Article written by: Amanda Hunter & Tracey P. Lauriault

The Organisation for Economic Co-operation and Development (OECD) recently published a policy response to COVID-19 in which they suggest that open science, and the policies & standards that support it, can accelerate the health, social, and economic responses to the virus as barriers to information access are eliminated.

As the first in a series of blog posts about Open Science (OS) and FAIR principles in Canada, here we highlight the key role open science plays in communicating and disseminating official COVID-19 research and public health data before assessing if official COVID-19 reporting in Canada adheres to OS principles.

In a next post, we will analyze official COVID-19 reporting in Canada to assess whether or not these follow Open Science, FAIR principles, and the Open Data Charter in the sharing of COVID-19 data.

What is Open Science?

The OECD Open Science program states that the benefits of open science is that it promotes a more accurate verification of scientific results, reduces duplication, increases productivity, and promotes trust in science.

https://www.oecd.org/science/inno/open-science.htm

Open science (OS) is a movement, a practice and a policy toward transparent, accessible, reliable, trusted and reproducible science. This is achieved by sharing how research and data collection are done so as to make research results accessible and standardized, created once and reused by many. This includes techniques, tools, technologies, and platforms should also be open source wherever possible.

In OS the outputs of the scientific process are considered to be a public good, thus wherever possible articles are published in open access (OA) journals, and research data are shared with the public and other scientists who may want to re-purpose those data in new work, or by people who want to verify the veracity of research results. Reporting COVID-19 Cases by normalizing an open by default approaches means that health scientists, population health experts and government officials make this part of their workflow (maintaining individual privacy of course), and by doing so decision makers beyond government, can scrutinize the results, leading to trust the results while also increasing data sharing.

What role does open science play in combating COVID-19?

In the early stages of the pandemic, knowing the genome provided crucial information to help scientists and researchers identify the origin of the outbreak, treat the infection, develop a diagnostic test and work on the vaccine. In other words, the easier—and quicker—researchers can produce, share and access scientific data, the quicker and the more informed is the collaborative response to the virus.

During the 2002-03 SARS outbreak it took five months to publish a full genome of the virus largely due to information blackouts and lack of data sharing. In contrast, the full genome of COVID-19 was published to an open-access platform nearly a month after the first patient was admitted to the hospital in Wuhan. This provided researchers around the world with a head start. Since OS policies have been operationalized during the pandemic, the resulting free flow of ideas in terms of biomedical research has accelerated (OECD).

The implementation of OS standards during COVID-19 has indeed been largely successful. OECD described how collaborative research and  thee global sharing of information reached unprecedented levels, for example:

  • In March 2020, 12 countries (including Canada) launched the Public Health Emergency COVID-19 Initiative at the level of Chief Science advisors, calling for open access to publications and machine-readable access to data related to COVID-19.
  • Open online platform Vivli offers an easy way to request anonymized data from clinical trials.
  • A COVID-19 Open Research Dataset [CORD-19] was developed that hosts 157,000 + scholarly articles about COVID-19 and related coronaviruses; 75,000 of which are full-text machine-readable data that can be used for AI and natural language processing.

These online, open-source platforms have supported rapid scientific COVID-19 research. OS, facilitated by standards, shared infrastructure and techniques, policies and licences, has been instrumental in the global fight against the pandemic.

Yet, despite the numerous successes, many challenges remain. For example, not all COVID-19 related health research and data adhere to the FAIR principles. FAIR principles are a standards approach which support the application of open science by making data Findable, Accessible, Interoperable, and Reusable. Failure to adhere to FAIR principles has led to an overall lack of communication and coordination during the pandemic. In Canada, data should also adhere to CARE principles, which address issues of Indigenous data governance with respect to Indigenous knowledge along with the OCAP Principles of the First Nation Information Governance Centre (FNIGC). More on this in the following section.

The reporting COVID-19 demographic data and reports in Canada to date falls short on standardized classifications in terms of demographics, as we discussed in an earlier blog post, which makes doing a comparative analysis difficult or impossible: for example, many countries define “recoveries” differently, and in Canada, since health is the jurisdictional responsibility of the provinces and territories, each report in their own way. Even though numerous official organizations publish COVID-19 and health related data, as open data databases or in open data portals, there remains an overall lack of interoperability, comparability and standards.

Where does Canada stand on Open Science?

Canada was implementing an open science framework before the pandemic as follows.

