bluuebunny/crossref_metadata_2025 huggingface dataset:
A Longitudinal Analysis of the Families First Screening Program in Manitoba, Canada: Cleaning, Validating and Linking via Health Registry Data
Authors: Au Wendy , Brownell Marni , Chartier Mariette , Santos Rob
Published: September 2018

IntroductionManitoba Public Health Nurses (PHNs) attempt to visit all families with newborns shortly after discharge from birth hospitalizations. Since 2000, PHNs have completed the Families First Screen (FFS) at these visits, to identify families at risk for child maltreatment. The information captured in FFS is a valuable tool for research.

Objectives and ApproachOur objective was to clean and validate FFS data and link to health data in the Manitoba repository in order to determine the percent of births in Manitoba hospitals that had FFS. We identified all babies born in Manitoba hospitals 2000-2015 using ICD-9-CM /ICD-10-CA codes. Mothers were identified through the Health Registry (Mom_Baby Link File) using scrambled Personal Health Identification Numbers (sPHINs). FFS data were linked to births via baby’s sPHIN. Determining which FFS records linked to babies required several steps of cleaning and validating the data to account for differences in birthdates between files, missing sPHINs, and multiple records.

ResultsFor example, in 2014 there were 16,079 births and 14,002 FFS records; 13,524 FFS had mother and/or baby sPHIN. For those missing baby sPHIN (9,295), 99.8% were retrieved via the Mom_Baby Link File. Linking the FFS to the hospital births we found: 3,043 births didn’t have an FFS; 12,762 had a single FFS, and 274 births had multiple FFS (i.e., baby associated with more than one mother, FFS and/or form date). To ensure that the baby was only associated with one mother and one FFS the most current FFS was kept. We found that in 2014, 81.07% (13,036/16,079) of the births had an FFS. In the longitudinal analysis, the percent of births with an FFS ranged from 74.6% in 2000 to 81.1% in 2014.

Conclusion/ImplicationsWe were able to achieve good linkage between FFS and health registry data, allowing this rich data source to be used for research on maternal and child health. Information on percent of births with FFS has been shared with policy-makers over the years and changes to screening practices implemented.
A Data Science Approach to Predictive Analytic Research and Knowledge Translation
Authors: Fisher Stacey , Talarico Robert , Sequeira Yulric , Bennett Carol , Spruin Sarah , Hsu Amy , Tanuseputro Peter , Manuel Doug
Published: September 2018

IntroductionCurrent approaches to the development and application of predictive studies is inefficient and difficult to reproduce. Thousands of predictive health algorithms have been developed; however, less than 2\% have been assessed outside their original setting and even fewer have been applied and evaluated in practice.

Objectives and ApproachObjective: To develop a standardized workflow for algorithm development, dissemination and implementation.

Existing predictive analytics workflow and open standards were adapted and expanded for health research and health care settings. The approach was designed to work within multidisciplinary teams and to improve research transparency, reproducibility, quality, efficiency and application. Key components include standardized algorithm description files, documentation and code libraries. All libraries and programming packages, which were created for/with open-source software, can be used for a wide range of statistical and machine learning models. Publicly-available repositories contain the algorithms, validation data, R code and other supporting infrastructure.

ResultsAlgorithm development involves variable pre-specification and documentation of model variables, followed by creation of data preprocessing code to generate model variables from the study dataset. Preprocessing uses algorithm specification documentation and a function library, building upon and integrating with existing algorithms when possible to preventing code duplication. Models are output as a Predictive Modelling Markup Language (PMML) file, a portable industry standard for describing and scoring predictive models. A separate scoring "engine" is used to implement PMML-described algorithms in a range of settings, including algorithm validation at other research institutions. Algorithm applications currently include the Project Big Life (www.projectbiglife.ca) online calculators, population, health services and public health planning uses and an algorithm visualization tool. An API permits use of the calculator engine by other organizations.

Conclusion/ImplicationsBarriers to the implementation of predictive analytics in real-world settings—such as within electronic medical records or decision aid applications—can be mitigated with well described algorithms that are easy to replicate and implement, especially as access to big health data increases and algorithms become increasingly complex.
International Comparison in Opiate Prescribing for New Users in Primary Care using Electronic Medical Record Data
Authors: Tamblyn Robyn , Girard Nadyne , Habib Bettina , Dixon William , Jani Meghna , Bates David , Haas Jennifer
Published: September 2018

IntroductionThe opioid epidemic in North America has, in part, been attributed to an increase in opiate use for non-cancer pain and the prescription of more potent molecules. In contrast, the United Kingdom appears unaffected by this crisis, possibly because of differences in primary care prescribing, or health system policies.

ObjectiveTo determine if there are differences in opiate prescribing for new users in primary care in the United Kingdom, United States, and Canada.

