bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of interventions to improve inpatient hospital documentation within electronic health records: A Systematic Review
Authors: Wiebe Natalie , Varela Lucia Otero , Niven Daniel , Ronksley Paul E , Iraggori Nicolas , Robertson Helen Lee , Quan Hude 
Published: September 2018

IntroductionDespite increased use of electronic health records (EHRs), EHR documentation quality remains poor. Consequently, EHR data quality is also negatively affected. Many services, including disease surveillance and health services research, utilize EHR data. Accordingly, several studies have attempted to improve EHR documentation quality in the inpatient setting using various interventions.
Objectives and ApproachThe purpose of this systematic review was to synthesize the literature, and assess the effectiveness of interventions seeking to improve inpatient EHR documentation quality. To identify relevant experimental, quasi-experimental and observational studies, a search strategy was developed based on elaborate inclusion/exclusion criteria using four main themes: EHR, documentation, interventions, and type of study. Four databases, Cochrane, Medline, EMBASE, and CINAHL, were searched. Study quality assessment and data extraction from selected studies were performed using a Downs and Black and Newcastle-Ottawa Scale hybrid tool, and a REDCap form, respectively. Data was then analyzed and synthesized in a narrative semi-quantitative manner.
ResultsAn in-depth search of the identified databases, grey literature and reference lists, revealed a final 20 studies for inclusion in this systematic review. Due to high heterogeneity in study design, population, interventions, comparators, document types and outcomes, data could not be standardized for a quantitative comparison. However, statistically significant results in interventions and affected outcomes were further presented and discussed. A higher number of studies reported significantly improved EHR documentation when using the interventions: ‘Education’ and ‘Implementing a new EHR Reporting System’. When implementing two or more interventions, more outcome measures were affected. There was no association between study quality or study design and number of interventions used. Only one of the 20 studies found EHR documentation worsened with the interventions used.
Conclusion/ImplicationsInterventions implemented to enhance EHR documentation are highly variable and require standardization. Emphasis should be placed on this novel area of research to improve communication between healthcare providers, enhance continuity of care, reduce the burden in health information management, and to facilitate data sharing between centers, provinces, and countries.
Patterns of pharmacotherapies used to treat alcohol use disorders: A population-based administrative data study
Authors: Nickel Nathan , Leong Christine , Prior Heather , Konrad Geoffrey , Bolton James , MacWilliam Leonard , Ekuma Okechukwu , Paille Michael , Valdivia Jeff 
Published: September 2018

IntroductionAlcohol use disorders (AUDs) – mental and/or physical health diagnoses due to harmful alcohol consumption – are associated with compromised quality of life for the individual. Over the past two decades, pharmacotherapies have been developed to treat alcohol addiction and may help mitigate the harmful outcomes linked with excessive alcohol.
Objectives and ApproachThe objectives were to examine the pharmacotherapy dispensation patterns among individuals with an AUD and their mental health comorbidities. We used ICD codes from medical claims and hospital discharge data to identify anyone with a physical / mental health diagnosis due to harmful alcohol consumption – AUD, April 1, 1996-March 31, 2015. We identified mental health comorbidities using administrative health records. Drug dispensation data were used to identify all first-time prescriptions for acamprosate, naltrexone, or disulfiram occurring after an initial AUD diagnosis. Generalized linear models tested for predictors of receiving a prescription and to identify differences in mental health comorbidities.
ResultsWe identified 53,556 treatment niave individuals with an AUD who were eligible to receive one of these three prescriptions. 493 of these received a prescription for acamprosate, naltrexone, or disulfiram. The majority of prescriptions came from general practitioners from urban centers. Those with a prescription were significantly more likely to have a comorbid mood or anxiety diagnosis. Those with a prescription were more likely to have a physician visit for a mental health issue a year to two years before diagnosis compared with those who did not have a prescription; and, they were more likely to be dispensed a selective serotonin, a sedative, and an anti-anxiety medication prior to receiving an AUD diagnosis.
Conclusion/ImplicationsDrug therapies to aid in the recovery from AUD are being underutilized. Diagnosis of and treatment for mental health disorders is more common among those dispensed these medications. Programs that study clinicians’ use of AUD-targeted drug therapies should be considered, while psychiatric services in addiction care require significant improvement.
A Toolbox for Research – Population Data BC’s tools for overcoming challenges in the data access process
Authors: Medearis Melissa , Mintha Jim 
Published: September 2018