National Action Plan on Open Government

The Government of Canada recently published Canada’s 2018-2020 National Action Plan on Open Government, listing ten commitments to furthering the open government initiative. The plan asserts five commitments to implementing OS in Canada by the end of 2020, as seen below:

A screenshot showing a portion of Canada's 2018-2020 National Action Plan on  Open Government. The main issue addressed here is the difficulty for Canadians to access scientific research outputs: thus the commitments focus on making federal science, scientific data, and scientists themselves more accessible.

The OS portion of Canada’s 2018-2020 National Action Plan on Open Government. It aims to address the difficulty for Canadians to access scientific research: thus the commitments on making federal science, scientific data, and scientists themselves more accessible (Government of Canada, 2018).

The Action Plan addresses issues of accessibility and transparency of scientific research and outlines 5 commitments to amending these issues. These commitments include:

  1. Development of an OS roadmap,
  2. Providing an open access platform for publications,
  3. Raising awareness of federal scientists’ work,
  4. Promoting OS and soliciting feedback on stakeholder needs, and
  5. Measuring progress & benefits of the OS implementation.

Despite the comprehensiveness of the Roadmap (see below), Canada has not yet moved past the Action Plan’s second commitment—to provide a platform for Canadians to find and access open access (OA) publications from federal scientists—despite the projected March 2020 deadline. Also, at the time of writing, there is no federal open science platform or portal for users to access open science data in Canada even though there is an open data portal. The New Digital Research Infrastructure Organization (NDRIO) does show promise.

There are however some open data initiatives, such as the Federal Open Government and COVID-19 section on the Open Government Canada Portal.  Here Epidemiological and economic research data, with mathematical modeling reports, a map of cases and deaths by province, daily and weekly detailed epidemiological reports, and an ongoing dataset of COVID-19 cases, deaths, recoveries, and testing rates in Canada’s provinces and territories are made available. This is a significant improvement from the early days of reporting, as data journalist Kenyon Wallace discovered that on a daily basis, the Province of Ontario published new data but each time they did they overrode the previous day’s reports. His article and some work by Lauriault with the Ontario Open Government team resulted in changing that practice and raw data are now updated daily and reported. Open data is but one part of the OS process as we will see when we look at the FAIR principles.

Open Science Roadmap

The plan’s first commitment, to “develop a Canada Open Science Roadmap…” was completed and published in February 2020. The document provides ten recommendations made by Chief Science Advisor, Dr. Mona Nemer, to advance Canada’s OS initiatives. Like the policy brief by OECD, the roadmap is driven by the importance of trust among collaborators, inclusiveness of varying perspectives, and transparent processes throughout.

A screenshot of the cover of Canada’s Roadmap for Open Science (Government of Canada, 2020)

Canada’s Roadmap for Open Science (Government of Canada, 2020)

Most importantly, the Roadmap describes a commitment to developing an OS framework, including adopting the FAIR principles and “open by design and by default” specifications. The roadmap asserts Canada’s commitment to upholding these standards and policies via 10 recommendations:

10 recommendations made in the Roadmap for Open Science. Key points include the adoption of an OS framework in Canada, making federal scientific research outputs ‘open by default’, and implementing FAIR principles. (Government of Canada, 2020).

10 recommendations in the Roadmap for Open Science. Key points include the adoption of an OS framework, making federal scientific research outputs ‘open by default’, and implementing FAIR principles (Government of Canada, 2020).

Model Science Integrity Policy

Canada also has a Model Science Integrity Policy (MSIP) for the public service. The MSIP represents an internal commitment to integrity and accountability in science. Various mandates in the MSIP state that their purpose is to increase public trust in the credibility and reliability of government research and scientific activities, and ensure that research and scientific information are made available in keeping with the Government of Canada’s Directive on Open Government. The MSIP echoes Canada’s commitment to OS.

Indigenous Data Governance 

Finally, Canada has some commitment to supporting Indigenous rights to self-determination and data governance, but does not incorporate standards such as CARE principles which support OS  nor the OCAP Principles when it comes to Indigenous data governance. These extend the FAIR principles.

The Global Indigenous Data Alliance (GIDA) introduced the CARE principles to complement the FAIR principles in 2019. The CARE principles for Indigenous data governance were developed to address a lack of engagement between the open science movement and Indigenous rights and interests (GIDA, 2019).

