bluuebunny/crossref_metadata_2025 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).
International meta-analysis of 684,660 men with vasectomies: a study utilising the International Population Data Linkage Network
Authors: Randall Sean , Boyd James , Fuller Emma , Brooks Caroline , Morris Carole , Earle Craig , Ferrante Anna , Moorin Rachael , Semmens James , Holman D'Arcy
Published: September 2018

IntroductionEvidence on the effect of vasectomy and vasectomy reversal on risk of prostate cancer is conflicting, with the issue of detection bias a key criticism. In this study we examined the effect of vasectomy reversal on prostate cancer risk in a cohort of vasectomised men.

Objectives and ApproachA proof of concept study involving the International Population Data Linkage Network which pooled aggregated result data from participating centres in Australia, Canada and the United Kingdom. De-identified linked data extractions took place at each centre. Each participating centre locally conducted Cox proportional hazards regression analysis compared the risk of prostate cancer in those with/without vasectomy reversal in a cohort of vasectomised men. These results were then combined in a meta-analysis. Evidence of a protective effect of vasectomy reversal would suggest the harmful effect of vasectomy on prostate cancer risk, while nullifying detection bias.

ResultsData were received from Australia (the states of Western Australia and New South Wales), Canada (the province of Ontario), Wales and Scotland. In total, there were 9,754 men with vasectomy reversals, and 684,660 men with a vasectomy.

The combined analysis showed no protective effect of vasectomy reversal on incidence of prostate cancer when compared to those who had vasectomy alone (HR, 95%CI: 0.92, 0.70-1.21). As such, the results align with previous studies which found little or no evidence of a link between vasectomy and prostate cancer.

Conclusion/ImplicationsThe study, originally conceived at the first IPDLN meeting in London, found no obvious protective effect of vasectomy reversal on prostate cancer in vasectomised men. The project demonstrated the utility and feasibility of collaborative studies fostered through the IPDLN, despite methodological challenges faced when aggregating international data.
Making the link: using long-term cancer survival measures linked with acute care data to plan health services.
Authors: Wright Cameron , Moorin Rachael
Published: September 2018

IntroductionLong-term cancer survival measures have become more important as management has improved outcomes. However, we know some cancer-related acute care use persists into the long-term. This project sought to integrate cancer survival analysis and health services research, validating our modelling retrospectively, for prospective application for health service planning.

Objectives and ApproachWe used linked Western Australian cancer registry, mortality and hospitalisation data. Flexible parametric models for first-time invasive cancer diagnoses between 1/1/1997 and 31/12/2006 were used to extract marginal estimates of the cure proportion –that expected not to experience excess mortality from cancer - for those diagnosed in 1999. Fine and Gray competing risks regression was used to estimate the proportion who had died of non-cancer causes. The expected number of individuals diagnosed in 1999 still alive in 2011 was multiplied by the mean expected rate of cancer-related hospitalisations 12 years post-diagnosis (modelled for individuals diagnosed between 1/1/1997 and 31/12/2011).

ResultsCure modelling was appropriate for colorectal cancer (CRC) and melanoma. CRC cure proportions were 0.58 for ≤ 50 years at diagnosis, 0.61 for 51 – 70 years, and 0.49 for ≥71 years. For melanoma, corresponding proportions were 0.94, 0.91 and 0.83. The expected number still alive in 2011 was similar to the actual observed in the linked data for the youngest age group, with an over-estimate for older groups. The actual age-standardised, cancer-related hospitalisations in 2011 for those diagnosed with CRC or melanoma in 1999, was within the lower and upper limits of the expected number for all except melanoma diagnosed between 51 and 70 years of age. For this group, observed cancer-related hospitalisations were higher than the expected (355 versus 271 (203 – 341)).

Conclusion/ImplicationsCancer registry linked with health service use data can provide useful insights for health service planners. While a decline in cancer-related hospitalisations from diagnosis is expected, this study shows that some demand remains 12 years post-diagnosis. Further refinement of our approach will facilitate its utility in planning cancer-related acute care.
US Data Access and the Commission for Evidence-based Policymaking
Authors: O'Hara Amy
Published: September 2018

IntroductionIn September 2017, the bipartisan Commission for Evidence-based Policymaking released twenty-two recommendations to improve secure data access for evidence building activities involving population-level government files. Many of the files are siloed in government agencies. The commission deliberated over eighteen months to understand the risks and barriers to broader data use.

Objectives and ApproachI will describe the Commission’s charge and review its recommendations, in context of US laws and privacy debates. I will compare the report’s recommendations and implications to laws and initiatives in other countries. The report calls for the establishment of a National Secure Data Service (NSDS), which has the potential to transform the data sharing environment for federal agencies, policy makers, and researchers. The report suggests more extensive use of differential privacy and secure multiparty computation to protect privacy. I will describe how the current environment could change depending on how the recommendations are implemented.

