bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Linked Administrative Data to Examine the Educational Outcomes of Children in Care in Manitoba
Authors: Brownell Marni , Chartier Mariette , Au Wendy , MacWilliam Leonard 
Published: April 2017

ABSTRACT
ObjectivesManitoba has one of the highest rates of children in out-of-home care in the world. Our aims were to: 1) describe characteristics of children in care; 2) determine differences in educational outcomes for children in care, children receiving in-home protection services and children not involved with child welfare; and, 3) identify factors contributing to educational success for children in care.
ApproachPopulation-level administrative data from the Population Health Research Data Repository at the Manitoba Centre for Health Policy, were used. Several data sources were linked together at an individual level: child welfare services (e.g., in care, in-home services; hospital birth records (e.g., birth weight, gestational age); health status (e.g., from hospitalizations, physician visits); family risk factors (e.g., maternal prenatal smoking and/or drinking, maternal education); family characteristics (e.g., number of children in family, receipt of income assistance); education assessments at kindergarten, grades 3, 7, 8, 9 and 12. Children in care from 1998 onward were included. Number of children analyzed differed depending on the educational outcome examined: e.g., analyses at kindergarten include 1,893 children in care, 4,229 children receiving in-home services, and 32,280 children not involved with child welfare services; for high school completion these numbers were 11,234, 40,671, and 162,265 respectively. Multivariate regressions identified characteristics associated with educational outcomes for children in care.
ResultsOver one-third of children in care first entered care as infants, and of those, close to half were apprehended at birth. 5.5% of children in Manitoba spent some time in care before their 15th birthday, with an almost tenfold difference for Indigenous (16.6%) and non-Indigenous (1.7%) children. Across the range of educational outcomes, children in care did more poorly than children receiving in-home services, who in turn did more poorly than children with no child welfare involvement. For example, only 33.4% of children in care completed high school compared to 66.8% of children receiving in-home services and 89.3% of children with no child welfare involvement. Adjusting covariates reduced differences but all remained statistically significant. Factors associated with positive outcomes for children in care differed depending on the outcome and included measures like poverty (at Kindergarten, p<0.05; at high school, p<0.0001) and total number of care placements (at Kindergarten, p<0.05; at grade 9, p<0.01).
ConclusionsChildren in care are at risk of experiencing poor educational outcomes, from school entry onwards. Identifying factors associated with educational success for children in care can inform policy and program development.
A review of a standards-based set of information security practices applied in Data Linkage Centers
Authors: Marinho Da Silva Márcia Elizabeth , Camargo Kenneth , Pinheiro Rejane , Coeli Claudia 
Published: April 2017

ABSTRACT
ObjectiveThis research aimed to study regulatory and operational aspects related to information security, especially confidentiality, in organizations that systematically carry out record linkage.
ApproachWe searched international experiences of data linkage units from the literature and from the catalog of International Population Data Linkage Network (IPDLN) members. In addition, we surveyed technical standards of the International Association for Standardization (ISO) on health informatics.
ResultsWe studied organizations in Australia, Canada, UK and the United States. Six standards were selected for deep analysis. In the end, we organized a set of 75 practices relating to information security in data linkage units, grouped by 5 dimensions: infrastructure and operations; record linkage model; relationship with managers; relationship with researchers and relationship with the society. The linkage process must be described in a sufficiently clear and didactic way, so that ordinary citizens are able to understand that the privacy of their health information is protected. In addition to a transparent work process, the data linkage center must also make their privacy policies available. The Australian and Canadian experiences with ethic review committees that include social participation and awareness of media and explanations to the public are a good source of inspiration. Regarding safety, the institutions responsible for health databases should apply security controls in their information systems to consider the rules on consent to perform record linkage. Ideally, all institutions should seek full compliance with the controls recommended in the technical safety standards. However, the scarcity of resources (human, financial and technical) lead to the prioritization of the implementation of these security controls. The criteria for this prioritization can be given by feasibility analysis (cost / time impact, benefits), providing an orderly road map for the adoption of these measures.
ConclusionThe practices systematized in this study can be used in order to check current information security conditions of data linkage centers and as guidelines for further improvements. This will certainly bring more confidence in the data linkage center process and, at least, help researchers, managers and society move forward toward the same objective of better public health.
Modelling Diagnostic Validity Estimates from Administrative Health Data
Authors: Kroeker Kristine , Lix Lisa M. , Jiang Depeng , Muthukumarana Saman 
Published: April 2017