The FAIR principles focus on data accessibility of data and sharing but fail to address power differences and the impact of colonialism experienced by Indigenous peoples and their right to exercise control and ownership of data about them and local and traditional knowledge. The CARE principles are crucial for the recognition and advancement of these rights as they encourage open science (and other ‘open’ movements) to “consider both people and purpose in their advocacy and pursuits” (GIDA, 2019). The CARE principles are contrasted with the FAIR principles in the below image from the GIDA website:

The CARE principles, which are “collective benefit, authority to control, responsibility, and ethics”, contrasted with the FAIR principles, which are “findable, accessible, interoperable, and reusable” (GIDA, 2019)

The CARE principles are “collective benefit, authority to control, responsibility, and ethics”, contrasted with the FAIR principles, which are “findable, accessible, interoperable, and reusable” (GIDA, 2019)

The OCAP Principles of Ownership, Control, Access and Possession are another set of important principles, that are a better fit in the Canadian Context.  Members of our project currently taking the Fundamentals of OCAP course and we hope to better incorporate these approaches in our work and in how we assess official reporting. Though Indigenous data governance and handling of Indigenous knowledge are not addressed in the Open Science Roadmap, the Data Strategy Roadmap for the Federal Public Service does demonstrate a federal approach to supporting Indigenous data strategies (see below):

Recommendation #8 from the Data Strategy Roadmap for the Federal Public Service which states Canada’s recognition of the Indigenous right to self-determination and data governance (Government of Canada, 2019)

Recommendation #8 from the Data Strategy Roadmap for the Federal Public Service which states Canada’s recognition of the Indigenous right to self-determination and data governance (Government of Canada, 2019)

Next Steps

Much progress has been made in terms of publishing, reporting and communicating data in the short time since COVID-19 began (though not without pressure from the media!). Open access to scientific research and public health reports have been helpful to facilitate the rapid response to the virus and keeping the public informed on how science informs governments actions. There is, however, much left to be done.

  1. Open Science should consider bias in data as well as invisibilities for example interdisciplinary work that helps paint the fuller picture of the impact of the virus. For example, interdisciplinary and intersectional approaches to data categories, including research based in critical race theory (CRT), Indigenous perspectives, socio-demographics and gig labour groups for example.
  2. Second, as suggested by the OECD, making COVID-19 data Findable, Accessible, Interoperable and Reusable is critical for a more effective rapid response. Lack of adherence to FAIR principles currently presents challenges to open science research.
  3. Finally, a meaningful Canadian OS framework should also incorporate standards for Indigenous Data Governance such as CARE Principles and OCAP Principles ensure respectful data practices are followed.

The Tracing COVID-19 Data team is in the process of developing a framework to assess official COVID-19 reporting in Canada to see if they comply with OS, FAIR, CARE, OCAP, and open-by-default at all levels of government. We will draw on Canada’s commitments OS and FAIR in – Canada’s 2018-2020 National Action Plan on Open Government, Open Science Roadmap, the Model Science Integrity Policy and the Open Data Charter.

Is Canada FAIR?

Stay tuned!

Recommendation

All official Federal, Provincial/Territorial and City public COVID-19 data reporting should be open data, open by design and by default, research should be published in open access (OA) Journals and should adhere to open science (OS) such as the FAIR principles , CARE Principles, OCAP Principles and the Open Data Charter.

References

Canadian Internet Policy and Public Interest Clinic. Open Data, Open Citizens? https://cippic.ca/en/open_governance/open_data_and_privacy

Centres for Disease Control and Prevention. (n.d.). SARS- Associated Coronavirus (SARS-CoV) Sequencing. https://www.cdc.gov/sars/lab/sequence.html

CTVNews. (2020). Project Pandemic: Reporting on COVID-19 in Canada. 
https://www.ctvnews.ca/health/coronavirus/project-pandemic

Federated Research Data Repository. (2018). FAIR Principles. 
https://www.frdr-dfdr.ca/docs/en/fair_principles/

Global Indigenous Data Alliance. (2019). CARE Principles for Indigenous Data Governance. 
https://www.gida-global.org/care

Government of Canada. (2014). Directive on open government. 
https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=28108

Government of Canada. (May, 2016). Open by default and modern, easy to use formats. 
https://open.canada.ca/en/content/open-default-and-modern-easy-use-formats

Government of Canada. (2017). Model policy on scientific integrity.
https://www.ic.gc.ca/eic/site/063.nsf/eng/h_97643.html

Government of Canada. (2018). Canada’s 2018-2020 National Action Plan on Open Government. https://open.canada.ca/en/content/canadas-2018-2020-national-action-plan-open-government#toc8

Government of Canada. (2018). Report to the Clerk of the Privy Council: A Data Strategy Roadmap for the Federal Public Service. https://www.canada.ca/content/dam/pco-bcp/documents/clk/Data_Strategy_Roadmap_ENG.pdf

