bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lessons learned: Linking patient-reported outcomes data with administrative databases
Authors: Davis Laura , Mahar Alyson , Bubis Lev , Li Qing , Zhao Haoyu , Moody Lesley , Sutradhar Rinku , Barbera Lisa , Coburn Natalie 
Published: September 2018

IntroductionSince 2007, Cancer Care Ontario (CCO) has systematically collected patient-reported outcomes (PROs) in the form of symptom data, for cancer outpatients visiting regional cancer centres or affiliate institutions. Data are used in real-time to facilitate conversation between clinicians and patients and have recently been combined with provincial administrative databases.
Objectives and ApproachCCO collects PROs using the Edmonton Symptom Assessment System (ESAS), which scores 9 symptoms on a scale of 0 (no symptoms) to 10 (worst symptom severity). Data were imported from CCO in 2015 and linked to a cancer cohort at ICES. We investigated differences between patients who completed $\geq$1 ESAS record and patients who did not, as well as the number of records, timing of data collection and missingness. We describe our experience linking and using the PRO data to administrative data, including presenting trajectories of symptoms over time and combining scores into composite indices.
Results120,745 cancer patients had 729,861 symptom records between 2007 and 2014. Not all patients with a cancer diagnosis had $\geq$1 ESAS record and this varied by patient, disease and system level factors. Because implementation occurred from a clinical perspective, data collection was irregular within and across patients and depended on treatment and other factors; the number of records per patient varied, as well the number of contributing patients in each time period following diagnosis. Attempts were made to create meaningful composite indices by combining all symptom scores as well as combining multiple high scores for each individual symptom. As a result, selecting the best statistical analysis to use these PRO data as an exposure or outcome is still uncertain.
Conclusion/ImplicationsPRO data linked to provincial, administrative data holdings represent a new frontier for population-based cancer research, both in their challenging structure as well as their implications for clinical practice and health system. These lessons learned will hopefully support other researchers rigorous use of these data in the future.
Establishing the Occupational Disease Surveillance System (ODSS) for Ontario: a linkage of administrative data
Authors: MacLeod Jill , Logar-Henderson Chloe , McLeod Chris , Peter Alice , Demers Paul A 
Published: September 2018

IntroductionWorkplace conditions and exposures are important determinants of health. However, identifying and monitoring population-level trends in work-related disease is challenged by existing data limitations. Administrative health databases capture timely and accurate information about disease diagnoses among the Ontario population, but these data do not include work history.
Objectives and ApproachThe Occupational Disease Surveillance System (ODSS), launched in 2017, captures and reports trends in work-related disease in Ontario. A cohort of 2+ million workers was identified from compensation claims (1983-2014). Records were linked through probabilistic and deterministic methods to the Registered Persons Database (1990-2015), and administrative health databases including the Ontario Cancer Registry (1964-2016), hospitalization (2006-2016), ambulatory care (2006-2016) and provincial health insurance plan billing data (1999-2016). Preliminary applications of ODSS have examined risks of 28 cancer sites and 11 non-cancer health conditions. Risks are estimated with Cox proportional hazards models for thousands of industry and occupation groups.
ResultsLinkage of existing administrative databases is an efficient approach for examining risk factors for work-related disease at the population level. ODSS can identify groups of workers by industry or occupation that are at increased risk of disease due to known or suspected workplace conditions and risk factors. For example, ODSS detected elevated risk of lung cancer among known at-risk workers employed in mining and quarrying (HR 1.47, 95% CI 1.33-1.61), transport equipment operating (HR 1.39, 95% CI 1.34-1.44), and construction (HR 1.09, 95% CI 1.06-1.13). Exploratory analyses can also detect previously unknown associations between work-related risk factors and disease. For example, although dermatitis and asthma are common occupational diseases, many causative exposures remain unclear. ODSS is currently being used to further explore potential risk factors.
Conclusion/ImplicationsTimely information about work-related disease is crucial to support prevention initiatives to protect workers. This novel linkage identifies existing and emerging trends in occupational disease in Ontario. By capturing work-related risk factors, ODSS serves as a model for other provinces to overcome existing gaps in disease surveillance.
A Population-Based Examination of Benzodiazepine Receptor Agonist & Z-drug Dispensations in the Alberta Population: Prevalence of Use and Indicators of Potentially Inappropriate Use and Prescribing
Authors: Weir Daniala , Samanani Salim , Niruban Selvanayagam John , Jess Ed , Gilani Fizza , Eurich Dean 
Published: September 2018