ABSTRACT
ObjectivesValidation studies compare diagnostic information in linked administrative and reference (i.e., gold standard) data; they are an essential tool to develop accurate case definitions, the rules used to identify individuals in administrative data with a specific health condition. Validation studies often estimate the accuracy of multiple case definitions, in order to identify the data features (e.g., diagnosis codes, type of data source) that influence accuracy estimates. Descriptive analyses are commonly used to select a case definition(s) with the greatest accuracy estimates, but fail to account for uncertainty in accuracy estimates. The objectives were to: (1) compare the performance of regression-based approaches to test for differences in diagnostic accuracy estimates, and (2) demonstrate how to apply and use these models.
ApproachComputer simulation was used to compare three regression models: (a) univariate fixed-effects models applied to estimates of sensitivity and specificity; (b) univariate fixed-effects model for Youden's index, the average of sensitivity and the complement of specificity; and (c) bivariate random-effects joint model of sensitivity and specificity. The simulations varied the means and variances of sensitivity and specificity, the correlation between these parameters, and the number of case definitions. Performance was compared using: (a) bias (i.e., difference between estimated and observed mean), (b) mean squared error (MSE), the sum of the estimated variance and bias squared, and (c) 95% confidence interval (CI) coverage, the proportion of times the population mean is contained in the 95% CI. For objective 2, we applied the models to estimates of diagnostic accuracy from a published rheumatoid arthritis (RA) validation study with 61 case definitions.
ResultsUnivariate models of sensitivity and specificity had lower bias than the bivariate model (e.g., univariate=1.8%, bivariate=2.2%). The bivariate model had a smaller MSE than the univariate models when sample size was large and there was a small correlation between sensitivity and specificity (e.g., univariate=3.4%, bivariate=2.6%). Across all scenarios, the univariate model for Youden’s index showed small bias (average=2.4%) and MSE (average=2.1%). For objective 2, the univariate models of sensitivity, specificity, and Youden’s index revealed multiple case definition features that were associated with estimates of RA diagnostic accuracy: 1+ diagnosis in hospital records, >1 diagnosis in physician claims, and 1+ diagnoses by a specialist physician.
ConclusionsWe recommend the bivariate model when a validation study contains a large number of case definitions. When the data contain a small number of case definitions, univariate models are recommended.
Clinical Text De-identification and Other Large Scale Processing Tasks in Resource Constrained Environments
Authors: Jackson Richard , Dobson Richard , Stewart Robert 
Published: April 2017

ABSTRACT
ObjectivesClinical text de-identification is a common requirement of the ‘enclave’ governance model of ethical EHR research. However, there is often little consideration of the engineering task that is required to scale these approaches across the hundreds of millions of clinical documents containing personal identifiers that are resident in the data repositories of a typical NHS Trust. Similarly, natural language processing is an increasingly important field of clinical data science, yet it requires fault tolerant approaches to data processing. This work concerns the development of “turbo-laser” - a distributed document processing architecture based upon the popular ‘battle hardened’ Spring Batch framework - an industry standard for large scale processing tasks.
ApproachUsing Spring Batch, we developed a highly scalable unstructured data processing framework, using the concept of remote partitioning. Remote partitioning allows us to offload processing tasks to any and all computers in a network. With this approach, it is possible to harness the entire compute available of an organisation, whether it be an office of 15 desktop PCs that go unused overnight, or a compute cluster of a thousand processors. This method is especially valuable in the NHS, where the provision of sufficient compute to make large scale analytics possible are often hindered by the lack of available hardware, or difficulties in navigating technical governance policies ill equipped for the demands of modern data science.
ResultsTurbo-laser was developed in consideration of the processing challenges common in the NHS. Currently, four types of ‘job’ are available - De-identification, using the Cognition algorithm, generic GATE output, text extraction from binary files such as MS Office, PDF and scanned documents, and a document re-compiler to deal with EHR legacy issues. Examples of turbo-laser usage include processing 9 million binary documents on modest hardware, within 48 hours.
ConclusionTurbo-laser is an enterprise grade processing tool, in keeping with the software engineering pattern of ‘batch processing’ that has been at the forefront of the informatics movement. An open source project, it is hoped that others may contribute and extend its principles, lowering the barrier of large scale data processing throughout the NHS.
High resolution data from everyday life: coproducing a technically robust and engaging sensing platform
Authors: Machin Matthew , Hassan Lamiece , Ainsworth John 
Published: April 2017