Government of Canada. (2020). Coronavirus disease (COVID-19): Outbreak update.
https://www.canada.ca/en/public-health/services/diseases/coronavirus-disease-covid-19.html?utm_campaign=not-applicable&utm_medium=vanity-url&utm_source=canada-ca_coronavirus

Government of Canada. (2020). Office of the Chief Science Advisorhttps://www.ic.gc.ca/eic/site/063.nsf/eng/h_97646.html

Government of Canada. (2020). Open Government Portal.
https://open.canada.ca/data/en/dataset

Lauriault, T. (2020, April 17). Tracing COVID-19 Data: COVID-19 Demographic Reporting. Datalibre.
http://datalibre.ca/2020/04/17/covid-19-demographic-reporting/

National Centre for Biotechnology Information. (2020). Public Health Emergency COVID-19 Initiative.
https://www.ncbi.nlm.nih.gov/pmc/about/covid-19/?cmp=1

Open Data Charter. (n.d.). The International Open Data Charter.
https://opendatacharter.net

Organisation for Economic Co-operation and Development. (2020, May 12). OECD Policy Responses to Coronavirus (COVID-19): Why open science is critical to combatting COVID-19.
http://www.oecd.org/coronavirus/policy-responses/why-open-science-is-critical-to-combatting-covid-19-cd6ab2f9/

Ford & Airhihenbuwa. (2010). The public health critical race methodology: Praxis for antiracism research. Science Direct.
https://www.sciencedirect.com/science/article/abs/pii/S0277953610005800#!

Semantic Scholar. (2020). CORD-19: COVID-19 Open Research Dataset.
shorturl.at/wETZ5 

The Lancet. (January, 2020). Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding.
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30251-8/fulltext

The National Academies of Science, Engineering, Medicine. (2018). Open Science by Design: Realizing a Vision for 21st Century Research. Chapter 1, Front Matter. 
https://www.nap.edu/read/25116/chapter/1

The Star. (2020). Coronavirus & COVID-19 Data. https://www.thestar.com/coronavirus/data.html

Vivli. (2020).
https://vivli.org/about/overview-2/

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Article written by: Kit Chokly & Tracey P. Lauriault

The phrase “flatten the curve” has recently come into the vernacular to encourage slowing the spread of COVID-19. The concept comes from the Centre for Disease Control (CDC) report (p.18), where the curve renders the daily number of COVID-19 infections on the Y-axis and the day these are counted on X-axis of a line chart. A flatter curve visually depicts when the illness is under control, while a higher peak curve emerges when the health case system becomes overwhelmed and instances of the virus are high. Together we work to “flatten the curve” by following the recommendations of our Chief Medical Officers of Health.

This is not the only visual metaphor used to refer to COVID-19, however; the “second wave” has also been used to describe the spread of COVID-19. This term refers to another peak in the line chart that may appear as restrictions are lifted and cases spike as a result. By visualizing health data, we can see the spread of COVID-19 and set public health goals through the peaks and valleys of a line chart.

In addition to demonstrating a growing public awareness of the pandemic, the popularity of these phrases shows the importance of data visualization in understanding—and thus managing and communicating—the virus. Following this prevalence, many official sources now share live data to track and communicate COVID-19 data. John Hopkins University, for example, rendered COVID-19 datasets into the first global and US dashboards. Health Canada provides an interactive map of Canada featuring case counts and rates. Some city public health departments such as Ottawa Public Health have also launched dashboards to communicate the current status of local COVID-19 cases.

These dashboards get complex information across quickly and include many types of indicators. For instance, as seen below, the City of Ottawa dashboard includes key indicators such as the number of COVID-19 cases, hospitalization levels, and other case data. These are depicted in a cumulative curve of confirmed cases, their rate across time, bar charts of cases reported by age, and a pie chart that shows cases by gender. Visualizing these data in a dashboard makes it easy to see patterns and trends.

A screenshot of the city of Ottawa’s COVID-19 dashboard, which effectively condenses complex data on COVID-19 cases and outcomes into bite-sized visualizations (Ottawa Public Health, 2020).

A screenshot of the city of Ottawa’s COVID-19 dashboard, which effectively condenses complex data on COVID-19 cases and outcomes into bite-sized visualizations (Ottawa Public Health, 2020).