IntroductionNavigating the regulatory environment of data access is complex. Where there are common challenges, there are opportunities to develop systems and services that can help researchers. These tools are designed to ensure secure and smooth navigation of the process of data access and data analysis.
Objectives and ApproachThere is increasing focus on technical solutions to support data requests and analysis. We have responded to existing challenges by developing a suite of tools and services including: MetaData Central, DARonline, AppTracker, Snippets, and REDCap. We will describe the obstacles that motivated each tool, how it was built, and how it addresses the original issue. We will also discuss the overall challenges in building these tools, including technical complexity and getting buy-in from data stewards and researchers. Finally, we will touch on some of the progress we have seen based on these tools, and our plans for future development.
ResultsMajor obstacles included providing information to researchers to help form their data request, communications among researchers, data stewards and PopData, general automation of procedures, and sharing among researchers. The tools developed in response include: MetaData Central, AppTracker (for project tracking); DARonline (for data requests); REDCap (for primary data collection); and Snippets (for code sharing). MetaData Central has proven to be a valuable resource for all stakeholders to learn basic data descriptions without needing access to the record level data. This helps in making sure the data requested meets the needs of the research project. AppTracker is helpful for monitoring applications, and reduces the time it takes for us to monitor compliance and provide reports. Other tools are newer and have less evaluation information.
Conclusion/ImplicationsUser-friendly, flexible tools are beneficial for making the data access process smoother and for meeting the evolving needs of researchers and data stewards. These tools can create both transparency through a common source of information and efficiency by automating routine processes.
Differential spatial distribution of hepatitis B virus by ethnicity in British Columbia, Canada: Expanded role of a large administrative cohort
Authors: Butt Zahid , Janjua Naveed , Wong Stanley , Yu Amanda , Alvarez Maria , Chapinal Nuria , Wong Jason , Binka Mawuena , Buxton Jane , Gilbert Mark , Darvishian Maryam , Rossi Carmine , Krajden Mel 
Published: September 2018

IntroductionMost chronic hepatitis B virus (HBV) infections in Canada are diagnosed among immigrants from endemic countries and lack traditional risk factors while most acute infections are usually diagnosed in Caucasian population with co-occurring risk factors. Thus, understanding geographical distribution of HBV infection by ethnicity could inform screening and care strategies.
Objectives and ApproachWe identified geographic clusters of HBV infection in British Columbia by ethnicity during the years 1990-2015 using the BC Hepatitis Testers Cohort (BC-HTC). The BC-HTC includes ~1.7 million individuals tested for HCV or HIV at the BC Public Health Laboratory or reported as a case of HCV, HIV, or HBV linked to healthcare administrative databases. We plotted maps of HBV diagnoses (acute and chronic) rate at the Dissemination Area level between 1990-2015 stratified by ethnicity and compared this distribution with injection drug use (IDU) distribution in BC.
ResultsThe distribution of HBV varied considerably by ethnicity. From 1990 to 2015, a higher rate of HBV infection was found among East Asians and Caucasians followed by South Asians and other ethnicities. East Asians with highest rates were mainly concentrated in Vancouver city, Burnaby and Richmond (Metro Vancouver) while South Asians with highest rates were mostly concentrated in urban areas in Surrey and Abbotsford. Caucasians with higher rates were clustered in Downtown Eastside in Vancouver, Surrey and Abbotsford (Metro Vancouver) and urban areas in Greater Victoria (Vancouver Island), Prince George (Northern BC) and Kamloops (Interior BC). The distribution of IDU closely followed the distribution of HBV among Caucasians but did not align with other ethnic groups.
Conclusion/ImplicationsResults highlight distinct areas of HBV infection clustering by ethnicity, which differ from areas with high IDU distribution except in Caucasians. Findings support ethnicity-based HBV screening/prevention and care services to areas with immigrants from HBV-endemic countries and integrated HBV and harm reduction services for early diagnosis and treatment in Caucasians.
Visualising linked data using GIS: Western Australian Child Development Atlas
Authors: Sims Scott 
Published: September 2018

IntroductionWhere people live can strongly influence their level of exposure to health-damaging factors, their vulnerability to poor outcomes, and the consequences of experiencing those poor outcomes. Geographical mapping of child outcomes is therefore an important component of monitoring the health and wellbeing of children and young people.
Objectives and ApproachThe Western Australian Child Development Atlas (CDA) maps population-level administrative, census, registry, and survey data, aggregated at various levels of geography. The project utilises geographic information system (GIS) technologies to identify and investigate spatial patterns in key health, social, learning and development indicators, overlaid by service locations.
The CDA is an online, interactive platform that provides policy developers, service providers, communities, and government with access to quality and easily comprehendible spatial data on the outcomes of children and young people. This information helps to identify geographic areas of highest need and priority, and patterns in service distribution relative to need.
ResultsThe CDA maps linked and non-linked de-identified aggregated data on children and young people (0-18 years) born or residing in Western Australia from 1990 until 2016. Key indicators from health, social, education, and welfare datasets are mapped at various levels of geography.
Using a case example of a WA community, we demonstrate how the CDA can enable policy-makers, service providers, and researchers to better identify priority areas for improved child health and wellbeing, and implement place-based approaches to service delivery.
Conclusion/ImplicationsMapping population data on children’s health and wellbeing can help identify areas of highest need and priority, and facilitate a targeted focus for service delivery within jurisdictional areas, including rural and remote regions. Outcomes can be monitored over time, enabling evaluation of the effectiveness of changes to service and policy.
Through the legal maze: An Act Respecting Research
Authors: McDonald Ted , MacKenzie Patricia , Barry Krista 
Published: September 2018