ABSTRACTObjectives Researchers are increasingly recognising the potential for connected health devices – in particular, wearables and smartphones - to capture high-resolution, multi-dimensional data from everyday life. The Dementias Platform UK project aims to develop research capacity in this emerging area by providing a combination of hardware and software: a pool of devices capable of generating data supported by a ‘sensing platform’ designed to securely receive, store and link these data with sources, including clinical records and cohort data. We collected feedback from researchers and patients to (i) develop requirements for the sensing platform and (ii) inform procurement of a device pool.
ApproachSeparate workshops were held to involve (i) researchers (public and private sector) and (ii) over 30 patients from four potential user groups, including people with dementia. Both groups gave feedback on the suitability and acceptability of a range of wearable devices for capturing data for different study purposes. Additionally, researchers commented on the platform functionality. Patients were provided opportunities to handle multiple devices and test them at home. We captured feedback at workshops using notes, which were collated, shared and discussed among the team. An agile software development methodology was used to respond rapidly to changing requirements.
ResultsPatients supported plans for connected health dementia research and, in principle, were willing to wear devices, provided they consented voluntarily and data were stored securely and confidentially. Many patients were prepared to undergo some level of inconvenience, for example wearing devices for longer periods, particularly if given ongoing feedback about research progress, findings and benefits. Researchers and patients agreed the platform should be open to a wide range of devices, available currently and in future. Researchers envisaged using the platform for a range of projects and data types. As a result, we established a device pool (including wearables, smartphones and tablets) and developed a generic, ‘device-agnostic’ platform to receive and store data. Platform architecture was designed to be as flexible as possible to allow for future modifications.
ConclusionWe found it was feasible to integrate requirements from both researchers and patients when developing a sensing platform for dementia research. By seeking feedback from both user groups, we were better able to attend to device linkage requirements, platform functionality and acceptability, integrating these within development and procurement processes. Furthermore, we identified aspects of research setup and design that could support sustained engagement from participants, thereby improving data completeness and quality.
IMECCHI-DATANETWORK: empowering knowledge generation through international data network
Authors: Le Pogam Marie Annick , Metcalf Amy , Johnsen Søren Paaske , Hider Phil , Patel Alka , Kim Hongsoo , Carlini Emanuele , Perego Raffaele , Quan Hude , Gini Rosa 
Published: April 2017

ABSTRACT
ObjectivesThe International Methodology Consortium for Coded Health Information (IMECCHI), an international collaboration of health services researchers, launched the IMECCHI-DATANETWORK initiative in October 2015. Its main objective is to enable replication of observational studies across countries through a distributed data infrastructure.
ApproachIn a distributed data infrastructure, individual raw data are not shared. Instead, data are converted locally using a common data model (CDM) and loaded into a common software for data processing and analysis. Whenever a study protocol is agreed upon and ethically approved, an ad hoc procedure is programmed - using that software - including the data processing steps needed to create the analytical dataset from the CDM: record linkage, case selection, sampling, matching, etc. The procedure is then shared and locally run by each partner to generate an analytical dataset of integrated data. Analytical datasets may then be shared and pooled for statistical analyses.
ResultsSix partners of the IMECCHI collaboration, located in countries across 4 continents (Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland), currently participate in the initiative. They first conducted a survey to describe the origin, content, completeness and main attributes of each table in their original databases. Based on the results of the survey, a CDM was created, encompassing 4 tables of coded or structured data to be linked at the individual level using a common personal identifier: (1) characteristics of the subjects with dates of birth and death; (2) hospital discharge summaries with diagnosis and procedure codes, and admission, discharge and procedure dates; (3) drug dispensing information with date of dispensing, drug name, duration of the amount of active principle according to the Defined Daily Dose of the World Health Organization; (4) causes of death. In each table, additional attributes describe the coding systems in which the other attributes are coded. Using such specific attributes facilitates interoperability across multiple coding systems. The open source Java-based software, TheMatrix, which operates on flat csv files using a domain-specific programming language, was chosen to embed the ad hoc procedure.
ConclusionWithin the IMECCHI-DATANETWORK initiative, databases from various countries will be locally converted in a CDM which will facilitate study replication in a distributed fashion while granting interoperability across coding systems. Through such international data networks, data are empowered for creating results which are generalizable to multiple countries. Cross-border data sharing and international comparisons are also facilitated.
Gaps in Health and Wealth: The relationship between trends in income inequality and breastfeeding inequalities
Authors: Nickel Nathan , Brownell Marni , Chateau Dan , Katz Alan , Burland Elaine 
Published: April 2017