While the city and all the other public health agencies across Canada should be lauded for their dashboards, these, like all other demographic reporting systems, suffer from a lack of a nuanced and intersectional approach to collecting and reporting data. For example, this dashboard alongside those just mentioned do not report how COVID-19 affects people of low socio-economic status. They do not include how living in a densely populated area may also influence health outcomes. There is also little to no data on how COVID-19 may disproportionately affect racialized groups such as Indigenous, Black, and other people of colour. And most importantly, they do not demonstrate how all of these variables intersect. As Jamie Bartlett and Nathaniel Tkacz (2017) explain,

“Like all visualisations of data, dashboards necessarily distort the information that they are attempting to present neutrally by defining how a variable is to be understood and by excluding any data which isn’t compatible with this definition” (p. 15).

As Maggie Walter and Chris Andersen (2013) discuss in their book Indigenous Statistics, data dashboards—like all data and technological systems—are more than neutral numerics as they play,

“a powerful role in constituting reality through their underpinning methodologies by virtue of the social, cultural, and racial terrain in which they are conceived, collected, analyzed, and interpreted” (p. 9).

They also emphasize that there is a distinction to be made between groups of people being “statistical creations based on aggregated individual-level data, rather than ‘real world’ concrete groups” (Walter & Andersen, 2013, p.9).

It is not uncommon that dashboards miss these subtleties. We are only just coming to terms with the idea that data are not neutral objects, and that data dashboards are cultural artifacts. As such, they reflect the limitations of the normative values of the institutions that create them, of our health data collection systems, and of this very form of communication.

Overcoming these limitations means that we can improve the information environment of decision makers to target limited resources when and where they are needed most, while not reinforcing nor perpetuating inequality. Making things visible makes them actionable. Doing so also prevents the deep systemic societal problems that have led to such poor health outcomes for some during the pandemic. Below, we consider some ideas on how to re-think data and data dashboards by framing their creation with an intersectional design approach.

What is intersectionality?

Intersectionality is a term conceptualized by Black feminist legal scholar Kimberlé Crenshaw in 1989. The concept has earlier roots in Black feminism and builds on the work and struggles of Sojourner Truth, Ida B. Wells, Louise Thompson Patterson, and Audre Lorde (to name only a few). Intersectionality refers to the notion that different forms of oppression interact with and multiply each other, demonstrating their inseparability. Health scientists Chandra Ford and Collins Airhihenbuwa (2010) write:

“Intersectionality posits that social categories (e.g., race, gender) and the forms of social stratification that maintain them (e.g., racism, sexism) are interlocking, not discrete” (p. 1396).

These scholars and social justice leaders emphasize that rather than adding these co-occurring categories together in public health research, it is essential that the interactions between categories are also considered (Ford & Airhihenbuwa, 2010). As an example of intersectional public health, Ford and Airhihenbuwa (2010) describe the importance of considering race alongside gender and sexuality when approaching HIV risk behaviours because of the way “racism operates via gendered and sexualized proscriptions” (p. 1394).

How does intersectionality relate to the Tracing COVID-19 Data project?

Epidemiologist Greta Bauer (2014) describes how datasets about public health and marginalized populations tend to only examine a singular axis of oppression, even though intersectional data reporting can help reduce health inequalities.

One of the aims of the Tracing COVID-19 Data project is to identify these asymmetries in COVID-19 data reports, as well as rapidly and effectively mobilizing this knowledge and communicating our findings with decision makers. As demonstrated by the growing use of dashboards in reporting COVID-19 data, data visualization can be a useful communication tool. We thus ask:

How can we communicate COVID-19 data in a way that improves health outcomes for all? How can we use data visualization techniques to communicate intersectional issues effectively?

We aim to provide answers to questions such as these for those responsible for official COVID-19 reporting. Our hope is that our recommendations will lead to an intersectional approach to communicating COVID-19 impacts and health outcomes.

How have people tried to visualize intersectionality already?

Visual metaphors are useful to describe intersectionality. Crenshaw (1989) herself uses the metaphor of the traffic intersection, writing:

“Discrimination, like traffic through an intersection, may flow in one direction, and it may flow in another. If an accident happens in an intersection, it can be caused by cars traveling from any number of directions and, sometimes, from all of them. Similarly, if a Black woman is harmed because she is in the intersection, her injury could result from sex discrimination or race discrimination (p. 149).”

In another metaphor, provided by Black feminist scholar Patricia Hill Collins (1990) is the “matrix of domination”. Here, she uses a multi-level matrix to describe how oppression—and privilege—must be understood through an interlocking structural model (Hill Collins, 1990).

More recently, communications scholars Jenna Abetz and Julia Moore (2019) describe how visual metaphors for intersectionality often focus on centralizing difference and are often conceptualized as linear. They suggest the use of fractals—repeating and irregular geometric patterns—as a metaphor for the scalability and recursion of oppression (Abetz & Moore, 2019).