ApproachElectronic health record data from Quebec, Canada (MOXXI), the United States (Partners Health Care, Boston MA), and the United Kingdom (CPRD random sample of 600,000) were used to identify new users of opiates (no prior prescription in 2 years), at least 18 years old between 2006-2016. Cancer patients were excluded after harmonizing equivalent READ and ICD9/10 codes. Generic drug names in each jurisdiction were mapped to the WHO ATC classification, and characterized using morphine milligram equivalents (MME).

ResultsOverall 655,877 new users were identified, of whom 78% of 58,286 (U.S.), 88% of 6,251 (Canada), and 96% of 600,000 (UK) were non-cancer patients. Mean age of new users was 49 (SD 16) in the US, 57 (SD 16) in Canada, and 52 (SD 19) in the UK. 57.6% (UK) to 67.3% (US) of new users were women. In the UK, 86.5% of patients were started on codeine (MME:0.15), compared to 43.9% in Canada and 8.5% in the U.S. In the U.S 65.0\% were started on oxycodone (MME:1.5), and 10.9% on hydrocodone (MME:1). In Canada, tramadol (18.2%; MME: 0.1) followed by oxycodone (13.2%) were the next most commonly prescribed drugs.

Conclusion/ImplicationsSubstantial differences in opioid prescribing practices for non-cancer pain were observed between the UK and Canadian and United States sites. The predilection to start patients on more potent opiates in North America may be a contributing cause to the opiate epidemic.
Defining a GP consultation
Authors: Lugg-Widger Fiona , Jones Hywel , Angel Lianna , Cannings-John Rebecca , Lau Mandy , Hughes Kathryn
Published: September 2018

IntroductionThe method used to define a GP-consultation using routine data will vary depending on the data source and research question. However, without consensus the approach will be tackled by numerous researchers and invariably result in different definitions. The LUCI study is assessing outcomes of childhood UTI using routine data sources.

Objectives and ApproachWe are interested in GP urine sampling rate and re-consultation rate. The GP dataset includes multiple clinical (acute, routine monitoring, prescribing, results) and administrative codes. A GP consultation therefore needed to be defined. GP read code frequencies for our study cohort of children were produced. One researcher reviewed the codes that represented 80% of the data and classified them as a consultation or not. Where there was uncertainty this was independently reviewed by two clinicians and discrepancies agreed by the management team.

ResultsWe will present the decisions and assumptions made for this study in defining a GP consultation. 25,623 Read Codes represented the consultations for our study cohort of which 460 codes represented 80% of the consultations. The remaining 20% of codes represented <0.032% of records each. The 460 Read Codes were reviewed and following independent review and agreement from the management group 149 Read Codes were excluded. These were grouped into the following categories Administrative (e.g. Incoming Mail Processing, Ethnicity codes), Vaccinations, Child Health (e.g. First Smiled), Asthma and Medication Review. The GP consultations to be included in the analysis were then restricted to exclude these codes. This has been an important step in cleaning and understanding the data prior to analysis.

Conclusion/ImplicationsA consultation was defined for this study however there will be pros and cons to this method. In lieu of a consensus approach to the method this work is the first step to sharing approaches to help researchers with the same challenge.
Cognitive development Respiratory Tract Illness and Effects of eXposure (CORTEX) project: Combining high spatial resolution pollution measurements with individual level data, a methodological approach.
Authors: Rodgers Sarah , Lyons Jane , Mizen Amy , Berridge Damon , Akbari Ashley , Carruthers David , Davies Gwyneth , Dearden Lorraine , Doherty Ruth , Lake Iain , Mavrogianni Anna , Milojevic Ai , Strickland Sarah , Wilkinson Paul
Published: September 2018

IntroductionThe Secure Anonymised Information Linkage (SAIL) databank facilitated linkage of routinely collected health and education data, high spatial resolution pollution modelling and daily pollen measurements for 18,241 pupils in 7 cross-sectional cohorts across Cardiff city, UK, to investigate effects of air quality and respiratory health conditions on education attainment.

Objectives and ApproachAn urban atmospheric dispersion and chemistry modelling system (ADMS-Urban) simulated modelled hourly concentrations of air pollutants: PM2.5, PM10, NO2 and ozone levels. These were summarised into minimum, average and maximum daily readings for 4 time periods (e.g. school hours 9am-3pm) for all home and school locations across Cardiff between 2009 and 2015. The combination of different pollutants, measurements and time-periods created a comprehensive multi-row dataset per location. We transformed the dimensionality of this high-resolution data to create one row of summarised data per pupil per cohort, in preparation for statistical analysis.

