan
Published: April 2017

ABSTRACT

ObjectivesOral diseases are highly prevalent worldwide and their treatment places considerable economic burden on individuals and society. The ADVOCATE (Added value for Oral Care) project aims to promote new, patient-centred preventively oriented primary care and treatment.

As part of the project, a benchmarking dashboard is being developed to give stakeholders (i.e. health professionals, policy makers and health service planners) access to routinely collected data through a number of indicators such as: the types of care provided, costs of care and care outcomes, enabling comparisons across countries to be made.

ApproachAt the core of the ADVOCATE project, health data from several European countries are being collected. A bidirectional approach is used. Routinely collected claims data from 9 health insurers (insurance companies and NHS schemes) are being gathered. Concurrently, a patient e-questionnaire is being developed. Patients will be asked to answer additional health information questions anonymously whilst in the dental practitioner’s office using an app, thus providing data that isn’t available through the routinely collected claims data.

A previously developed set of indicators is being implemented in an electronic benchmarking dashboard. With the help of the linkage of aggregated forms of both claims data and data from the patient e-questionnaire, the dashboard will be used to visualize these indicators in order to monitor, direct and evaluate system change and related behaviors of patients, professionals, policymakers and insurers.

ResultsA pilot version of a dental healthcare dashboard has been developed using data from the Survey of Health, Aging, and Retirement Europe (SHARE).

In this context, the number of remaining teeth (indicating oral health status), the proportion of replaced teeth (indicating restorative activity of dentists) and the mean number of dentist visits per day (indicating oral health awareness of the population) were modeled and visualized for 12 European countries and Israel, resulting in highly diverse country groups in terms of the above indicators.

ConclusionThe ADVOCATE oral healthcare dashboard, containing essential information from both claims data and data from a patient e-questionnaire (via an app), can be used to review compliance with best practice guidelines and help close the guideline implementation gap.

It will be the first time that oral health professionals, insurers, and health policy makers will have the opportunity to review oral health care performance against standards.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635183".
The DASSL or Data Access, Storage, Sharing and Linkage Model & Proposals for an Enabling Data Environment for Health and Related Research in Ireland
Authors: Moran Rosalyn
Published: April 2017

ABSTRACTObjectivesThis project set out 1)to examine the barriers and facilitators which impact on researchers’ ability to access, share and link data and thus carry out health and related research in Ireland and 2)to explore possible solutions.

ApproachInterviews were conducted with individuals involved in the collection, use, storage, sharing and linkage of data in Ireland. Data-related needs, practices and attitudes were explored, as were respondents’ views regarding both existing and required infrastructure and services for the safe sharing and linkage of data. Ongoing discussions with key stakeholders, visits to centres of excellence abroad and a review of the international literature informed results.

ResultsBased on the work carried out a model for safe access, sharing and linkage of data within the Irish context was developed – the DASSL or data access, storage, sharing and linkage model. The model comprises seven elements – five related to infrastructure and services [a health research data hub, safe haven, trusted third party and data linkage service, output checking and disclosure control and a research support unit] required for safeguarding data, and two related to the broad legislative and socio-cultural context needed to facilitate implementation of the model i.e. governance and public engagement. It is suggested that a research data trust [RDT] would be established to house the DASSL elements and to provide the institutional and technical environment to respond to the growing data-related needs within the Irish health research environment and the broader data ecosystem.

ConclusionThe proposals put forward for creating a safe environment for access, sharing and linkage of research and related data need to be considered by all key stakeholders. It is suggested that a Data to Benefits Committee be established to drive strategic discussions and action towards the implementation of the DASSL model elements. Implementation of the DASSL model will allow for safe usage of currently under-exploited data which can help inform health and wellbeing but also serve national economic and social agendas.
Statin therapy and mortality among new long-term care residents in Ontario, Canada: the contribution of clinical assessment data to a population-based cohort study
Authors: Campitelli Michael , Bronskill Susan , Giannakeas Vasily , Morris Andrew , Maxwell Colleen , Bell Chaim
Published: April 2017

ABSTRACTObjectiveThere is limited evidence from randomized trials and observational studies to guide clinical practice regarding the use of statins in long-term care (LTC); the effectiveness of statins among those with limited life expectancy is not clear and there is concern that the risk of drug-related adverse events might outweigh any benefit. We examined the impact of initiating statin therapy on mortality for patients newly admitted to LTC.

