Consultative engagement objectives

In Budget 2023, the Government of Canada introduced a national dental program to be implemented by Health Canada and committed over $13 billion in funding for the administration of this program. In parallel, Statistics Canada received $23.1 million over two years "to collect data on oral health and access to dental care in Canada" and inform the rollout of the new Canadian Dental Care Plan (CDCP). The funding received by Statistics Canada is being leveraged to establish a robust statistical program that includes the collection of data on Canadians' self-reported oral health status and oral health care needs, as well as data on the state of the oral health care system. Secondly, Statistics Canada is making the necessary investments required to build infrastructure for future collection activities beyond 2025.

Statistics Canada's new Oral Health Statistics Program (OHSP) aims to address oral health data needs through a comprehensive and integrated strategy that focuses on two core activities: the creation of new oral health surveys, and the acquisition and integration of administrative data on the topic.

With the creation of its new statistical program, Statistics Canada launched a series of consultative engagement sessions with key stakeholders. The objective of the engagement was to better understand stakeholder preferences for: accessing OHSP results, the format of dissemination products, and how to engage with OHSP data. This activity also aimed to informally survey the stakeholders' awareness of relevant data sources on oral health and oral health care.

Consultative engagement methods

Consultations on the Oral Health Statistics Program were conducted virtually through information sessions that included group discussions with a broad range of stakeholders from the Oral Health community. Input was received from regulatory authorities, professional associations, research networks, and other groups. The engagement sessions took place in two phases, one in the first two weeks of December 2023 and the second in the last two weeks of January 2024. These consultative engagement sessions were publicized through Statistics Canada's Consulting Canadians page. Moreover, individual stakeholders were invited by email to participate and to share the invitation with others within their network. In addition to the virtual group discussions, interested parties were offered an opportunity to provide feedback through means that included electronic forms and written submissions.

Overall, Statistics Canada moderated 10 group discussion sessions in both official languages and received feedback from 115 individuals who represented a total of 61 organizations from both the public and private sectors. These organizations—who were potential data providers and/or data users—included academia, municipal and provincial governments, and provincial and national professional associations in all oral health professions. Oral health professions including dental hygienists, dentists and dental surgeons, dental assistants, dental technicians/technologists, and dental therapists took part in the discussions. Input was also received from provincial- and federal-level regulatory bodies in oral health care.

What we heard from stakeholders

Organizations varied widely in their capabilities and experience when it came to oral health data analytics. Our consultations revealed that academic institutions, professional associations, and government agencies typically have dedicated data analytics teams, while smaller entities such as regulatory bodies tended to have limited capacity for independent data analysis.

Expressing their challenges in the current oral health data ecosystem, several stakeholders generally identified the following: limited resources to conduct analysis; barriers to data access; incomplete datasets due to the lack of integration of claims data with electronic health records, hospital admissions data for oral health issues, and provincial dental program data; organizational silos; and fatigue from responding to surveys resulting in low response rates. Despite these barriers, participants overwhelmingly expressed the potential benefits of using data from the Oral Health Statistics Program (OHSP) in support of advocacy efforts and to inform strategic decision-making. Stakeholders emphasized the importance of enabling access to aggregated, readily analyzable data, along with the flexibility to request specific datasets as needed. Notably, most organizations stated they do not conduct their own oral health surveys or maintain independent data repositories, choosing instead to leverage multiple external data sources. In sum, oral health stakeholders actively seek comprehensive, actionable data to effectively address key challenges and improve oral health outcomes.

Statistics Canada thanks participants for their contributions to this consultative engagement initiative. Their insights and experiences will be essential in developing relevant and timely data dissemination products and strategies that support data users.

As StatCan seeks to improve the quality of delivery of its IT services and reduce overall management and support costs, and as part of its ongoing modernization efforts, the Agency is now aligning services with the current and future needs of the business and has implemented a new Enterprise Service Management Solution (ESMS) for the organization called Helix Software as a Service (SaaS) system from the service provider BMC.

Objective

A privacy impact assessment for the Enterprise Service Management Solution (ESMS) was conducted to determine if there were any privacy, confidentiality, or security issues with this initiative and, if so, to make recommendations for their resolution or mitigation.

Description

The new solution integrates all the functions of service delivery formerly available through the outgoing systems: HEAT, Service Request Management (SRM), Self-service Hub request forms, Informatics Account Portal (IAP) and other portals and forms that integrate with the HEAT system. BMC Helix is a cloud-based SaaS, rather than the on-site hosted solutions of SRM-HEAT.

This system serves two functions: to serve as a service request system for IT as well as a service request management system for internal service delivery areas including: HR, Procurement, Finance, Facilities and Security. All services will now be provided via a unique front-end portal (DWP). The IT Service Management (ITSM) suite (incident management, work order management, change management, asset and configuration management) will be used to deliver IT services. Business Workflows will be used for delivering other internal service delivery areas requiring confidentiality within StatCan.

Risk Area Identification and Categorization

The PIA identifies the level of potential risk (level 1 is the lowest level of potential risk and level 4 is the highest) associated with the following risk areas:

Conclusion

This assessment of the Enterprise Service Management Solution (ESMS) did not identify any privacy risks that cannot be managed using existing safeguards.