An example of a von Koch curve fractal from Abetz and Moore’s (2019) article, which they use as a metaphor to explore the scalability and recursion of oppression.

An example of a von Koch curve fractal from Abetz and Moore’s (2019) article, which they use as a metaphor to explore the scalability and recursion of oppression.

The issue with metaphors

While these visual metaphors are extremely useful to conceptualize intersectionality, Bauer (2014) points to the issue with their use in quantitative research. She writes:

“Interestingly, quantitative applications of intersectionality can be obfuscated by the predominance of mathematical-like language in intersectionality theory, though its use there is conceptual rather than strictly mathematical” (Bauer, 2014, p. 12).

While illustrations of traffic intersections, graphic matrices, and fractal patterns can be used to explain intersectionality, they do not easily map onto the visualization of quantitative data in a data dashboard and can actually obscure the meanings of these data. They may, however, be useful instruments to help model which data should be collected and rendered visually.

Feminist Data Visualization

To address this issue, data scientists Catherine D’Ignazio and Lauren Klein (2016) suggest applying feminist theory to data visualization. Rather than resign ourselves to the limitations of current dashboards, feminist data visualzation offers the possibility of “challeng[ing] the validity of a variety of binaristic and hierarchical configurations” (D’Ignazio & Klein, 2016, p. 1). This includes non-intersectional data analysis.

D’Ignazio and Klein (2016) thus suggest that data visualization begins with the way data are collected and organized—even before they are visualized.

This means starting with the evidence collected and finding the best way to get the stories they tell across. This could mean enlisting the help of metaphors; however, it is important to find the stories from the data first and then use metaphor to communicate them as effectively as possible.

To find and interpret these data stories, D’Ignazio and Klein (2016) offer six starting principles:

  1. Rethink binaries
  2. Embrace pluralism
  3. Examine Power and Aspire to Empowerment
  4. Consider Context
  5. Legitimize Embodiment and Affect
  6. Make Labor Visible

Finding and using intersectional data

Despite the lack of intersectional data in current COVID-19 dashboards, a number of organizations are already making efforts to find and use intersectional data. The Data Standards for the Identification and Monitoring of Systemic Racism, produced by the Ontario Anti-Racism Directorate, not only takes an intersectional approach to the collection of data and data characteristics, but uses it to identify and monitor systemic racism. These ideals align with the Research, Evaluation, Data Collection, and Ethics (REDE) Protocol for Black Populations in Canada Protocol (or the REDE4BlackLives Protocol for short), which also suggests that these data should be part of ongoing conversations in pre-existing communities.

This is not unlike the work of:

The cover of the First Nations Data Governance Strategy, produced by the FNIGC as a response to direction received from First Nations leadership.

The cover of the First Nations Data Governance Strategy, produced by the FNIGC as a response to direction received from First Nations leadership.

A graphic from the Global Indigenous Data Alliance (2019) encouraging the use of CARE principles to encourage Indigenous data sovereignty alongside Wilkinson et al’s (2016) FAIR open data principles.

A graphic from the Global Indigenous Data Alliance (2019) encouraging the use of CARE principles to encourage Indigenous data sovereignty alongside Wilkinson et al’s (2016) FAIR open data principles.

The Tracing COVID-19 Data project is working to bring together some of these datasets as they pertain to COVID-19. For example, with the help of Aidan Battley, we are looking into social models of disability and data such as the International Classification of Functioning, Disability and Health (ICF) by the World Health Organization (WHO). These models and data fall well into the intentions of this project, which is to encourage technological citizenship and a rights based approach to data during a crisis. Alongside collecting these data, visualization is key to reach our goals.

But how do we align these principles, protocols, standards, and practices? How might one model these in a population health data system? And how should these data be rendered visually? These are challenges worth pursuing, as lives are quite literally on the line.

What efforts might be useful to adapt for the Tracing COVID-19 Data project?

Following these principles, protocols, standards and practices, the Tracing COVID-19 Data project is critically thinking about data and data systems. We are currently collecting data from all over the country, including data on the way location, age, ability, race, Indigeneity, gender, and income may intersect with COVID-19 outcomes. From there, we aim to develop a series of rapid and archivable visualizations and blog posts to communicate our findings in ways best suited to both researchers and the communities they describe, as well as provide recommendations to decision makers on how to improve their data dashboards and other data visualizations techniques.