ApproachPopulation-based health administrative data from Ontario, Canada were used to conduct a retrospective cohort study of newly admitted LTC residents, aged 66+ years and no statin use in the previous year, between January 1 2011 and December 31 2014. This cohort was linked to Resident Assessment Instrument (interRAI) data to capture clinical and functional characteristics (including frailty, activities of daily living, and cognitive function). The primary exposure was statin use within 30 days following LTC entry; residents who died or did not receive an interRAI assessment within 30 days were excluded. A propensity score for receiving statins was computed using resident demographic, clinical and functional characteristics. We matched exposed to unexposed patients on the basis of age (±1 year), sex, prior myocardial infarction(MI)/stroke hospitalization, frailty and propensity score (±0.2 standard deviations). Patients were followed in an intention-to-treat manner from the end of the exposure window until the earliest of death or March 31 2015. Cox regression was used to compare mortality between the study groups.

ResultsWe identified 39,560 newly admitted LTC residents aged 66+ years with no statin use in the previous year, of which 1,953 (4.9%) were prescribed a statin within 30 days of LTC entry. Propensity score matching resulted in 1,710 pairs of exposed and unexposed patients. In the matched cohort, those receiving statins had a lower rate of mortality compared with those not receiving statins (Hazard Ratio 0.77; 95% Confidence Interval [CI] 0.70-0.85). In pre-specified subgroup analyses, the association between statin use and reduced mortality persisted among those with and without a prior MI/stroke hospitalization and among those categorized as frail and not frail.

ConclusionOur data suggest initiating statins may be beneficial in reducing mortality risk among LTC residents, despite the complexity and advanced age of the patients. By linking rich resident-level health and functional assessment data with health administrative data we were able to characterize the association between demographic and clinical characteristics (including frailty) and exposure to statins more fully than with administrative data alone.
Geocoding routinely collected administrative data to measure access to alcohol outlets in Wales
Authors: Fry Richard , Rodgers Sarah , Orford Scott , Morgan Jennifer , Fone David
Published: April 2017

ABSTRACTObjectivesA substantial level of excess alcohol consumption results in a wide range of harm and the potential impact on health at the population level of a reduction in consumption is considerable. A proposed policy for reducing alcohol consumption is restricting the availability of alcohol through reducing the density of alcohol outlets. We set out to create a high spatial resolution alcohol outlet dataset suitable for evaluating longitudinal changes in chronic alcohol related conditions.

ApproachRequests were made for the names and location of all licensed alcohol outlets within each of the 22 Unitary Authorities in Wales, between Nov 2005 and Dec 2011. Data requested for each outlet consisted of: the date permission was granted or the licence became active, the licence expiry date or an indicated date of outlet closure, whether this premise is licensed for ON and/or OFF premise sales, the hours permissible to sell alcohol or general opening hours of the outlet and the type of premise as assigned by the LA if available. Our approach included collating, geocoding and manually matching alcohol outlet data received from each unitary authority for use in a longitudinal analysis of outlet density.

ResultsAll authorities were able to provide an actual or approximate license issue date, allowing us to summarise the number of outlets annually. Several authorities were unable to provide precise outlet closure dates, so the date of the last interaction with the outlet was used to generate an approximate end date. One-half of the unitary authorities were able to provide the On/Off sales status of outlets, and 9 were able to provide opening hours. From these data we were able to geocode 53% (range 28% to 72% by local authority) using GIS, the remaining 47% were matched using Google products to verify and extract a precise geographic location.