Results157,361 school and home locations across Cardiff were anonymised and household linkage fields were appended to combine pollution estimates at the household/school to individual health data. The pollution dataset contained 369 columns, 472,083 rows per year with one column per location, pollutant type, pollutant measurement, daily time-period, and day of year. Dataset transformation reduced algorithm computation by creating a single date column, producing a five column, 3,446,205,900-row matrix per year dataset. The algorithm adjusted for weekends, school/bank holidays and allowed location to vary 3pm-5pm on school days when pupil location was uncertain. The algorithm calculated tailored pollution exposures per pupil for revision and examination periods, creating one row per pupil and reducing 7 years of data and 24 billion rows to 18,241.

Conclusion/ImplicationsWe successfully linked 95% of the cohorts’ household/school pollution data to their corresponding health and education data. This demonstrates data linking retrospective exposures for total populations using multiple daily locations, and extends our analysis platform for natural experiments to include daily exposure. Future work includes adding modelled route exposures.
Trends in Socioeconomic Inequalities in Ischemic Heart Disease, 2000-2012
Authors: Smith Brendan , Ramraj Chantel , Smith Peter , Chen Hong , Tu Jack , Manson Heather , Rosella Laura
Published: September 2018

IntroductionLow socioeconomic position (SEP) is an important risk factor for ischemic heart disease (IHD). Current surveillance methods use area-based SEP measures to monitor trends in socioeconomic inequalities in IHD. The extent to which these methods underestimate individual-level socioeconomic inequalities in IHD is unclear.

Objectives and ApproachThe study objective was to estimate socioeconomic trends in IHD by household income and material deprivation in Ontario from 2000 to 2012. A pooled cross-sectional study was conducted using data from 6 Canadian Community Health Survey (CCHS) cycles (2000-2012) linked to the Discharge Abstract Database (n= 119,529 over 35 years of age, 55% female). Relative-weighted Poisson regression models were used to estimate IHD prevalence rates (adjusted for age, sex, ethnicity and immigration) across quintiles of equivalized household income and area-level material deprivation. Socioeconomic inequalities were estimated using the slope index of inequality (SII) and relative index of inequality (RII).

ResultsSocioeconomic inequalities in IHD were observed across income and material deprivation quintiles. Measured using the SII, adjusted IHD rates were 345 per 10,000 (95%CI: 207,483) higher at the bottom of the income distribution than the top in 2000, decreasing to 167 per 10,000 (95%CI: 40,293) by 2012. These differences represented 2.52 (95%CI: 1.58,3.46) times higher IHD rates in 2000, an increased risk that remained in 2012 (RII: 1.80, 95%CI: 0.97,2.63). A similar pattern was observed across material deprivation quintiles, however with smaller absolute and relative inequalities observed in 2000 (SII:195 per 10,000, 95%CI:79,312; RII:1.64, 95%CI:1.16,2.11) and 2012 (SII:142 per 10,000, 95%CI:16,268; RII:1.54, 95%CI:0.94,2.14).

Conclusion/ImplicationsConsistent socioeconomic inequalities in IHD were observed in Ontario, with an absolute reduction between 2000 and 2012. Area-level material deprivation underestimated individual-level socioeconomic inequalities in IHD.
Developing trauma mortality prediction models to measure injury severity
Authors: Walters Angharad , Cook Alan , Gabbe Belinda , Lyons Ronan
Published: September 2018

IntroductionInjury severity measurement is integral to meaningful benchmarking and injury prevention strategies. Numerous injury mortality prediction methods have been developed and advanced, however, no consensus on the best model has been reached.

Objectives and ApproachThe International Collaborative Effort (ICE) on Injury Statistics propose to develop a set of trauma mortality models for use in diverse settings by deriving and validating models with data from member countries including Wales, Australia, New Zealand and USA. The ICE team will create a definitive list of injuries required to identify trauma patients using ICD-10 codes. Models will be developed using Welsh data then validated with data from other member nations. The outcomes of interest are in-hospital and 30-day mortality. Models will be used for country benchmarking by comparing the distribution of injury severity and outcomes between nations.

ResultsInitial results from replicating the model published by Wada, et al., as closely as possible using 348,433 cases held in SAIL for the years 2000 to 2013 achieved an AUROC value of 0.908 (95% CI 0.905-0.911). This model included 38 injury indicator variables, age, sex and comorbidity score. From 2009 onwards, the estimated number of deaths exceeded the actual number of deaths indicating improving risk adjusted survival. We aim to further enhance these models with additional covariates by linking with critical care data to enable us to determine the level of support patients received during their hospital stay, linking with laboratory data to provide indications of multiple organ dysfunction, acute physiological response and infection, and with GP data to incorporate measures of frailty.

Conclusion/ImplicationsUsing multi-sourced population based linked data allows us to develop a suite of enhanced mortality models for use in observational and interventional research. Applying these methods to data from different countries will allow comparisons to be made of trends in severity and outcomes and support collaborative research.
Integrated Data Enabling Statistics and Analysis (IDEAS) for UK Statistics
Authors: Wroth-Smith Jonathan
Published: September 2018

IntroductionThe Office for National Statistics (ONS) is transforming the way it produces statistics and how it facilitates policy and academic research. Legislative change will enable access to administrative and commercial data to underpin this transformation but only through an innovative record linkage will the full benefits of transformation be realised.