We are also mindful of becoming flexible and adaptable to new solutions (and issues) as they arise. The work of visualizing the intersectional impacts of COVID-19 is important, but loses its value if it becomes too brittle to be used effectively. We have already heard words of caution from racialized communities who rightfully fear becoming further stigmatized by being described through COVID-19 data. Listening to and working with the communities who are being multiply impacted by COVID-19 is critical to the success of this project. For this work to truly be intersectional, it is essential that we are all on board to listen and work together.

References, Links, & Resources

Abetz, J., & Moore, J. (2018). Visualizing intersectionality through a fractal metaphor. In J. Dunn & J. Manning (Eds.), Transgressing Feminist Theory And Discourse (1st ed., pp. 31–43). Routledge. https://doi.org/10.4324/9781351209793-3

Bartlett, J., & Tkacz, N. (2017). Governance by Dashboard. https://core.ac.uk/download/pdf/80851285.pdf

Bauer, G. R. (2014). Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Social Science & Medicine, 110, 10–17. https://doi.org/10.1016/j.socscimed.2014.03.022

Centre for Disease Control. (2007). Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States. 97. https://www.cdc.gov/flu/pandemic-resources/pdf/community_mitigation-sm.pdf

Columbia Law School. (2017, June 8). Kimberlé Crenshaw on Intersectionality, More than Two Decades Later. https://www.law.columbia.edu/news/archive/kimberle-crenshaw-intersectionality-more-two-decades-later

Crenshaw, K. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics. University of Chicago Legal Forum, 1989(1), 31. https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1052&context=uclf

D’Ignazio, C., & Klein, L. F. (2016). Feminist Data Visualization. 5. http://www.kanarinka.com/wp-content/uploads/2015/07/IEEE_Feminist_Data_Visualization.pdf

First Nations Information Governance Centre. (2020). The First Nations Information Governance Centre. https://fnigc.ca/index.php

Ford, C. L., & Airhihenbuwa, C. O. (2010). The public health critical race methodology: Praxis for antiracism research. Social Science & Medicine, 71(8), 1390–1398. https://doi.org/10.1016/j.socscimed.2010.07.030

Fundamentals of OCAP®. (2020). The First Nations Information Governance Centre. https://fnigc.ca/training/fundamentals-ocap.html

Gilyard, K. (2017). Louise Thompson Patterson: A Life of Struggle for Justice. Duke University Press. https://www.dukeupress.edu/louise-thompson-patterson

Government of Canada. (2020, July 8). Coronavirus disease (COVID-19): Outbreak update. https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

Government of Ontario. (2016, June 28). Anti-Racism Directorate. https://www.ontario.ca/page/anti-racism-directorate

Government of Ontario. (2019, August 27). Data Standards for the Identification and Monitoring of Systemic Racism. https://www.ontario.ca/document/data-standards-identification-and-monitoring-systemic-racism

Government of Ontario. (2020, July 8). COVID-19 (coronavirus) in Ontario. https://covid-19.ontario.ca/

Greenwood, F., Howarth, C., Poole, D. E., Raymond, N. A., & Scarnecchia, D. P. (2017). The Signal Code: A human rights approach to information during crisis. Harvard Humanitarian Initiative. https://hhi.harvard.edu/publications/signal-code-human-rights-approach-information-during-crisis#:~:text=The%20Signal%20Code%20is%20the,have%20to%20information%20during%20disasters.

Hill Collins, P. (1990). Black feminist thought: Knowledge, consciousness, and the politics of empowerment. Unwin Hyman. http://www.hartford-hwp.com/archives/45a/252.html

John Hopkins University. (2020, July 8). COVID-19 Map—Johns Hopkins Coronavirus Resource Center. https://coronavirus.jhu.edu/map.html

Lauriault, T. (2020, June 1). Tracing COVID-19 Data: Data and Technological Citizenship during the COVID-19 Pandemic. Datalibre. Retrieved 8 July 2020, from http://datalibre.ca/2020/06/01/tracing-covid-19-data-data-and-technological-citizenship-during-the-covid-19-pandemic/

Lorde, A. (1981). The Uses of Anger: Women Responding to Racism. BlackPast. https://www.blackpast.org/african-american-history/speeches-african-american-history/1981-audre-lorde-uses-anger-women-responding-racism/

National Underground Railroad Freedom Center. (n.d.). Ida B. Wells. Retrieved 8 July 2020, from https://freedomcenter.org/content/ida-b-wells

Ottawa Public Health. (2020, July 8). Daily COVID-19 Dashboard. https://www.ottawapublichealth.ca/en/reports-research-and-statistics/daily-covid19-dashboard.aspx