Conclusions The collation and processing of retrospective alcohol outlet data was successfully completed to enable the building of a longitudinal exposure dataset. There was considerable variation between the unitary authorities in the quality of address data, and data related to the availability of alcohol, for example opening hours. The lack of address structure required us to devise a manual address matching process to capture the addresses that could not be geocoded. To aid future data linkage based evaluations to provide policy evidence in a timely manner, local government datasets should use standardised data fields, including addresses and Point-of-Capture address verification.
Data safe havens to combine health and genomic data: benefits and challenges
Authors: Jones Kerina H , Lacey Arron S , Perkins Brian L , Rees Mark I
Published: April 2017

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely.

ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data.

ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals.

ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.
iConsent an Electronic Consent Platform with the MS Register
Authors: Middleton Rod , Ford David , Naeh Daniel
Published: April 2017

ABSTRACT

ObjectivesThe UK MS Register is a large scale observational research platform, capturing data from patients, NHS and carries out linkage with routine data from the SAIL databank.

We have 14,000 People with MS (PwMS) submitting Patient Reported Outcome Measures (PRoMS) quarterly and over 3000 patients consenting at NHS Sites around the UK

A differentiating factor between Register and previous attempts to capture PRoMS and clinical data was the goal that it be paperless. One area, where paper had to be used, was obtaining informed consent. Clinical participants are consented using a triplicate consent form, one copy for the patient, one for medical notes and one for the Register

It’s desirable for patients to be able to electronically consent, providing the following benefits:

Tablet computers already in use to collect PRoMS

Printing costs

Participant expectations

Improved content and user experience: improved feedback, of multimedia elements about informed consent

Increased familiarity with tablets

ApproachChanging consent methodology is complex, all documentation, processes and changes are reviewed by the ethics committee. A privacy protecting, secure software package (iConsent) was developed by modifying an existing package from Welsh Cancer Bank.

The software is server based, running on a Secured MS SQLServer 2014 and developed in .net to iOS/Android tablets

The practitioner taking consent explains the process, participants then see the approved documentation and materials. Finally they fill in their email address and name, and are presented with the consent form, the participant uses a stylus to sign. The practitioner then countersigns.

Once completed a digitally signed, secure pdf is generated on the server. Links are sent by email to the participant, the Register and unit administrator. The pdf is functionally identical to the paper.

ResultsThe South West Central Bristol ethics committee approved the software following guidance on security and documentation design. Staff were trained in system usage.

A number of patients were successfully e-consented, Of note was a potential issue with some patients and how MS impacts their ability to sign without resting a hand on the screen.

ConclusionPatients who have been e-consented have expressed satisfaction in the ease of use and security of the software. Patients being unable to rest their hands on the screen is being examined. Newer tablets can ignore inputs other than the stylus.

The MS Register intends to use the software in additional centres to capture patient consent.
Big Data, Big Responsibility! Building best-practice privacy strategies into a large-scale neuroinformatics platform
Authors: Popovich Christina , Jeanson Francis , Behan Brendan , Lefaivre Shannon , Shukla Aparna
Published: April 2017

ABSTRACT

ObjectiveThe Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its’ large-scale informatics platform, known as Brain-CODE (Centre for Ontario Data Exploration). Brain-CODE manages the acquisition, storage, processing, and analytics of multidimensional data collected from patients with a variety of brain disorders. Our vision is for the platform to act as an informatics catalyst; encouraging multidisciplinary research collaboration, data integration, and innovation in neuroscience research. Brain-CODE’s infrastructure was designed with best-practice privacy strategies built at the forefront to enable secure data capture of sensitive patient information in a manner that abides by government legislation while fostering data sharing and linking opportunities.

