bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of regular GP visits on diabetic potentially preventable hospitalisations
Authors: Youens David , Moorin Rachael 
Published: April 2017

ABSTRACT
ObjectivesPotentially preventable hospitalisations (PPHs) place a substantial burden on the Australian health system, with over 212,000 PPHs reported for diabetes alone in 2005/06. Timely and effective primary care may reduce the risk of acute episodes and subsequent hospitalisation among those with chronic diseases. The Medicare Enhanced Primary Care program, introduced to improve the regularity and quality of healthcare provided by GPs to Australians with chronic disease, has been shown to improve regularity of GP access. The aim of our study is to ascertain whether more regular GP access reduces diabetes PPHs.
ApproachWhole of population longitudinal study using linked hospital, mortality, and general practice data. Regularity of GP access was determined through calculating the variance in the number of days between GP visits within a year. Regular GP contact was taken to indicate planned, proactive primary care, and irregular GP contact taken to indicate unplanned, reactive care. Multilevel modelling techniques were used to determine the relationship between regularity of GP access and diabetic PPHs in the population at risk of diabetes. Analyses were performed for the periods prior to and following the introduction of policies aimed at promoting primary care contact.
ResultsThis paper will report results on the relationship between regular, proactive GP contact and diabetes PPHs in the population with and at risk of diabetes. Socio-demographic, geo-spatial and access factors were found to influence the regularity of GP access. Individual factors, in particular disease status, were found to substantially modify the relationship between regularity and PPH outcome. Findings differed between the periods prior to and following the introduction of government policies aimed at promoting proactive primary care in chronic disease.
ConclusionFindings from this study will provide important evidence concerning strategies to reduce PPHs in relation to diabetes, which will be of interest to policy-makers wishing to reduce unnecessary hospitalisations. This work will extend to examine the impact of regularity on PPHs for a number of other chronic conditions considered priorities in Australia.
Implementing privacy-preserving record linkage: welcome to the real world
Authors: Boyd James , Ferrante Anna , Brown Adrian , Randall Sean , Semmens James 
Published: April 2017

ABSTRACT
ObjectivesWhile record linkage has become a strategic research priority within Australia and internationally, legal and administrative issues prevent data linkage in some situations due to privacy concerns. Even current best practices in record linkage carry some privacy risk as they require the release of personally identifying information to trusted third parties. Application of record linkage systems that do not require the release of personal information can overcome legal and privacy issues surrounding data integration. Current conceptual and experimental privacy-preserving record linkage (PPRL) models show promise in addressing data integration challenges but do not yet address all of the requirements for real-world operations. This paper aims to identify and address some of the challenges of operationalising PPRL frameworks.
ApproachTraditional linkage processes involve comparing personally identifying information (name, address, date of birth) on pairs of records to determine whether the records belong to the same person. Designing appropriate linkage strategies is an important part of the process. These are typically based on the analysis of data attributes (metadata) such as data completeness, consistency, constancy and field discriminating power. Under a PPRL model, however, these factors cannot be discerned from the encrypted data, so an alternative approach is required. This paper explores methods for data profiling, blocking, weight/threshold estimation and error detection within a PPRL framework.
ResultsProbabilistic record linkage typically involves the estimation of weights and thresholds to optimise the linkage and ensure highly accurate results. The paper outlines the metadata requirements and automated methods necessary to collect data without compromising privacy. We present work undertaken to develop parameter estimation methods which can help optimise a linkage strategy without the release of personally identifiable information. These are required in all parts of the privacy preserving record linkage process (pre-processing, standardising activities, linkage, grouping and extracting).
ConclusionsPPRL techniques that operate on encrypted data have the potential for large-scale record linkage, performing both accurately and efficiently under experimental conditions. Our research has advanced the current state of PPRL with a framework for secure record linkage that can be implemented to improve and expand linkage service delivery while protecting an individual’s privacy. However, more research is required to supplement this technique with additional elements to ensure the end-to-end method is practical and can be incorporated into real-world models.
Linking the Diagnostic Imaging Dataset (DID) to Hospital Episode Statistics (HES) – improving and understanding the diagnosis of lung cancer
Authors: Kennedy David , Bentley Abigail , Denton Erika , Ohuma Ella , Ormison-Smith Nick , Dixon Sheila 
Published: April 2017

ABSTRACT
ObjectivesTo link the Diagnostic Imaging Dataset (DID) to Hospital Episode Statistics (HES) to explore the association between patient imaging and hospital based care and outcomes for cancer patients in English NHS hospitals. This is the first time this linkage has taken place. The analysis also aims to look at how each patient first presented in HES (A&E, emergency admission, elective care, regular attendance), when and by what route relevant tests were requested and geographic variation in access to imaging
ApproachPatient imaging records from the DID were linked to HES databases containing details of all inpatient, outpatient and A&E admissions at NHS hospitals in England in 2012/13 and 2013/14. Match rank criteria were developed to ensure that patients in HES were accurately linked to patients in DID by NHS number, date of birth and other unique identifiers. We used HES to identify patients with a lung cancer diagnosis and investigate their use of imaging and the temporal nature of tests to evaluate whether patients were following recommended pathways (e.g. having chest x-rays followed by CT scans before their lung cancer diagnosis) and the time between these events. Lung cancer patients were identified by developing an algorithm to search through diagnostic fields for clinical codes within HES databases. Where there was a lung cancer code the record was flagged and subsequently extracted.
ResultsThe combined HES and DID datasets consisted of more than 340 million records. Within this large dataset we identified 49,888 patients with a lung cancer code in one of their HES diagnosis fields to a DID diagnostic image record for inpatient records alone. A high proportion (97%) of records were matched in one of the top 3 rank levels, suggesting the linkage was successful. The results illustrate the relationship between the imaging referral pathway and hospital episodes (e.g. surgical resection, emergency presentation).
ConclusionInvestigating how people engage with imaging services and hospital care will increase our understanding of the pathways associated with lung cancer diagnosis.
The results from this analysis will contribute new knowledge about how lung cancer patients interact with hospitals and imaging services.
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.