IntroductionThe New Brunswick Institute for Research, Data and Training (NB-IRDT) is a recently established provincial research data centre and data custodian hosting anonymized linkable administrative data from the Government of New Brunswick (GNB) and other public bodies. GNB has committed to transferring research-relevant data from across GNB operations to NB-IRDT.
Objectives and Approach
Although NB-IRDT had received a small number of administrative data sets from the GNB Department of Health as of the end of 2016, transfers of other datasets from the Department of Health, Department of Social Development and other Departments was halted because of a series of legal opinions citing a lack of legislative authority to do so. This presentation details an innovative and transformative approach that overcame these obstacles to facilitate continued data sharing with NB-IRDT not just from those Departments but from across the spectrum of government operations.
ResultsPassed in the NB Legislature in March 2017 and proclaimed in May 2017, An Act Respecting Research modified 12 different pieces of existing legislation to define a clear legal authority through which pseudo-anonymized data from all of the Provincial Government plus numerous other public bodies could be transferred to NB-IRDT in linkable form. This included Acts as disparate as the Education Act, Mental Health Act, the Nursing Homes Act, the New Brunswick Housing Act, etc. An Act Respecting Research was the culmination of more than a year of collaborative effort between NB-IRDT and the Executive Council Office plus 14 different provincial line departments. The Act also permits the collection of the Medicare health insurance numbers by departments and public for data matching and transfer purposes.
Conclusion/ImplicationsThe Act Respecting Research is unique in Canada and would not have occurred without GNB’s commitment to the principle and practice of evidence-based policymaking. After the Act’s passage, NB-IRDT has received numerous datasets and work is ongoing on many more, from postsecondary education to road accidents and workers compensation claims.
How do socio-demographic differences in administrative records affect the quality (accuracy) of data linkage?
Authors: Randall Sean , Ferrante Anna , Boyd James , Brown Adrian 
Published: September 2018

IntroductionRecord linkage is inherently uncertain, with all linkages containing some amount of false positive and false negative errors. Previous results have suggested that linkage error may not be evenly distributed throughout the population, with particular subgroups exhibiting higher rates of linkage error. Record linkage is inherently uncertain, with all linkages containing
Objectives and ApproachThis study investigated the distribution of linkage error using four large-scale Australian administrative datasets; hospital admissions datasets from Western Australia and New South Wales, and emergency presentation datasets from New South Wales and South Australia. Each dataset had been previously de-duplicated to a very high standard, with large scale manual review taking place; these results were used as our truth set.
Each dataset was linked using probabilistic record linkage with results (precision and recall) compared by gender, age, geographic indices of remoteness and socioeconomic status.
ResultsResults were highly dataset dependent. Consistent findings were lower linkage quality found for individuals living in remote locations, and lower linkage quality in those in the youngest category (those born after 1980). Some datasets showed lower linkage quality for females, for those in middle age as compared to the elderly, and for those with lower socioeconomic status. The differences in linkage quality found were typically small. Changes in threshold settings had generally no effect on the relationship between sociodemographic characteristics and linkage quality.
Conclusion/ImplicationsLinkage studies focussing on younger individuals and those in remote areas may have greater uncertainty regarding their results. Targeting efforts by linkage units may be required to ensure even distribution of linkage errors. Further research is required into investigating how linkage errors effect research outcomes.
Cecilia: an R package to automate data cleaning of administrative datasets
Authors: Garcia Alexandre Franco , Palfy Miro , Vasquez Stacy Ann 
Published: September 2018