ResultsThe Commission was established under one administration, but the recommendations were released under another. Despite the political and budget uncertainty in Washington, a bill was introduced and passed in the House in November 2017 to implement some recommendations. I will summarize the actions to be taken if the bill becomes law, including directives on learning agendas to prioritize and coordinate evidence-building activities across government, the roles of chief evaluation and chief data officers, and formation of an advisory committee to plan a NSDS. I will describe benefits that could follow from directives in the bill, including transparency about uses of administrative data, development of guidance to assess the risk when combining data sources, and minimization of the risk of publicly releasing de-identified data.

Conclusion/ImplicationsThe US may develop a national secure data service to support evaluations and policymaking. The recommendations are akin to the UK Data Service. Some recommendations are straightforward, others need years of planning and technical breakthroughs, and all require political buy-in and funding.
People with mild cognitive impairment are at increased risk of serious injury
Authors: Harvey Lara , Toson Barbara , Brodaty Henry , Draper Brian , Kochan Nicole , Sachdev Perminder , Mitchell Rebecca , Close Jacqueline
Published: September 2018

IntroductionData-linkage studies using administrative hospital data have shown that people with dementia have double the rate of injury-related hospitalisations and poorer health outcomes than those without. No previous research has explored whether people with mild cognitive impairment are also at increased risk of serious injury requiring hospitalisation.

Objectives and ApproachA major barrier to the use of administrative hospital data for undertaking research focusing on people with MCI is that MCI cannot be reliably identified from ICD-10 coded administrative data. To overcome this limitation, hospitalisation and death data was linked to data from participants (community-dwelling 70-90 year olds) enrolled in the population-based longitudinal Sydney Memory and Ageing Study (MAS). MAS participants underwent comprehensive neuropsychological assessments at baseline, then 2, 4 and 6 years’ follow-up to accurately determine cognitive status at each time-period. Linkage to hospital records allowed identification of injury-related hospitalisations and outcomes for the 2-year period following each assessment.

ResultsThere were 335 injury-related hospitalisations for the 867 participants; 222 (25.6%) participants had at least one injury-related hospitalisation. After adjusting for age-and-sex, participants in a state of MCI had 1.7 (95%CI 1.2-2.4) times higher odds of an injury-related hospitalisation than participants in a state of normal cognition, whilst participants with dementia had 2.3 (95%CI 1.2-4.4) times higher odds. There was no difference in odds between participants with MCI and dementia.

Of the 116 hospitalisations for people with MCI, the majority (79.3%) were due to falls. Non-fracture head injuries (25.9%), upper limb and trunk fractures (13.8% respectively) were the most common injury type. There were no differences in injury type, mean length of stay, or 30-day mortality between people with normal cognition, MCI and dementia.

Conclusion/ImplicationsOlder people with objectively defined MCI are at higher risk of injury, predominantly as a result of falls, than their cognitively intact peers. Falls-risk screening and fall prevention initiatives may be indicated for people with MCI. Further research is required to determine which cognitive domains contribute to this increased risk.
Evaluation of the Western Australian population based data linkage Intellectual Disability Exploring Answers (IDEA) surveillance system
Authors: Strobel Natalie , Bourke Jenny , Leonard Helen , Richardson Alice , Edmond Karen , McAullay Dan
Published: September 2018

IntroductionThe IDEA system is a population-based data linkage system for intellectual disability (ID), which combines data from two government departments. Due to recent policy changes the future of the IDEA system is unknown. Understanding the IDEA system's strengths and limitations will provide data custodians with the opportunity to re-design the system.

Objectives and ApproachAn evaluation of the IDEA surveillance system was undertaken to assess the quality, efficiency and usefulness of the system. The primary objectives were to evaluate systematically and objectively the attributes of the system and provide recommendations to data custodians and stakeholders to strengthen the surveillance system.

The evaluation was based on the methods from the 2001 U.S. Centers for Disease Control and Prevention guidelines on evaluation of public health surveillance systems. We assessed the following system attributes: usefulness, simplicity, flexibility, data quality, acceptability, representativeness, timeliness, and stability. This was completed by process observation, semi-structured interviews and data analysis.

ResultsOur results found the IDEA system was flexible, acceptable, representative, timely and stable. Given data linkage process and maintaining confidentiality the data linkage process was considered relatively simple. We compared individuals in the IDEA surveillance system to a sub-group of individuals, cerebral palsy with ID, to the mandatory reporting surveillance system WARDA-CP. There were 582 individuals identified in the WARDA-CP surveillance system as having cerebral palsy and ID. Of those identified 501 (86.1%) were also in the IDEA database and 81 (13.9%) were not. There were little differences in WARDA-CP cases that were not identified in the IDEA system between Indigenous status, sex and place of residence.

