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The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.

Objectives People with intellectual and developmental disabilities (IDD) have been disproportionately impacted by COVID-19, a population more likely to experience poor health outcomes. An Applied Health Research Question (AHRQ) request through ICES led to an investigation that monitored COVID-19 related infections and outcomes in adults with and without IDD. Approach The ICES-AHRQ team approved a request from the Ministry of Children, Community and Social Services (MCCSS), to determine the proportion of adults with IDD tested and confirmed positive for COVID-19. The scientists also explored if positive IDD cases experienced similar health outcomes to the general population. The open cohort was derived by linking those with a COVID-19 test in Ontario Laboratories Information System, from January 2020 to December 2021, to supplemented positive case data and administrative health databases. An algorithm was used to define IDD by a series of inpatient hospitalizations, emergency department and/or physician visits, with IDD diagnostic codes. Results Similar rates of testing and positivity were observed for those with (46%; 6%) and without IDD (43%; 5%). However, adults with IDD confirmed positive for COVID-19 were mostly male (62% vs 49%), aged 18-29 (40% vs 27%), had medical conditions associated with frailty (15% vs 4%), and from the lowest neighborhood income quintile (27% vs 23%), compared to the non-IDD population, respectively. Cumulatively, deaths and hospitalizations following a COVID-19 diagnosis were two-times more likely in the IDD population, while comparable rates of intensive care unit (ICU) admissions were reported. Particularly, adults with IDD aged 18-54 experienced higher rates of hospitalizations (48% vs 30%), ICU admissions (56% vs 36%), and deaths (26% vs 6%), compared to the same age group in those without IDD. Conclusion/Implications Real-time administrative health data and analytics were used to support Ontario’s COVID-19 response in those with IDD. To inform decision making and policy actions related to immediate testing strategies, MCCSS used such findings to increase the availability of testing for adults with IDD, who may face multiple barriers.

Objectives Equitable vaccine distribution was essential in supporting Ontario’s COVID-19 response, particularly for persons with intellectual and developmental disabilities (IDD), who have been disproportionately impacted by the pandemic. An Applied Health Research Question (AHRQ) request through ICES, sought to report on COVID-19 vaccination uptake in adults with and without IDD. Approach To examine the proportion of adults with IDD vaccinated for COVID-19, a request from the Ministry of Children, Community and Social Services (MCCSS) was approved by the ICES-AHRQ team. A secondary goal was to explore if vaccinated individuals with IDD experienced similar levels of protection from COVID-19 infection, to the general population. The open cohort was derived by linking vaccination events reported in Ontario COVID-19 Vaccine Data, between December 2020 to December 2021, to several administrative health databases. An algorithm was used to define IDD by a series of inpatient hospitalizations, emergency department and/or physician visits, with IDD diagnostic codes. Results The proportion who received at least two doses was similar between both groups (82% (IDD); 83% (non-IDD)). However, despite emerging evidence suggesting that high-risk populations should receive a third dose, a lower proportion of people with IDD (25%), compared to the general population without IDD (30%), had done so. The IDD subpopulation is generally younger, and partially explained the third dose findings when examined by age. Withal, adults with IDD vaccinated with at least two doses were mostly male (62% vs 48%), 18-29 years of age (44% vs 17%), and from the lowest neighborhood income quintile (26% vs 18%), compared to the non-IDD population, respectively. Cumulatively, adults with IDD had comparable rates of confirmed breakthrough case infections following vaccination (17%), to those without IDD (19%). Conclusion/Implications Administrative health data and analytics through the ICES-AHRQ program were used in responding to COVID-19, and providing public health guidance, for those with IDD. To inform decision making and policy actions related to immediate vaccination strategies, MCCSS used such findings to target vaccination efforts in specific IDD subpopulations, across Ontario.

Objectives The COVID-19 pandemic has necessitated access to large health system datasets to inform the public health response. To meet this need, the Provincial Health Services Authority and the British Columbia (BC) Ministry of Health collaborated to create a population-based platform that integrates COVID-19 datasets with sociodemographic and administrative health data. Approach A BC COVID Data Library proof-of-concept was created as a cloud-based, dynamic platform composed of de-identified datasets. The BC COVID-19 Cohort (BCC19C) represents a subset composed of people accessing COVID-19 health services (e.g., testing, vaccination) and linked health histories. Provincial COVID-19 datasets are updated daily and include COVID-19 lab tests, case surveillance, vaccinations and hospitalizations/deaths. These can be linked to administrative data holdings for the BC population, which are updated weekly/monthly and include vital statistics, medications, hospital admissions, medical visits, among others. A patient matching algorithm creates unique patient keys that allows the same individual to be linked across datasets. Results The BCC19C has been used provincially to 1) support ongoing surveillance, reporting, and modelling of COVID-19; 2) describe and characterize the epidemiology of COVID-19; and 3) inform acute care planning, public health interventions and health care services in BC. Ongoing and completed BCC19C analyses include assessment of vaccine safety, vaccine effectiveness, and characteristics associated with infection and severe outcomes; use of medical visit data for syndromic surveillance and monitoring of unintended outcomes of the pandemic (e.g., mental health visits); and characterization of long-COVID. Availability of linked administrative data holdings has been crucial for identifying non-COVID control groups, measuring sociodemographics and co-morbidities, and complementing COVID-19 datasets for more complete capture of health outcomes (e.g., deaths, hospitalizations). Conclusions The large scope/breadth and timeliness of the linkable datasets integrated within the COVID Data Library and the BCC19C has supported the public health response in BC. Additional linkage to other data sources will further strengthen this data platform.