Objectives and ApproachGreater access to non-survey data brings with it huge opportunities for both social and economic statistics in the UK - significant improvements in timeliness and geographic fidelity. There are also risks in working with large, complex datasets which have not been compiled for statistical analysis. ONS are developing the Integrated Data Enabling Analysis and Statistics (IDEAS) initiative to provide ONS staff to linked datasets, without constructing a large database of everything. The approach involves routinely linking data to three indices (person, address, business) and assigning unique reference numbers. Data are then de-identified but can be re-joined for defined projects.

ResultsThe session will set out case studies of linkage work that either have already been taken forward using IDEAS or those which are planned. Particular emphasis will be given to how IDEAS can facilitate research across a range of statistical research topics by linking on person, address and business.

By making anonymised linked data available in a safe setting the IDEAS framework also has the potential to facilitate research across government and across academic research. ONS have ambitious plans to use this approach to draw together expertise from across both sectors to be able to realise the benefits of linked where knowledge is shared on data quality, methods and previous research undertaken.

Conclusion/ImplicationsThe IDEAS framework is as step change in the production of official statistics. With appropriate legal, ethical and security controls, linked data within IDEAS can be repeated and refined for different applications. The framework enables collaboration across disciplines and has the potential to be using across government and academia.
Designing and Implementing a Privacy Preserving Record Linkage Protocol
Authors: Gee Tom , Behan Brendan , Lefaivre Shannon , Azimaee Mahmoud , Dharsee Moyez , El Emam Khaled , Yang Julie , Vaccarino Anthony , Evans Kenneth , Victor J. Charles , Theriault Elizabeth
Published: September 2018

IntroductionThe Ontario Brain Institute has developed Brain-CODE, an informatics platform, to support the acquisition, storage, management and analysis of multi-modal data. The standardized research data within Brain-CODE spans several brain disorders, allowing for integrative analyses, while also providing the opportunity to leverage existing clinical administrative data holdings through external linkages.

Objectives and ApproachWithin Ontario, the majority of individuals who access the healthcare system have a unique identifier, the Ontario Health Insurance Plan (OHIP) number. The OHIP number can facilitate linkages with administrative data holdings, such as those at the Institute for Clinical Evaluative Sciences (ICES). Given that OBI is not permitted under Ontario’s privacy legislation to hold OHIP numbers, identifiers for consented participants are encrypted using a public key mechanism upon entry into Brain-CODE, where the private key is inaccessible. To facilitate linkages involving OHIP numbers between Brain-CODE and ICES, Brain-CODE Link software was co-developed by members of the Indoc Consortium.

ResultsBrain-CODE Link allows a deterministic linkage between encrypted identifiers (OHIP numbers), without revealing participant identity. The same homomorphic encryption algorithm applied to identifiers upon entry to Brain-CODE, is applied to relevant identifiers within ICES data holdings. Encrypted identifiers from Brain-CODE are securely transferred to ICES, where a comparison computation calculates differences between the encrypted sets. These differences are sent to a semi-trusted third party, who has no access to the original data, to decrypt the differences using the private key. A zero difference indicates a set of matching identifiers. One of the main challenges during testing and development of Brain-CODE Link was ensuring the software was capable of scaling to a population level, performing a large number of comparisons, in a computationally efficient manner.

Conclusion/ImplicationsOngoing pilot projects within the areas of epilepsy, neurodevelopment disorders, and neurodegeneration will be the first examples of linkages between Brain-CODE and ICES. Brain-CODE Link has successfully performed several billion test comparisons, indicating its suitability to function as a scalable privacy preserving record linkage to support comprehensive analyses.
Identification of Frailty using EMR and Admin data: A complex issue
Authors: Katz Alan , Wong Sabrina , Williamson Tyler , Taylor Carole , Peterson Sandra
Published: September 2018

IntroductionFrailty is a state of vulnerability to diverse stressors emphasizing the importance of identifying the frail to support them. The burden of frailty in Canada is steadily growing. Today, approximately 25% of people over age 65 and 50% past age 85 – over one million Canadians – are medically frail.

Objectives and ApproachTo develop an administrative data definition of frailty to facilitate clinical and health system planning. We will validate the definition by linking the administrative data to electronic medical records (EMR) data. The EMR definition is based on a Machine Learning binarized frailty flag for patients with a Rockwood Clinical Frailty Score > 5 on physician chart audit. The sensitivity of the Machine Learning was disappointing: 28% (95% CI: 21% to 36%).specificity was: 94% (95% CI: 93% to 96%), positive predictive value: 53% (95% CI: 42% to 64%), negative predictive value: 86% (95% CI: 83% to 88%).