ApproachPrivacy and security features have been incorporated into the very foundation of Brain-CODE’s comprehensive guidelines, which are reinforced by our state-of-the-art approaches to keep patient data safe. To ensure clarity for study participants, we have developed standard consent language outlining how sensitive patient data will be collected, entered, de-identified, and shared using Brain-CODE. Moreover, our tiered approach to data accessibility enables the storage of encrypted Ontario Health Card Numbers as well as other patient information, secure long-term storage of de-identified data, and data sharing opportunities by request from third parties following risk-based analysis re-identification techniques. OBI has also established a comprehensive Information Security Policy and Informatics Governance Policies, as well as a carried out a Privacy Impact Assessment and Threat Risk Assessment for Brain-CODE.

ResultsBrain-CODE is proudly named a "Privacy by Design" Ambassador by the Office of the Information and Privacy Commissioner of Ontario, Canada. Moreover, approximately 200 neuroscience researchers and 35 institutions from across Canada have adopted our standard consent language to enable secure data sharing within and across neurological disorders as well as linkage opportunities with national and international databases in a secure environment.

ConclusionOBI’s rigorous approach to data sharing in the field of neuroscience maintains the accessibility of research data for big discoveries without compromising patient privacy and security. We believe that Brain-CODE is a powerful and advantageous tool; moving neuroscience research from independent silos to an integrative system approach for improving patient health. OBI’s vision for improved brain health for patients living with neurological disorders paired with Brain-CODE’s best-practice strategies in privacy protection of patient data offer a novel and innovative approach to “big data” initiatives aimed towards improving public health and society world-wide.
Validation and Vindication: Enhancing the quality of Electronic Health Record (EHR) outcomes in a large prospective study biobank
Authors: Sansome Sam , Turnbull Iain
Published: April 2017

ABSTRACT

ObjectivesTo date we have derived and linked over 1.5 million electronic health records (EHR) to over 280,000 of our biobank participants (across 10 diverse urban and rural geographical regions of China) through established morbidity and mortality registries, and by linkage to the China national Health Insurance (HI) system to identify disease outcomes.

To ensure accuracy, completeness and consistency of these outcomes we have developed data handling, processing, and validation procedures.

ApproachOn a disease-by-disease basis we have developed multiple strategies varying from simple standardisation of disease description to ICD10, to a more comprehensive system of retrieval and examination of medical notes.For those diseases chosen for more precise phenotyping, such as stroke, we send trained research technicians to hospitals where they retrieve clinical documents that correspond to the disease outcomes as reported by registries and HI agencies.

A Portable Validation Device (PVD) tablet has been developed to guide the medical notes gathering process and ‘validate’ chosen disease events, by photographing key documents and ascertaining disease subtypes. For diseases selected for further adjudication, medical specialists in China are given access to an internet based Case Adjudication System for clinical Events (i-CASE). The information captured by PVD is presented in i-CASE according to pre-specified clinical and procedural criteria.

At least 5% of all of data undergoes quality control assessments at both validation and adjudication stages in the UK.

ResultsDiseases causing the greatest burden in China (Cancer, Diabetes, COPD, Stroke and IHD) have all been standardised and subsequently validated using the PVD where appropriate. To date, we have retrieved over 45,000 hospital records through the PVD and, more recently, over 32,000 IHD and Stroke cases have been adjudicated through the i-CASE system.

Outcomes are categorised as ‘confirmed’, ‘re-classified’, ‘sub-typed’ or ‘refuted’ and are integrated back into the study dataset for future analysis, along with additional data gathered from the notes.

ConclusionThe study provides a uniquely rich and powerful resource for investigating environmental and genetic determinants of chronic disease in the Chinese population. By treating disease types on a case-by-case basis we can both confirm their accuracy, and to link more precise clinical phenotypes to emerging data types, such as genetic and omics data. These innovations in disease validation and adjudication will enable the study to reach new insights into the aetiology of chronic disease to improve the health of the population in China and elsewhere.
An International Cross-cohort Harmonization and Data Integration Initiative towards Achieving Statistical Power and Meaningful Results
Authors: Flanagan Tanya , Fortier Isabel , Fon Sing Mélanie , Moore Celine
Published: April 2017

ABSTRACT

ObjectivesThe complex interaction between lifestyle, behaviours, genetic factors and the social and physical environment have a fundamental role in modulating risk and/ or progression of health outcomes, especially cancer. To address this complexity, access to large-scale cohorts involving hundreds of thousands of participants and collecting comprehensive and valuable information are required. In the real world however, attaining adequate statistical power presents a major challenge.