IntroductionCanadian clinical practice guidelines for the management of anxiety disorders and insomnia suggest that benzodiazepine receptor agonist and Z-drug (BZRA) treatment is appropriate for short term use in adults (aged 20-64); use of BZRA’s outside of these recommendations is considered “potentially inappropriate” given the increased potential for adverse effects.
Objectives and ApproachThe objective of this study was to characterize the use of BZRA’s for all individuals 10 years of age or older in the province of Alberta in 2015 and to explore indicators of potentially inappropriate prescribing and use. Using data from Alberta’s Pharmaceutical Information Network (PIN) as well as Vital Statistics, prevalence of BZRA use was determined as were characteristics of BZRA dispensations according to prescriber type, days supply and number of defined daily doses. Patient-level indicators of potentially inappropriate BZRA prescribing or use (days of consecutive use, use of multiple ingredients etc.) were calculated by age category and sex.
ResultsOverall, 372,870 individuals received 2,463,585 BZRA dispensations in 2015 (10% of Alberta population). The majority of users were female (63%), 28% were 65 years of age or older and 20% of individuals were dispensed both z-drugs and benzodiazepines. BZRA users received a median of 3 dispensations (IQR: 1-6), for a total of 91 days (IQR: 30-261) of use in the year, with a median period of consecutive use around 51 days (IQR: 21-128). 26\% of individuals utilized these medications for 121 consecutive days or more increasing to 40% in those 65 + years of age. 17% of individuals utilized more than one distinct BZRA ingredient concurrently while those in the 20-49 year age group were most likely to visit multiple prescribers.
Conclusion/ImplicationsPrevalence of BZRA use in Alberta is high, especially among females and the elderly. This study supports the need for patient and physician education, decision support for prescribers and continued monitoring of medication dispensing at the population level within the province of Alberta
Patterns of opioid utilization in the 90-days post hospital discharge and risk of re-admissions and emergency department visits
Authors: Kurteva Siyana , Weir Daniala , Schmidt Alexandra , Lee Todd , Tamblyn Robyn 
Published: September 2018

IntroductionOver the past 20 years, increases in opioid prescribing rates and average prescription volumes have been documented in both the United States and Canada. As prescription opioid use and overdose has steadily increased in North America, a dramatic rise in hospitalizations resulting from opioid poisonings has also been witnessed.
Objectives and ApproachTo describe opioid utilization patterns for patients admitted to a tertiary care hospital in Montreal between 2014 and 2016, and to estimate the association between opioid use and risk of adverse health outcomes in the 90-days post discharge. Opioid utilization was measured using medication dispensing data from the provincial healthcare databases (RAMQ) while hospital/ED visits were obtained from RAMQ medical services. Patient characteristics and discharge prescriptions were obtained from the hospital chart. Time-varying utilization of opioids was modeled as: 1) current use, 2) cumulative duration of past use, and 3) duration of use of past 10 days, using Cox models.
ResultsOf the 3,308 included patients mean age was 70 (SD 14), 57% were male and 47% were discharged from surgical units. 856 (26%) patients had a history of opioid use in the 1-year prior to admission, 1528 (46%) were prescribed an opioid at discharge and 1481 (45%) filled an opioid in the 90-days post discharge. Among patients prescribed an opioid at discharge, 79\% filled their prescription post discharge, where opioid naïve patients were less likely to fill their prescriptions compared to those with a history of opioid use (40% vs 81%). Our multivariable Cox models suggested that cumulative duration of opioid exposure in the past 10 days was associated with a 10% increased risk of ED visits and hospitalizations.
Conclusion/ImplicationsPatients with a history of opioid use were more likely to both receive an opioid prescription at hospital discharge and fill their prescription post-discharge. Our findings suggest that longer-term utilization patterns of these medications after hospitalization may increase risk of re-admissions and ED visits.
Whose Benefit is it Anyway? Public Expectations of Public Benefits from Health Informatics Research
Authors: Aitken Mhairi , Porteous Carol , Creamer Emily 
Published: September 2018