ABSTRACT
ObjectivesThe objective of this study was to identify whether breastfeeding inequalities have increased between 1984 and 2014 and to examine whether trends in income inequality are related to breastfeeding inequalities.
MethodsWe used linkable administrative data from the Population Health Research Data Repository. Our sample included all infants born in Manitoba, 1984 to 2014. We used area-level income – derived from the Canadian Census – to stratify infants into income quintiles. Canadian Census income data were also used to quantify provincial level income inequality for each fiscal year in our study period. Data from the hospital discharge abstract database were used to classify infants according to whether or not they had initiated breastfeeding. We linked infant data to maternal data using the Manitoba health insurance registry to capture maternal characteristics – including the mother’s postal code of residence and her age at first birth. We used generalized linear models to calculate income quintile-specific breastfeeding rates for each fiscal year in our observation period for all of Manitoba. We also calculated age-adjusted breastfeeding rates to account for the changing age distribution in Manitoba mothers, over time. We measured breastfeeding inequities using the concentration index as well as the rate ratio and rate difference (comparing the breastfeeding rate between the highest and lowest income quintiles). We quantified income inequality using the Gini coefficient on income. Trend analyses and two-sided Z-tests tested for changes, over time. Time by income-quintile interactions tested whether breastfeeding rates were statistically significantly different, across socioeconomic groups.
ResultsBreastfeeding rates increased from 1985 to 2014, from 72% to 81% (p<0.01). The Gini coefficient increased from 0.16 to 0.21; a linear trend test of the Gini coefficient showed income inequality increased over the study period (p<0.05). Rate differences, rate ratios, and the Concentration index showed that significant breastfeeding inequalities existed throughout the study period. Trend tests revealed that breastfeeding inequalities did not increase, over time.
ConclusionsAggregate analyses may suggest overall improvement when inequality persists. Although there was improvement in breastfeeding initiation rates, children from lower socioeconomic status continue to lag behind their counterparts. Policy-focused health equity research needs to measure outcomes, overall, and inequity across time.
Linking hospital and immigrant landing data to understand patterns of hospital use among refugees in Canada
Authors: Ng Edward , Sanmartin Claudia , Manuel Douglas 
Published: April 2017

ABSTRACT
ObjectivesIn Canada, refugees represent between 10% and 15% of the total immigrant annual in-take of immigrants. Refugees are generally admitted with different selection criteria and have different settlement challenges which may lead to higher health risk, compared to other categories of immigrants. The purpose of this study is to use linked hospital and immigrant landing data to report hospitalization rates among refugees arriving to Canada between 1980 and 2006 with special focus on those from refugee-dominant source areas, namely Poland and Vietnam, and the Middle East.
ApproachData from the 2006/07-2008/09 Discharge Abstract Database (DAD) were linked to the 1980-2008 Immigrant Landing File (ILF) to identify hospitalizations among immigrants by category of admission. Age-standardized hospitalization rates (ASHR) for overall causes (excluding pregnancy), and for selected causes are derived for refugees overall and by specific source country. Rates are compared with those for the Canadian-born population and with economic class immigrants from the same source country.
ResultsOverall, refugees had substantially lower overall ASHR compared with the Canadian-born population (494 per 10,000, 95% CI, 487, 500 vs 891 per 10,000, 95% CI, 890, 892, retrospectively). Among refugees, those who landed in Canada (refugee claimants) had the highest ASHR (539, 95% CI 524,555), especially for circulatory disease. Among refugee-dominant areas, Government-assisted refugees from the Middle East tended to have higher ASHR at levels comparable with that of the Canadian born population (for circulatory disease). Refugees had higher rates of hospitalization compared with their economic class counterparts, except among those from Poland.
ConclusionRefugees, like other immigrants, generally had lower hospitalization rates compared with the Canadian born population, but the heterogeneity within groups reveals specific vulnerability for certain types of health care use.