ResultsThere was little overlap between the EMR and administrative data definitions using the same population. Of the 29,382 eligible administrative data community dwelling patients over 65 years old, with a linkable EMR record, 2398 (8.15%) were identified as frail using the administrative data definition, but only 16.1% of these were frail according to the EMR definition. Of the 2396 who were identified as frail in EMR data, only 375 (15.7%) were identified as frail using the administrative data definition.

Conclusion/ImplicationsWe are not yet able to develop a reliable administrative data definition of frailty to identify community living individuals to support health service planning. The lack of agreement between the results obtained from EMR and administrative data definitions suggests that further refinement is necessary. Identification of frailty remains complex.
International Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research
Authors: Aitken Mhairi , Tully Mary , Porteous Carol , Cunningham-Burley Sarah
Published: September 2018

IntroductionData-intensive health research is a fast moving field in which public involvement and engagement (PI\&E) is essential for developing socially acceptable and ethically robust processes and ensuring a social license for research practices. Nevertheless while some consensus is emerging around the importance of PI\&E, commitments and practices are varied.

Objectives and ApproachOur objective was to identify a set of principles which would underpin international best practice in data-intensive health research.

A one-day consensus workshop was held in Manchester, U.K. in April 2017 and attended by 31 international participants from six countries, with a range of expertise relating to PI&E with data-intensive health research. Participants took part in facilitated discussions to identify key principles and a consensus statement was written to reflect the discussions at the workshop, this developed through iterative drafts on which all workshop participants as well as the four Farr Institute public panels were consulted.

ResultsThe consensus statement sets out key principles to establish a secure role for PI&E related to data-intensive health research internationally and to ensure best practice in its execution. While methods used may not vary greatly from PI&E with other types of research, or other policy areas, we have identified a number of particular features of data-intensive health research which make PI&E in this area worthy of special consideration. Often using data from multiple sources without explicit consent, PI&E in data-intensive health research is particularly important for raising awareness, for giving people a voice and for enabling people to participate in processes of research and governance. The consensus statement sets out the key principles to guide future best practice in PI&E with data-intensive health research.

Conclusion/ImplicationsThe emergence of data intensive health research and the importance of the social contract upon which it relies, demands that we move beyond rhetorical commitments and engage anew with clearly stated principles to build PI\&E into data-intensive health research at all levels.
Collaborative Public Health Surveillance Strategies to Address Local Health Needs
Authors: Padron Norma
Published: September 2018

IntroductionThis presentation will review the current strategies being used by health care delivery systems across the US to incorporate via linkage, publicly available data assets. The discussion will focus on lessons learned with a specific emphasis of collaborations between health systems to address the opioid crisis.

Objectives and ApproachTo review ongoing strategies to incorporate local, publicly available data assets to clinical data assets that health systems have for purposes of collaborations with public health surveillance.

The emphasis of the discussion presented will be in the data strategies that local health departments and health care delivery systems have used to address the opioid crisis in the US.

This presentation will propose strategies to be explored and bring forth concerns about data fairness, accountability and transparency when collaborations for public health surveillance are in place.

ResultsThe presentation will discuss the experiences learned in specific regions in the United States. The main results will center around assessing the effectiveness of current strategies to share and analyze data across health care delivery systems and local agencies and government partners. The lessons learned of what works and what hasn't will be discussed in light of the ongoing epidemic of opioid use and drug overdose deaths in the United States. Finally the presentation will present strategies that could be explored for collaborative public health surveillance that address issues and concers of fairness, accountability and transparency.

Conclusion/ImplicationsThe implications of this report and presentation is that ongoing data linkage and sharing strategies have been -for the most part- insufficient to enable delivery systems and local public health departments and government address rising epidemiological concerns. The proposed strategies complement what is being done and advance data-driven public health
Multi-province epidemiological research using administrative data in Canada: Challenges and opportunities
Authors: Butler Amanda , Smith Mark , Jones Wayne , Adair Carol , Vigod Simone , Kurdyak Paul
Published: September 2018

IntroductionCanada has a publicly-funded universal health care system with information systems managed by 13 provinces and territories. This context creates inconsistencies in data collection and challenges for epidemiological research conducted at the national or multi-jurisdictional level.

Objectives and ApproachUsing a recent five-province research project as a case study (BC, AB, MB, ON, QC), we will discuss the strengths and challenges of using Canadian administrative health data in a multi-jurisdictional context. Our goal is to contribute to a better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research.

ResultsMulti-jurisdictional data work is feasible but requires detailed coordination and extensive cooperation from all involved. There were noteable variations across provinces in this multi-province study. For example, time required to access the data varied greatly across the five provinces (from 4 to 9 months), and thus there were sequencing challenges, with some provinces being well into the analysis stage while others were still waiting for data. Access to human resources varied across provinces and in some cases led to delays in data abstraction. Cost of data (or analytic support) also varied across provinces, from $12,000 – $15,000. Critical to the success of the project was a coordinating group with expertise in both administrative health data and cross-provincial project coordination.