IntroductionPublic benefits serve as a key justification for secondary uses of data in health research. Yet, while it is known that assurances of public benefits are vital for ongoing public support, there is a lack of clarity regarding what constitutes a public benefit.
Objectives and ApproachOur research aimed to understand public expectations regarding “public benefits” from health informatics research.
In May and June 2017 three full-day deliberative workshops were held with members of the public across Scotland (between 20 and 25 participants per workshop). Participants were selected through quota sampling to represent key demographic characteristics. Workshops were structured around brief introductory presentations, videos in which data scientists presented the ways in which their research was anticipated to lead to public benefits, small group discussions, plenary discussions and activities.
ResultsWorkshop participants generally conceptualised “the public” as being “everyone” and had an inclusive view of this often taking an international or global perspective. This led to preferences for research to benefit the widest possible public, however participants also discussed the potential for benefits to be focussed at vulnerable or disadvantaged groups. There were wide-ranging views on what would constitute a “benefit” and participants generally acknowledged the value in keeping this loosely defined. A key theme to emerge was that, while some research might benefit particular individuals or groups more than others, the benefits should be societal benefits. In discussing the various data science projects presented, it was considered important that research had mechanisms in place in order to realise the benefits it was aiming for.
Conclusion/ImplicationsPerceived public benefits are crucial for maintaining public support for health informatics research. Our workshops found that there is value in keeping "public benefits" as a broad, loosely defined term. Nevertheless, across each of the workshops the discussions suggested that research should benefit society as widely and fully as possible.
Development of an automated system for clinical study recruitment
Authors: Youngson Erik , Bakal Jeff , Curic Tihomir , Adewuyi Esther Ekpe , Pannu Neesh 
Published: September 2018

IntroductionThe AFTER AKI study was developed to evaluate implementation of a clinical decision support initiative for acute kidney injury patients. Recruitment relies on staff observing changes in serum creatinine, discussing the study with patients, and then alerting study personnel for consenting patients – a process that misses many eligible patients.
Objectives and ApproachTo improve the efficiency of patient recruitment, we sought to develop an automated system to alert nurses on participating wards when patients on their wards met the study criteria with minimal risk of a data breech. To accomplish this, data from several databases were linked:

Calgary Laboratory Services (CLS; a subsidiary of Alberta Health Services (AHS))

Data refreshed daily to capture serum creatinine labs

AHS Analytics Data Warehouse

Admission/Discharge/Transfer (ADT) data to determine patient location in hospital on previous day
Discharge Abstract Database (DAD) to exclude patients with prior renal transplant

ResultsThe data were linked using the following process:

Daily procedure scheduled to flag patients who met the lab criteria on the previous day using CLS laboratory data.
The identified patients were located by hospital and ward using ADT data, and to exclude patients with a prior renal transplant. Only non-transplant patients located one of the study wards were retained.
Cumulative patient list updated with new patients and dates.
Tableau report created and securely released to ward clerk to enable clerk to view new patients each day for their assigned wards and discuss study with them as an impartial third party.
Consenting patients can then be approached by study personnel to discuss in more detail.

Conclusion/ImplicationsA system was successfully created to enable an automated process for patient identification in a clinical trial. Patient privacy was protected by applying user-level security when disseminating reports to ensure that only health care providers within a patient’s ‘circle of care’ had access to necessary information.
Linkage of Chronic Disease Data from Provincial Sources for Strategic Decision Support and Population Health Surveillance in British Columbia (BC)
Authors: Rasali Drona , Djurdjev Ognjenka , Li Crystal , Ord Edward , Laberge Carol , Woods Ryan , John Spinelli , Ding Lilian , Ye Xibiao , Smolina Kate , Almeida Alberto 
Published: September 2018