Conclusion/ImplicationsThe IDEA system has successfully been used to understand prevalence rates and inform resource allocation. Advocacy organisations could play an important role in the sustainability of the system. Additional variables or enhanced surveillance for functional capacity could strengthen the system and provide important information to inform policy and practice.
Leveraging best practices in data governance: An organization-wide data inventory and mapping project to support a five year data strategy
Authors: Travers Joseph , Campitelli Crystal , Light Richard , De Sa Eric , Stabile Julie , Iron Karey
Published: September 2018

IntroductionThe professional regulation sector is moving toward risk-informed approaches that require high quality data. A key component of a corporate 2017 Data Strategy is the implementation of a data inventory and mapping project to catalogue, centralize, document and govern data assets that support regulatory decisions, programs and operations.

Objectives and ApproachIn a data rich organization, the goals of the data inventory are to: enhance authoritative data that support programs; identify data duplications/gaps; identify data sources, owners and users; and, apply consistent data management and standards organizationally. Routinely used data assets outside the large enterprise workflow system (excel/word files; databases; paper collections) were catalogued. Using data governance principles and a facilitated questionnaire, departmental data stewards were interviewed about their generated data. Questions included data purpose/sources/types/formats/owners, retention rates, analytical products, gaps and visions for a desired data state. A data mapping methodology highlighted data set and variable connections within and across departments.

ResultsTo date, over 40 staff members in 10 departments were identified as data content experts. In addition to data in the corporate enterprise system, over 80 unique datasets were identified. In 1 large department, over 2,000 data elements across 26 datasets were inventoried. Data mapping analysis revealed thematic data domains, including member demographics, outcomes, certifications, tracking and financial data, collected and held in multiple formats ((Microsoft Access, Excel, Word), SPSS, PDF, e-mails and paper documents). While 72% of the data elements were formatted numerically, approximately 8% were free text. Significant data redundancies across staff members and departments were revealed, as well as unstandardized variable naming conventions. Gaps analysis highlighted need for standardized, electronic data, where not available and data management training.

Conclusion/ImplicationsCustomized data mapping reports to data users will facilitate the development of local, standardized departmental data hubs that will centrally link to a centralized data repository to facilitate seamless organization-wide analytics, improvements in current data management practices and greater data collaboration with the ultimate goal of supporting risk-informed approaches.
Improving the Measurement of Health System Performance across the Rural-Urban Continuum
Authors: Degani Naushaba , Gushue Sharon , Yurkiewich Alex , Buajitti Emmalin , Kumar Matthew , Campitelli Michael , Rosella Laura
Published: September 2018

IntroductionWe report on key performance indicators to highlight quality and variation in health care. Given Ontario’s diverse geography, we have prioritized improving measurement across the rural-urban continuum. This will improve our ability to discern the impact of geography on health care and health status to inform planning and decision making.

Objectives and ApproachBuilding on previous work to advance measurement of equity in health care, we struck a technical working group of experts to review methods for stratifying health system performance data by geographic location in the Ontario context. These methods were applied to a set of key performance indicators. The working group’s review of the results of this analysis will lead to recommendations for the best method to refine and standardize how geographic location is measured and stratified. This will improve our ability to discern the impact of geography on health system performance and health status for our suite of public-reporting products.

ResultsThe technical working group identified three methodologies for consideration that used linked postal code data: Population Centre (POPCTR), Statistical Area Classification (SAC) and a hybrid POPCTR/SAC methodology. These methods were tested against a set of key performance indicators across dimensions of quality including timeliness, effectiveness, population health and health outcomes. The results show that, in the health system performance dimensions of effectiveness and timeliness, as well as for a subset of health outcomes, there is variation in performance across the urban-rural continuum, though not always in a linear way. This may reflect differences in health care access, health risk factors, sociodemographic or socioeconomic characteristics across the urban-rural continuum. More definitive conclusions and recommendations will be available when the working group meets to review the results.

Conclusion/ImplicationsIdentifying a robust methodology for measuring performance across geographic locations will improve our ability to discern the impact of geography on health care including where geography may impact access and effectiveness of services as well as health outcomes. This information will enable better health system planning and decision-making.
Ensemble-based Classification Models for Predicting Post-Operative Mortality Risk in Coronary Artery Disease
Authors: Brobbey Anita , Faris Peter , Nettel-Aguirre Alberto , Williamson Tyler , Wiebe Samuel , Peng Mingkai , Quan Hude , Sajobi Tolulope T
Published: September 2018

IntroductionThere has been an increased demand for more accurate prediction tools to aid clinical decision-making regarding disease diagnosis prognosis for coronary artery disease(CAD) patients. Patients undergoing CABG surgery are older and a larger number have had previous heart surgery. Consequently, mortality after CABG is expected to increase despite procedural advances.