Conclusion/ImplicationsThis project demonstrated the value of comparable data infrastructure with equitable access policies. Many of the disadvantages to multi-province projects using health care administrative data, such as potential coding errors and inconsistencies, can be managed by developing national standards and protocols, and tools that are shared for data cleaning and validation.
Linking surveillance and administrative data to better understand dementia’s impact in Canada
Authors: Johnson Tracy , Husak Liudmila , Pelletier Catherine , Bartholomew Sharon
Published: September 2018

IntroductionCanadian Institute of Health Information and Public Health Agency of Canada combined analytical work for a dementia report. The report linked surveillance and administrative data to support policy makers, health system planners and public in understanding the prevalence of seniors with dementia and their interactions with the health care system.

Objectives and ApproachDementia prevalence data from PHAC’s Canadian Chronic Disease Surveillance System was used as a denominator, and data from CIHI’s administrative databases was used as a numerator to calculate the statistics on interactions of seniors with dementia with the healthcare system.

Examples of the measures reported by database include:

Using DAD: Proportion of seniors with dementia who were hospitalized;

Using NACRS: Proportion of seniors with dementia who visited emergency departments;

Using CCRS: Proportion of seniors with dementia living in and outside of nursing homes;

Using HCRS: Proportion of seniors with dementia living in the community and receiving home care services.

ResultsResults for the measures above, as well as rates of patients receiving different services, statistics on where they live, their characteristics, quality of care by sectors, and impacts on caregivers will be presented.

One out of 5 seniors with dementia is admitted to hospital every year, and one out of four visited emergency departments at least once. Two out of five seniors with dementia reside in long term care. While in nursing homes, seniors with dementia experience more inappropriate antipsychotic and restraint use compared to seniors without dementia. They are especially susceptible to injury and falls. Once hospitalized, they tend to stay longer waiting for placement and experience greater hospital harm. Caregivers of seniors with dementia experience more distress compared to caregivers of other seniors.

Conclusion/ImplicationsThis work illustrates benefits of combining data from different organizations and sectors to help inform policy and fill data gaps. This innovative approach using PHAC’s surveillance and CIHI’s administrative data avoids confusion from varying estimates and duplication of work between organizations, generates new evidence, and reaches a broader audience.
Integrating population-wide laboratory testing data with physician audit-and-feedback reports to improve glycemic and cholesterol control among Ontarians with diabetes
Authors: Campitelli Michael , Paterson Michael , Azimaee Mahmoud , Greenberg Anna , Paprica P. Alison , Schull Michael , Victor J. Charles , Kumar Matthew , Shah Baiju , Tu Jack , Glazier Rick
Published: September 2018

IntroductionImproving the care and management of patients with diabetes, particularly those with extreme blood glucose and/or cholesterol levels, has been identified as a key priority area for healthcare in Ontario. A multi-organizational collaboration produces audit-and-feedback reports distributed to consenting primary care physicians across the province for quality improvement purposes.

Objectives and ApproachWe examined the feasibility of linking the Ontario Laboratory Information System (OLIS), a large and nearly population-wide database of laboratory test results in Ontario, with the existing provincial audit-and-feedback reporting structure to integrate aggregated, physician-level measures of glycemic and cholesterol control among patients with diabetes.

All Ontario residents alive on March 31, 2014, attached to a primary care physician, and diagnosed with diabetes for at least two years were included. These patients were linked to OLIS to extract laboratory test orders and results for glycated hemoglobin (HbA1C) and low-density lipoproteins (LDL) between April 1, 2013 and March 31, 2014.

ResultsThere were 1,108,530 diabetes patients included who were assigned to 10,085 primary care physicians. During fiscal year (FY) 2013, 70%, 64%, and 59% of diabetes patients were tested for HbA1C, LDL, and both measures, respectively. Among the 648,238 diabetes patients with at least one of each test in FY2013, 13% had a HbA1C test exceeding a threshold of 9%, 4% had a LDL test exceeding a threshold of 4 mmol/L, and 0.8% exceeded both thresholds. At the physician-level, the median (Interquartile Range) proportions of diabetes patients exceeding the testing thresholds were 12% (9%-16%) for HbA1c and 4% (2%-6%) for LDL. In a multilevel logistic regression model, there was significant between-physician variability in the proportions of diabetes patients exceeding the HbA1C (p

Conclusion/ImplicationsWe developed a mechanism for integrating population-wide, clinical laboratory test results into physician audit-and-feedback reports to improve diabetes care in Ontario. Significant variation observed in the aggregated, physician-level proportions of diabetes patients testing above clinical thresholds for HbA1C and LDL highlights the importance of reporting such information to physicians.
Neighbourhood built environments as correlates of hospital burden and premature mortality in Canada
Authors: Mah Sarah M , Sanmartin Claudia , Riva Mylène , Dasgupta Kaberi , Ross Nancy
Published: September 2018

IntroductionThe built environment can shape modifiable risk factors such as obesity, poor diet, and physical inactivity, and could be a policy lever for the reduction of chronic disease. In Canada, the health care costs related to chronic disease continue to rise and there have been few policy options offered.