Objectives We will enrich the cancer research ecosystem in Canada through linking cancer registry and administrative health data to the Canadian Partnership for Tomorrow’s Health (CanPath) cohort and biobank. CanPath is Canada’s largest population health study, including 1% of the Canadian population, which seeks to investigate cancer development. Approach We are achieving record-level linkage of the CanPath harmonized dataset to provincial cancer registry data, and hospitalization and ambulatory care data from the Canadian Institutes of Health Information (CIHI). The CanPATH harmonized dataset includes comprehensive genetics, environment, lifestyle, and behaviour data. Our linkage activities will result in interprovincial data sharing, with centrally-held linked data, a first in Canadian history. We will demonstrate the CanPath-cancer registry-CIHI linkage potential by investigating the impact of the COVID-19 pandemic on healthcare utilization and outcomes among those with cancer. Results The linkage is ongoing and anticipated to be completed by September 2022. Linked data will be made available through the CanPath Data Safe Haven, a cloud-based solution that meets the legal requirements of the data sharing agreements and provincial privacy policies, and is accessible to researchers through secure access. The CanPath Data Safe Haven will be a federated data platform for Canadian researchers to access, analyze, and contribute research in a collaborative environment. By linking these datasets, this project will: address concerns related to accessibility of cancer data in Canada; bring more value to existing data; support an enhanced understanding of the impacts of cancer on marginalized populations; and create a more integrated approach to cancer data access and management. Conclusion CanPath will be the first program in Canadian history to combine the wealth of cohort resources with cancer registry and administrative health data in a central location at a national scale. We will provide a single point of access for researchers to conduct novel investigations into cancer development and outcomes.

Objectives To link population-based survey data to routinely collected administrative health data to enable investigation of how cancer survivors' ongoing physical, emotional, and practical needs and experiences after completing cancer treatment impact their healthcare utilization, including discharge from oncology to primary care. Approach The "Cancer Transitions Survey" is a population-based survey examining survivors' experiences and needs after completing cancer treatment. The survey was administered by the Nova Scotia Cancer Registry (NSCR) as part of a national study, the largest of its kind in Canada. Respondents included Nova Scotian survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1-3 years after treatment. Survey responses were linked to cancer registry, physicians' claims, hospitalization, and ambulatory care data. The data linkage provided a full four years of healthcare utilization data for each cancer survivor, beginning one year after their cancer diagnosis. Results 1557 survivors responded to the survey and subsequently had their data linked. Collectively, breast, colorectal, and prostate cancer survivors represented 78.5% of survey respondents. Most respondents (65.3%) were 65 years of age or older and 69.8% had an existing co-morbid condition. Regression analyses are now being conducted to investigate whether the type and magnitude of post-treatment care needs, and the interventions (services and supports) received, impact health care utilization in the survivorship period, including discharge to primary care. Conclusion This study represents a unique opportunity to link data unavailable in administrative health data: namely, self-reported needs and use of non-physician services and supports (e.g., support groups, counselling). As such, this dataset permits investigation of healthcare utilization and patterns of care that cannot be accomplished using administrative health data alone.

Introduction: Alberta's Tomorrow Project (ATP) is the largest population-based prospective cohort study of cancer and chronic diseases in Alberta, Canada. The ATP cohort data were primarily self-reported by participants on lifestyle behaviors and disease risk factors at the enrollment, which lacks sufficient and accurate data on chronic disease diagnosis for longer-term follow-up., Objectives: To characterize the occurrence rate and trend of chronic diseases in the ATP cohort by linking with administrative healthcare data., Methods: A set of validated algorithms using ICD codes were applied to Alberta Health (AH) administrative data (October 2000-March 2018) linked to the ATP cohort to determine the prevalence and incidence of common chronic diseases., Results: There were 52,770 ATP participants (51.2±9.4 years old at enrollment and 63.7% females) linked to the AH data with average follow-up of 10.1±4.4 years. In the ATP cohort, hypertension (18.5%), depression (18.1%), chronic pain (12.8%), osteoarthritis (10.1%) and cardiovascular diseases (8.7%) were the most prevalent chronic conditions. The incidence rates varied across diseases, with the highest rates for hypertension (22.1 per 1000 person-year), osteoarthritis (16.2 per 1000 person-year) and ischemic heart diseases (13.0 per 1000 person-year). All chronic conditions had increased prevalence over time ( p < for trend tests), while incidence rates were relatively stable. The proportion of participants with two or more of these conditions (multi-morbidity) increased from 3.9% in 2001 to 40.3% in 2017., Conclusions: This study shows an increasing trend of chronic diseases in the ATP cohort, particularly related to cardiovascular diseases and multi-morbidity. Using administrative health data to monitor chronic diseases for large population-based prospective cohort studies is feasible in Alberta, and our approach could be further applied in a broader research area, including health services research, to enhance research capacity of these population-based studies in Canada., Competing Interests: Statement on conflicts of interest: The authors declared no conflict of interest.