IntroductionData linkage has considerable potential to improve health and society. Linking vast and detailed information across multiple administrative population-scaled data sources enhances the quality of existing data, empowers population health research, and produces objective evidence to inform policy decisions. In this context, data cleaning is crucial to minimise linkage errors.
Objectives and ApproachAs dealing with the heterogeneity of administrative datasets is an acknowledged time-consuming task, the objective is creating a public and open-source R package to automate and report steps of data cleaning in a reproducible fashion.
The package automatically assesses variables and reports relevant information and issues for linkage purposes, then cleans the dataset based on problems found, reassesses the variables and reports results again.
It has a default cleaning procedure based on years of accumulated linkage knowledge and an interactive exploratory session to check variables individually. The report also includes all settings from both default and interactive session.
ResultsThe package accurately detected, cleaned and reported potential linkage problems in variables such as names, addresses and dates in so far 15 actual populational datasets from multiple sources, with a diverse range of format, content, and inconsistencies. The entire process took minutes rather than hours.
The reports correctly gathered, organised and presented all relevant information for linkage, in all distinct sections of the hyperlinked document, such as those related to the dataset, individual variables or settings used for cleaning. The different types of information included text, figures, data dictionaries, and frequency tables of detected issues, such as non-alphanumeric characters, annotation terms or suffixes and prefixes.
The output datasets had all evaluated variables with cleaned data plus extra columns containing only issues themselves or problematic records.
Conclusion/ImplicationsThe package accelerates the data cleaning of linkage variables, automating time-consuming steps, providing pertinent information for linkage as well as cleaned datasets. The complete process is time-efficient and reproducible.
As the output dataset contains variables with cleaned data and detected issues, it allows assessment of the level of cleaning performed.
Diabetic Ketoacidosis (DKA) at Diabetes Diagnosis in Children (0-18 years) in Ontario, Canada: A Population-Based Retrospective Cohort Study of Health Administrative Data
Authors: Mian Oxana , Wenghofer Elizabeth , Young Nancy , Jaakkimainen Liisa 
Published: September 2018

IntroductionDiabetic ketoacidosis (DKA), a life-threatening complication of diabetes, is the leading cause of mortality and disability in children. The high cost of care for DKA and negative consequences of DKA on child’s health are known; however, the pediatric DKA trends in Ontario were not studied in the last fifteen years.
Objectives and ApproachTo examine time trends of the prevalence of pediatric DKA at diabetes diagnosis and socio-demographic characteristics associated with the DKA risk (child’s sex; age; geographic region; rural residence; material deprivation and ethnic concentration) in Ontario, Canada. Person-level de-identified data from the provincial registry for all Ontario children (0-18 years) newly diagnosed with diabetes (type 1 and 2) in 2004-2012 was linked with records in the national discharge abstract database (CIHI DAD) at the Institute for Clinical and Evaluative Sciences (ICES). SAS software (v.9.3) was used for Poisson and logistic regressions.
ResultsOf 10,617 children diagnosed with diabetes, 15.5% were diagnosed during a hospital admission for DKA. Prevalence of DKA at diagnosis did not change over the nine-year period (ptrend =0.99). There were statistically significant within-province regional differences in DKA prevalence at diabetes diagnosis, with the highest prevalence in South-Western Ontario (17.2%). Younger children (0-6 years and 7-12 years) were at higher DKA risk than 13-18 years old children (adjusted odds ratio (OR) 2.5 95% CI 2.2-2.8 and 2.1 95% CI 1.9-2.4). DKA at diabetes diagnosis was associated with material deprivation in young children (0-6 years)(OR 1.9 95% CI 1.4-2.5 for “most deprived” versus “least deprived”). In the older group (13-18 years), boys were at higher DKA risk than girls (OR 1.4 95% CI 1.1-1.7).
Conclusion/ImplicationsPrevalence of pediatric DKA at diabetes diagnosis in Ontario is among the lowest in the world; however, higher DKA prevalence among children residing in some geographic regions or most deprived neighbourhoods of the province despite the universal access to government-funded health care warrants further research.
Reducing bias in multivariate analyses due to the modifiable areal unit problem.
Authors: Tuson Matthew , Kok Mei Ruu , Yap Matthew , Vickery Alistair , Boruff Bryan , Murray Kevin , Turlach Berwin , Whyatt David 
Published: September 2018

IntroductionThe Modifiable Areal Unit Problem (MAUP) arises from the aggregation of data organized by spatially defined boundaries. Aggregated values are influenced by the shape (zone effect) and scale of the aggregated units. Aggregations of the same data using different zones or scales can give different analytical results, none reliable.
Objectives and ApproachUsing population-level administrative health data in Western Australia, the objectives were to: accurately measure the association between health service utilization and demographic, socio-economic, and service accessibility variables; and develop models to accurately forecast areas of high health service utilization into the future.
Multiple zone designs and aggregation scales were used to examine the impact of MAUP in association studies. These zone designs and scales were then used in all-subset model selection processes, combined with repeated k-fold cross-validation, to generate forecast maps of areas having high future rates of health service utilization.
ResultsThe impact of the MAUP and methods to reduce this bias in association studies will be presented, for both simple and complex model designs. Maps indicating gradients of predicted probabilities of high rate of health service demand in the future can be used to optimize the placement of services, through the use of catchment areas based on road-network travel distance and population distributions.
Conclusion/ImplicationsThe impact of the MAUP on the analysis of spatially-aggregated data has been considered intractable. However, methods to reduce the impact of the MAUP can improve policy and planning decisions based on such studies.