IntroductionBC Ministry of Health (MoH)’s health administrative data holdings for a variety of general health care data are not readily linked with various data registries maintained by specialized care agencies of the Provincial Health Services Authority (PHSA). These provincial data sources have rich chronic disease information for BC residents.
Objectives and ApproachThe objective of this project is to develop a system for cross-agency linkage of provincial level chronic disease data to improve chronic disease information that would support the BC’s health system, MoH and PHSA agencies in particular, in healthcare delivery and chronic disease prevention planning. We aim to achieve linkage of data from various provincial chronic disease data sources of the MoH and PHSA, with further potential to link with variety of other external databases such as Census data for socio-economic determinants of health. We are reporting here the outcome of the first phase of this project.
ResultsThe outcomes from the project to date were as follows: Data linkage between the MoH’s administrative databases, Chronic Disease Registries (CDRs) in particular and Census based socio-economic status (SES) data was achieved, providing the population level evidence of health outcomes such as health inequity, comorbidities and multimorbidities (sub-project # 1). Preliminary results on data quality and health outcomes by SES will be presented. This was followed by completion of securing approval to ensure data security compliance for data linkages of CDRs with the Provincial Renal Agency’s Registry called “PROMIS” (sub-project # 2), Cardiac Services BC’s Registry called “HEARTis” ((sub-project # 3), and BC Cancer Agency’s Registry and BC Generations Project data (sub-project # 4), for implementation to answer agency specific research questions.
Conclusion/ImplicationsThis data linkage project to consolidate information from chronic disease and socio-economic databases for providing answers to various analytic questions posed will improve decision support and enhanced population health surveillance. The lessons learned from this multi-agency collaboration and their implications for other jurisdictions will be addressed.
Evaluating the Manitoba Infant Feeding Database: Linking an infant feeding data repository with total population administrative data
Authors: Paul Julia , Chateau Joanne , Green Chris , Warda Lynne , Katz Alan , Nickel Nathan 
Published: September 2018

IntroductionBreastfeeding during the first two years of life supports optimal maternal and child health. Few linkable databases capture infant feeding data beyond hospital discharge. The Manitoba Infant Feeding Database (MIFD), a novel initiative started in 2015, records infant feeding practices at vaccination visits and is linkable with administrative data.
Objectives and ApproachOur objective was to evaluate the data quality and staff experiences with implementing the MIFD. The MIFD records whether the infant was exclusively or partially breastfeeding at each visit; it also records the infant’s age when (a) something other than human milk was first introduced and (b) the infant stopped breastfeeding entirely. Personal Health Identification Numbers (PHINs), birthdate, and postal code are used to link infant feeding information with administrative health records. Two authors independently reviewed the proportion of complete data fields and data fields with potential transcription errors. A survey was developed to assess experiences with implementing the MIFD.
ResultsA total of 950 (out of 2500) records were randomly selected and reviewed, equating to 13,258 data fields. Data were 98.5% complete (n=13,064/13,258). Baby’s PHIN, mother’s PHIN, and relationship to the baby had 95.4%, 96.0%, and 97.6% complete data, respectively. Almost all records (95.5\%) had complete data for personal identifiers. Transcription had to be verified in 13.5% of MIFD data fields. The survey response rate was 78.4%. Nearly all felt that the MIFD data collection tool was easy to use (96.6\%). 65\% felt faxing the data to a central office was convenient. Most (93.1%) of respondents were happy to continue with the MIFD system. A 0.7 FTE is required to verify feeding data from all births in the province (N=15,000).
Conclusion/ImplicationsThe MIFD is a sustainable and viable system for collecting and storing infant feeding information which can be linked with administrative health and social data. Having breastfeeding information after hospital discharge which can be linked with administrative data will facilitate the evaluation of programs aimed at supporting breastfeeding.
Prenatal exposure to the 2009 pandemic H1N1 influenza vaccine on health outcomes in children
Authors: Donelle Jessy , Walsh Laura , Wilson Kumanan , Kwong Jeff , Hawken Steven , Fell Deshayne 
Published: September 2018