Objectives and ApproachWe examine the role of the built environment in hospital burden and premature mortality, with an emphasis on one of the highest burden diseases, Type 2 Diabetes (T2D). Neighbourhood built environment measures for active living were derived using geographic information systems for respondents of the Canadian Community Health Survey, for whom we have linked hospitalization and mortality records. A combination of ICD codes, self-reported diabetes status, as well as a population-based algorithm identifying those at higher risk of developing diabetes were used to identify cases. Differences in hospitalization frequency, cumulative length of stay, and mortality are investigated.

ResultsOver half a million hospitalization records were identified in our cohort of roughly 450,000 survey respondents. Key factors such as age, gender, race, and socioeconomic status are accounted for in modelling the association between neighborhood environment and hospitalization. Hospital burden and mortality in T2D patients are much higher than that of patients who do not report having the condition, and those at elevated risk of T2D display intermediate levels of hospitalization. Two-part hurdle models show evidence of an association between more walkable neighborhoods and lower hospitalization risk in non-T2D patients as well as those at elevated risk of developing T2D. The relationship between neighborhoods and the volume of chronic-disease related episodes as well as mortality is unclear, and under further investigation.

Conclusion/ImplicationsElucidating the role of neighbourhood built environments on hospital burden and premature mortality for individuals with diabetes will provide insight as to the full range of clinical and non-clinical interventions that could feasibly address the needs of some the highest health care system users.
Premature Mortality among People with Severe Mental Illness – New Evidence from Linked Primary Care Data
Authors: Lee Sze Chim , John Ann , McGregor Joanna , Jones Ian , Walters James , Owen Michael , O‘Donovan Michael , Del Pozo Banos Marcos , Berridge Damon , Lloyd Keith
Published: September 2018

IntroductionStudies assessing premature mortality in people with severe mental illness (SMI) are often based in one setting, hospital (secondary care inpatients and/or outpatients) or community (primary care). This may lead to ascertainment bias.

Objectives and ApproachThis study aimed to estimate standardised mortality ratios (SMRs) for all-cause and cause-specific mortality in people with SMI drawn from linked primary and secondary care populations compared to the general population. Standardised mortality ratios (SMRs) were calculated using the indirect method for a United Kingdom population of almost four million between 2004-2013.

ResultsThe all-cause SMR was higher in the cohort identified from secondary care hospital admissions (SMR: 2.9; 95% CI: 2.8-3.0) than from primary care (SMR: 2.2; 95% CI: 2.1-2.3) when compared to the general population. The SMR for the combined cohort was 2.6 (95% CI: 2.5-2.6). Solely hospital admission based studies may somewhat over-estimate premature mortality in those with SMI. However, there is no doubt this remains a major health inequality. Cause specific SMRs in the combined cohort were particularly elevated in those with SMI relative to the general population for ill-defined and unknown causes, suicide, and substance abuse, as well as a number of other causes.

Conclusion/ImplicationsThe ability to combine cohorts electronically from primary and secondary care is more representative of the whole population. Comprehensive characterisation of mortality is important to inform policy and practice and to discriminate settings to allow for proportionate interventions to address this health injustice.
First Nations Data Governance, Privacy, and the Importance of the OCAP® principles
Authors: Mecredy Graham , Sutherland Roseanne , Jones Carmen
Published: September 2018

IntroductionGovernance of First Nations data and information requires important considerations that go beyond those typically used in research. Researchers are generally not trained in how to work appropriately within the realm of First Nations data. Further, while Canadian legislation protects individual privacy, First Nations’ community privacy is not protected.

Objectives and ApproachThe OCA® principles were created to fill these identified gaps. OCAP® is an acronym that outlines principles regarding the collection, use, and disclosure of data or information regarding First Nations. The letters in OCAP® describe four key principles: Ownership, Control, Access and Possession.

ResultsFirst Nations OCAP® principles are beginning to make a paradigm shift in research. This shift in applying OCAP® is changing the standard for First Nations’ data and information. These principles give First Nations sovereignty over their data and information when applied appropriately. The principles go beyond the protection of individual privacy to include the additional consideration of community privacy, a vital issue when working with First Nations’ data.