2024 International Methodology Symposium registration
Statistics Canada
October 29 to November 1, 2024
statcan.symposium2024-symposium2024.statcan@statcan.gc.ca
Privacy notice

Registration procedures

External participants:

Participants who are not Statistics Canada employees can now register using the online registration form that can be accessed by selecting the “Register” button at the bottom of this page.

Statistics Canada employees:

Instructions will be sent to Statistics Canada employees closer to the Symposium.

The registration fee covers the following:

• Conference attendance
• Break refreshments
• Registration program, abstracts, and list of participants

Symposium 2024 Registration Fees (in Canadian dollars)

Registration deadline

The registration deadline for in-person conference attendance and workshops is September 30, 2024. Registration for online attendance will remain open until October 14, 2024. Participants are therefore invited to make sure to register before these dates.

Cancellation policy

Cancellations received in writing until September 30, 2024, will receive a full refund. After that date, no refunds will be made, but substitutions will be permitted. Please advise the registrar in advance of substitutions. However, if you do not cancel and do not attend, you will be charged the full registration fee. Travel and accommodation expenses are the responsibility of conference participants.

Register

Workshops will be held in person only on October 29, from 9:30am to 4:30pm.

Workshop 1

Smoothing based models using reproducible workflows in R – English session

Dr. Dave Campbell
Professor, Carleton University
https://people.math.carleton.ca/~davecampbell

Abstract:

In this workshop, we will introduce the use of Generalized Additive Models in R with emphasis on modern reproducible workflows that facilitate sharing and recycling efforts for use in new or updated datasets. Generalized Additive Models (GAMs) are a flexible regression tool that acts as an intermediary between linear regression and completely unconstrained function estimation from tools such as neural networks. GAMs are part of the inferential data science toolkit that allows a balance between ‘letting the data decide’ and exploiting expert insight into model curation.

Participants to this workshop will be introduced to reproducible workflows in R providing them with the ability to share results and automatically generate reports. In particular, a mathematical introduction to GAMs building on familiar tools from linear regression will be given. An overview of where these tools fit into the analytic toolbox and how they are combined into powerful predictive machines will also be discussed.

This workshop assumes only minimal experience with using R or a related data science coding language.

The workshop will be offered in English. The material will be available to participants in both official languages.

Biography:

Dr. Dave Campbell is a full Professor in the School of Mathematics and Statistics and the School of Computer Science at Carleton University in Ottawa. Academically, he runs a collaborative team researching inferential algorithms at the intersections of statistics with machine learning, computing, and applied mathematics to solve problems inspired by industry and government collaborations. He has co-authored discussion papers in Bayesian Analysis and the Journal of the Royal Statistical Society (series B) and been awarded over $3.5 million in research grants.

Dave’s career path maintains a theme of Industrial collaborations. He spent 2021-2023 period leading the inferential Data Science team at the Bank of Canada overseeing projects relating to cybersecurity, forecasting banknote demand, understanding drivers of inflation, and ensuring data privacy. Before moving to Ottawa in 2019, Dave was a Professor at Simon Fraser University, where he led the creation of their BSc in Data Science. He was the inaugural President of the Data Science and Analytics Section of the Statistical Society of Canada and was a co-organizer of the popular Vancouver Learn Data Science Meetup linking industry and academia.

It is an honor for us that Dr. Dave Campbell accepted our invitation to share his knowledge at a Symposium workshop! You can actually find him on LinkedIn: https://www.linkedin.com/in/drdavecampbell/

Workshop 2

Protecting the confidentiality of statistical data – French session

Dr. Anne-Sophie Charest
Professor, Laval University
https://www.fsg.ulaval.ca/corps-professoral/anne-sophie-charest

Abstract:

In this workshop, we will explore how to collect, analyze and share confidential data without disclosing personal information. We will look at the various risks associated with the use of personal data, as well as different ways of measuring these risks. In particular, we will consider differential privacy, an approach that has been the subject of much research and is now used in practice by some statistical agencies and private companies. We will explain the origin of this formal measure of confidentiality, look in detail at its mathematical definition and interpretation, and discuss the advantages and limitations of the approach. We will also discuss the use of synthetic datasets for privacy protection purposes: how to generate such datasets and assess their quality in terms of risk and utility. The content will be illustrated with R code, and part of the time will be set aside for participants to test the methods presented.

The workshop will be offered in French. The material will be available to participants in both official languages.

Biography:

Anne-Sophie Charest is an Associate Professor at Université Laval. She holds a PhD in Statistics from Carnegie Mellon University. Her research interests focus on the protection of the confidentiality of statistical data, including in the context of surveys or population census. She is particularly interested in the generation and analysis of synthetic datasets as well as the measurement of disclosure risk, particularly through the differential privacy framework.

It is an honor for us that Anne-Sophie Charest accepted our invitation to share her knowledge at a Symposium workshop! You can actually find her on LinkedIn: https://www.linkedin.com/in/anne-sophie-charest-900a585b/