Retrospective data harmonization and integration across multiple cohort studies has been shown to be an effective analytical approach to attaining statistical power, with the potential to support population health research and policy related questions and improve our understanding of the complex factors affecting health outcomes.

ApproachLarge cohorts, with at least 50,000 participants, initiated in countries all over the world, focused on innovative research on cancer and other chronic diseases were invited to participate in this retrospective data harmonization initiative. Cohorts shared their comprehensive metadata related to their study content and design. Almost 150 variables, selected for their relevance to be part of a generic set of information useful for a broad range of research question, were assessed for their harmonization potential and made available on an online searchable study catalogue. Lastly, a proof of concept research question on the retrospective harmonized data was conducted and aimed to investigate methods to analyze individual patient data from multiple studies by studying the determinants associated with age at menopause.

ResultsEight cohorts from multiple countries shared their comprehensive metadata related to their study content and design, resulting in over 2 million study participants. Of the 150 potential variables, the majority of them were harmonizable for co-analysis. The proof of concept research question, applied to these variables generated interesting results, widely supported by other research on this topic, found in the literature. This work demonstrates the value of retrospective data harmonization and integration to be an effective analytical approach to attaining statistical power.

The searchable study catalogue, available online for researchers to use in their own international research projects offers a new innovative tool for potential co-analysis of similar measures collected by separate cohort studies.

ConclusionRetrospective harmonization offers an innovative approach to optimize use of existing research data with increased statistical power.
Reusable, set-based selection algorithm for matched control groups
Authors: Thayer Daniel , Gregory John W , Kosnes Liv , Berridge Damon , Heaven Martin L , Ford David V , Lloyd Keith , John Ann
Published: April 2017

ABSTRACT

AimsThe wealth of data available in linked administrative datasets offers great potential for research, but researchers face methodological and computational challenges in data preparation, due to the size and complexity of the data. The creation of matched control groups in the Secure Anonymised Information Linkage (SAIL) Databank illustrates this point: SAIL contains multiple health datasets describing millions of individuals in Wales. The volume of data creates the potential for more precise matching, but only if an appropriate algorithm can be applied. We aimed to create such an algorithm for reuse by many research projects.

MethodsWe developed set-based code in SQL that efficiently selects matches from millions of potential combinations in a relational database environment. It is parameterized to allow different matching criteria to be employed as needed, including follow-up time around an index event. A combinatorial optimisation problem occurs when a potential control could match more than one subject, which we solved by ranking potential match pairs first by subject with the fewest potential matches, then by closeness of the match.

ResultsOne example of the algorithm’s use was the Suicide Information Database Cymru, an electronic case-control study on suicide in Wales between 2003 and 2011. Subjects who had a cause of death recorded as self-harm were each matched to twenty controls who were alive at the subject’s date of death and had the same gender and similar birth week. The rate of matching success was >99.9%, with all subjects but one matching the full twenty controls. >99.99% of the matched controls had a week of birth that was identical to the subject. The second example was a matched cohort study looking at hospital admissions and type 1 diabetes, using the Brecon register of childhood diabetes in Wales, with matching based on week of birth within two weeks, gender, county of residence, deprivation quintile, and residence in Wales at time of diagnosis. This study had a matching rate of 98.9%; 97.5% of subjects matched to five controls, and 69.8% of matches had the same week of birth.

ConclusionsThis algorithm provides good matching performance while executing efficiently and scalably on large datasets. Its implementation as reusable code will facilitate more efficient, high-quality research in SAIL. Instead of spending many hours developing a custom solution, analysts can execute parameterized code in a few minutes. We hope it to be useful more widely beyond SAIL as well.