Conclusion/ImplicationsOCAP®, when effectively applied, is a bridging tool for both First Nation communities and researchers to engage in relevant, reciprocal, and practical research projects to tell a story, provide insight, and effect policy change.
Mortality of mothers with opioid-use during pregnancy: an international comparison using linked mother-baby records for England and Ontario
Authors: Guttmann Astrid , Blackburn Ruth , Amartey Abigail , Zhou Limei , Wijlaars Linda , Saunders Natasha , Harron Katie , Gilbert Ruth
Published: September 2018

IntroductionOpioid-use during pregnancy is indicative of future adversity for mothers and children. We aimed to investigate neonatal abstinence syndrome (NAS) as a marker of drug-use in pregnancy and to compare maternal all-cause mortality relative to those with infants born without NAS in two high prevalence jurisdictions; England and Ontario.

Objectives and ApproachWe developed two parallel cohorts using linked mother-baby records for all births in hospitals in England and Ontario between 2002 and 2013. Mothers with opioid-use were identified based on NAS recorded in the baby’s record. Maternal mortality within 10 years of delivery was compared for mothers with and without a NAS-pregnancy. The association between opioid-use in pregnancy and all-cause mortality was modelled using Cox regression. In addition, we estimated the unadjusted cumulative incidence of cause-specific mortality within a competing risks framework. Harmonised clinical codes were used to define all exposures, outcomes and comorbidities.

ResultsThe study population comprised 13,581 and 4,966 NAS mothers in England and Ontario, respectively. Controls totalled 4,205,941 for England and 929,985 for Ontario. The crude hazard ratios for all-cause mortality were 12.1 (95% CI; 11.1-13.2) for England and 11.4 (9.7, 13.4) for Ontario, which were attenuated to 9.8 (9.0-10.6) and 9.0 (7.6, 10.7) respectively, after adjustment for maternal age at delivery and Charlson comorbidity index. The cumulative incidence of death was much higher among NAS mothers than controls for all causes except cancer in the English cohort, where estimates were similar. The majority of deaths within the NAS group were attributable to avoidable causes, particularly reflecting unintentional injuries (cumulative incidence of 19.6 (16.6-23.1) for England and 13.9 (9.7-19.3) for Ontario).

Conclusion/ImplicationsOur approach demonstrates the utility of mother-baby record linkage to examine long-term health outcomes of vulnerable families. We identified similarly high mortality rates among the mothers of NAS babies in both England and Ontario, indicating a need for public health programs to target support to mothers as well and infants.
Evidence from an Applied Research Health Question (AHRQ): Healthcare utilization of HIV patients before and after admission to Casey House, a specialized HIV hospital
Authors: Plumptre Lesley , Graves Erin , Ishiguro Lisa , Antoniou Tony , Stewart Ann , Carusone Soo Chan
Published: September 2018

IntroductionThe Applied Health Research Question (AHRQ) portfolio is an initiative funded by the Ontario Ministry of Health and Long-Term Care, leveraging the linked data and the scientific expertise at ICES to answer questions from Knowledge Users that have a direct impact on healthcare policy, planning or practice.

Objectives and ApproachA request from Casey House, a specialty HIV hospital located in Toronto, ON, was reviewed and approved by the ICES AHRQ Team to evaluate patient healthcare utilization and costs. The purpose was to support the design of programs and services, improve transitions from healthcare settings to community services, and inform continuous quality improvement initiatives. Using inpatient records, hospital admissions to Casey House were identified in fiscal years 2009-2014. Inpatient, emergency, outpatient and home care visits were characterized before and after admission. Using the Ontario Drug Benefit Claims, antiretroviral (ARV) prescription fills were examined 7 days post discharge.

ResultsBetween April 1, 2009 and March 31, 2015, 268 HIV patients had one or more hospital admissions to Casey House. The majority of Casey House patients had an Aggregated Diagnosis Group (ADG) ≥ 10 (79%) or Resource Utilization Band (RUB) = 5 (78%), indicating a high co-morbidity burden. Rate of emergency department usage declined from 4.61 to 2.46 per person-year, before and after Casey House admission (p < 0.0001). Conversely, home care visits increased from 24.29 to 35.63 per person-year and family physician visits increased from 18.33 to 22.59 per person-year before and after Casey House admission (both p < 0.0001). Interestingly, 89% did not fill an ARV prescription within 7 days of Casey House discharge, however 76% followed up with an outpatient HIV visit within 30 days.

Conclusion/ImplicationsHealthcare utilization differed before and after admission to Casey House. Follow-up post-discharge warrants further examination to increase ARV prescription fills. Data from this AHRQ has facilitated future policy and programming changes. Results have been disseminated throughout the Toronto HIV research community to generate discussion on quality improvement in this population.
Population Data Science: The science of data about people
Authors: McGrail Kim , Jones Kerina
Published: September 2018

IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward.

Objectives and ApproachOur objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper.

ResultsWe define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary “team science” field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement.

Conclusion/ImplicationsThese implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research.