IntroductionDuring the 2009 H1N1 pandemic, less than half of pregnant women in Ontario received the recommended influenza vaccine. Commonly-cited reasons for low vaccine uptake include misconceptions about the possible impact of maternal influenza infection and vaccine safety. Providing data on previously understudied pediatric health outcomes may help increase vaccine uptake.
Objectives and ApproachWe conducted a retrospective cohort study of all live births from November 2nd, 2009 to October 31st, 2010 using the BORN Ontario province-wide birth registry containing information on H1N1 vaccination. These data were deterministically/probabilistically linked with several health administrative databases held at the Institute for Clinical Evaluative Sciences to ascertain specific immune-related pediatric health outcomes and health services utilization over 5 years of follow-up. Negative binomial regression models were used to evaluate the association between prenatal H1N1 vaccination and outcomes. Stabilized inverse probability of treatment weights (sIPTW) derived from the propensity scores were used to adjust for potential confounding.
ResultsThe study cohort included 104,310 eligible infants, 31,310 (30%) of whom were born to H1N1-vaccinated women. Median follow-up time was 5 years. Using sIPTWs we were able to achieve good balance of baseline measured covariates across exposure groups, with no absolute standardized differences larger than 7%. The sIPTW-adjusted analyses indicated no significant associations between prenatal exposure to H1N1 vaccination and upper respiratory infections (adjusted rate ratio [aRR] 1.01; 95% confidence interval [CI] 0.98-1.03), lower respiratory infections (aRR 1.00; 95%CI 0.96-1.04), otitis media (aRR 1.04; 95%CI 1.00-1.07), all infections (aRR 1.00; 95%CI 0.98-1.03), and rates of urgent and in-patient health services utilization (aRR 1.00; 95%CI 0.98-1.02).
Conclusion/ImplicationsOur primary findings suggest there are no associations between prenatal exposure to H1N1 vaccination and (1) the development of several immune-related health outcomes in children; (2) rates of health services utilization. Furthermore, our study provides new evidence on the long-term safety of influenza vaccination during pregnancy, which is currently lacking.
Validating the accuracy of place of death in Vital Statistics of Calgary Zone residents in 2015
Authors: Fong Andrew , Cai Pin , Sinnarajah Aynharan 
Published: September 2018

IntroductionAdministrators and researchers of community and hospital based palliative care services have relied on Vital Statistics for place of death information without knowing the full extent of its accuracy. We sought to understand and document whether Vital Statistics’ place of death is confirmed by other data sources.
Objectives and ApproachUnderstand the degree of confirmation of Vital Statistics' place of death with multiple data sources using a first found cascading method. The following order of cascading data sources were used: Discharge Abstract Data (DAD), National Ambulatory Care Reporting System (NACRS), Strata Health Pathways (hospice), ACCIS (LTC), and PARIS (supportive living). Hospital deaths were first confirmed using DAD. If not found, we searched the next data source NACRS (Emergency Department) followed by hospice, long term care, and supportive living. If the death was not found in these five sources, death was classified as 'Other" and the residency of home was inferred.
ResultsOf 7,176 deaths recorded in Vital Statistics (VS), 4,749 were confirmed of which 78% (N=2580) of VS hospital deaths, 96.2 (N=1179) of VS home deaths, and 60.3% (N=990) of VS Nursing Home deaths were confirmed. Inferred nursing home death were recorded as Hospital (N=147), Auxiliary Hospitals (N=61), and Other (N=41). Inferred home deaths were classified as Other (256), Hospital (84), Nursing Home (33), Unknown (7), and En Route (7). Inferred en route or emergency department death were classified as hospital (360). Supportive living deaths, not a category in Vital Statistics, were classified as Other (N=81), Nursing Home (N=51), At Home (N=33). Hospice death, no longer a category in Vital Statistics since 2012, were classified as Nursing Home (N=563), Hospital (N=152), Auxiliary Hospital (N=168), Other (N=334).
Conclusion/Implications66% of 7,176 Deaths in Vital Statistics were confirmed by other data sources. Using multiple data sources, hospital deaths would decrease by 22%, confirmed nursing home death would increase by 25%, and hospice deaths would no longer be misclassified into hospital (N=152), Aux. Hospital (N=168), and Other (N=334).