Objectives and ApproachThis study aims to compare the predictive performance of random forest(RF) and logistic regression(LR) classifiers for predicting 30-day and 1-year post-operative mortality risk in CAD patients who underwent CABG. Data was obtained by linking the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease(APPROACH) registry, a prospective longitudinal data of patients undergoing cardiac catheterization in Alberta, Canada, to vital statistics database. All patients who underwent first-time isolated CABG between January 1, 2007 and December 31, 2012 were included in the analysis. Area under the receiver operating curve(AUC) was used to compare the predictive performance of LR and RF regression.

ResultsOf the 4,908 eligible subjects who underwent isolated CABG during the study period, mortality estimates of 30-day and 1-year post CABG surgery were 1.59% and 3.85%, respectively. Descriptive analysis revealed that age, sex, hypertension, dialysis, cerebrovascular disease, chronic obstructive pulmonary disease, and chronic heart failure were associated with 30-day and 1-year mortality. The accuracy of the LR and RF regression classifiers in predicting 30-day mortality were 74.1, and 99.7%, respectively. While the accuracy of the former and latter classifiers in predicting 1-year post CABG mortality were 74% and 97.4%, respectively.

Conclusion/ImplicationsThis study shows that RF classifier results in better predictive accuracy than LR in predicting post-operating mortality risk in CAD patients. Machine learning models are potentially usefully for developing clinical prediction models that can be used to aid the monitoring of post-discharge outcomes in the management of cardiovascular diseases.
Can mental health related hospital visits be relied upon for suicide prevention?
Authors: Boctor Kerstina , Harder Douglas , Letwiniuk Liz , Marcoux Gene , Langhorst Kristi , Inch Roxanne , Lapointe Candace , Balbuena Lloyd
Published: September 2018

IntroductionEven among people with mental disorders, relatively few die of suicide. However, a large proportion of people dying from suicide have seen a physician in the year before death. This raises the question whether focusing on hospital visits for suicide-related outcomes is a viable suicide prevention strategy.

Objectives and ApproachOur objective was to examine whether a hospital visit for a mental disorder or prior suicide attempt preceded suicide death. We requested Saskatchewan’s provincial coroner for records of people dying of suicide in the Saskatoon Health Region catchment area for the years 2012 to 2016. The coroner’s list was linked with hospital and community mental health databases. Patient charts and medical abstracts in both settings were reviewed for risk factors.

ResultsThere were 143 suicide deaths in the time period and the yearly incidence was higher in Saskatoon as compared with the national average. Only 38 percent were seen previously in any Saskatoon hospital for a mental disorder (11 percent for a self-harm diagnosis). The chart review confirmed several known psychological and social risk factors. Having a history of depression or psychosis and alcohol and/or drug use were common. Many decedents also had disadvantaged socio-economic backgrounds characterized by vulnerable housing and being on social assistance.

Conclusion/ImplicationsWith only 38 percent of decedents being seen in hospital, community-based mental health care and data are important for suicide prevention. Suicide prevention efforts can be aided by facilitating the linkage of community and medical records to better track patients as they move between care settings.
Code List Library: A Solution to Improve Research Repeatability, Transparency, and Efficiency by Curating Lists of Clinical Codes
Authors: Thayer Daniel , Bown David , Leake Thomas , Jones Jonathan-Lee , Noyce Richard , Brooks Caroline , Ford David
Published: September 2018

IntroductionSets of clinical codes that define conditions and events of interest are a key knowledge product in health data research. Documenting such lists is essential for transparency and repeatability, and there is great potential benefit in their sharing and reuse. We designed and implemented software to address these goals.

Objectives and ApproachOur goals were threefold:

Provide a graphical user interface (GUI) to allow easier creation of code lists, for less technical users.

Allow clear documentation of code lists, preserving the history of their creation and capturing metadata about their meaning, provenance, and use.

Facilitate programmatic access, so that the software is not just documentation but can be integrated into data preparation and analysis.

To these ends, we developed a web application using Python and PostgreSQL that allows creating, editing, and accessing via a GUI, as well as a REST API for integration into SQL, R, and other environments.

ResultsThe software allows users to view and create lists through a familiar web paradigm. Lists can be built by identifying codes in a variety of ways, including keyword searches, regular expressions, and more complex rules. A change history is stored.

Information such as a description, whether there was clinical reviewed, and relevant publications is captured.

The REST API allows access and use in a variety of settings. We have implemented a DB2 SQL interface to enable code lists to be used within database queries, and other interfaces such as an R package are planned for the future.

It will be used within the SAIL Databank initially, with a public version for sharing across institutions planned. The code will be open source to enable further development.

Conclusion/ImplicationsWe expect this tool to facilitate faster, higher-quality, more reproducible research in Wales and beyond. Hopefully it will be not just a standalone effort, but one small piece in a set of better tools and methods that will enable our field to truly realized the benefit of large linked datasets.