Podell, L. (n.d.). The Sojourner Truth Project. The Sojourner Truth Project. Retrieved 8 July 2020, from https://www.thesojournertruthproject.com

Research Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). CARE Principles for Indigenous Data Governance. The Global Indigenous Data Alliance. https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab221ce848fb2/1571419335217/CARE+Principles_One+Pagers+FINAL_Oct_17_2019.pdf

The First Nations Information Governance Centre. (2020). A First Nations Data Governance Strategy. https://fnigc.inlibro.net/cgi-bin/koha/opac-retrieve-file.pl?id=9c677f3dcf8adbf18fcda96c6244c459

The Protocol: REDE4BlackLives. (n.d.). REDE4BlackLives. Retrieved 8 July 2020, from https://rede4blacklives.com/the-protocol/

Walter, M. (2013). Indigenous statistics: A quantitative research methodology. Left Coast Press. https://www.routledge.com/Indigenous-Statistics-A-Quantitative-Research-Methodology/Walter-Andersen/p/book/9781611322934

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18

World Health Organization. (2018, March 2). International Classification of Functioning, Disability and Health (ICF). https://www.who.int/classifications/icf/en/

World Health Organization. (2020). https://www.who.int

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We were invited today to share the work we are doing on the Tracing COVID-19 Data project to the Ottawa Local Immigration Partnership (OLIP) Health and Wellbeing Sector Table Meeting. This is an amazing group of dedicated actors from the Social Sector, the City, Health Sector, Education Sector, Police and Equity seeking groups that work toward the promotion of equality in Ottawa.  One of their Theory of Change Domain Areas is Equity Data, and you can read more about OLIP’s work here.

You can access the slides here.

Screenshot (77)

 

I was just awarded a small but not insignificant award as part of the Carleton University COVID-19 Rapid Response Research Grants. Below is a description of what I will be up to, along with some great students and expert advisors.  I will share everyone’s names later.  Results of the work will be published here as it becomes available!  Stay tuned. Also, let me know if you want to contribute in any way! Tracey dot Lauriault at Carleton dot CA

Research Summary

There is much official COVID-19 data reporting by federal, provincial, territorial and Indigenous Communities. As the pandemic evolves, and more information comes to light, there is a call to add data attributes about Indigenous, Black and Racialized groups and of the affected labour force, and to report where cases predominate. The pandemic also revealed that foundational datasets are missing, such as a national list of elder care homes, maps of local health regions and data about the digital divide. This project will embrace technological citizenship, adopt a critical data studies theoretical framework and a data humanitarian approach to rapidly assess data shortfalls, identify standards, and support the building of infrastructure. This involves training students, conducting rapid response research, developing a network of experts, learning by doing and a transdisciplinary team of peer reviewers to assess results. The knowledge will be mobilized in open access blog posts, infographics, policy briefs and scholarly publications.

Research challenge:

Official COVID-19 public heath reports by Federal, Provincial, and Territorial (F/P/T) and First Nation Communities are uneven and there are calls to improve them ( 1 CBC News, Toronto Star). Asymmetries can be attributed to dynamically evolving challenges associated with the pandemic, such as working while practicing social distancing; jurisdictional divisions of power in terms of health delivery; and responding to a humanitarian crisis, where resources are stretched and infrastructures are splintered (i.e. digital divide, nursing home conditions).

The Harvard Humanitarian Initiative (HHI) developed a rights-based approach to the management of data and technologies during crisis situations which includes the right to: be informed, protection, privacy and security, data agency and rectification and redress (2). These apply to contact tracing (3 ITWorld, Scassa) and to equity groups calling for demographic data (1). Other have conducted rapid response data reporting, for example after the Haiti Earthquake volunteers developed real-time crowdsourcing data collection systems to support humanitarian responders (4 Meier) and WeRobotics mobilizes local drone expertise to objectively assess proposed pandemic response technologies (5 WeRobotics).

This research will apply a critical data studies (CDS) theoretical framework (6 Kitchin & Lauriault), the principles of the HHI and, practice technological citizenship (7 Feenbert) to the study of the Canadian COVID-19 data response. Lauriault will leverage her expertise and Canadian and international network of open data, open government, civic technology experts in government, civil society, and Indigenous Communities (see CV) as seen in the policy briefs published on DataLibre.ca (8) to rapidly assess and support COVID-19 data management and reporting.

The objective is to carry out the following activities:

  1. Compare official COVID-19 public health data reports to identify gaps and best practices (9 Lauriault & Shields).
  2. Identify and support the building of framework datasets to standardize reporting (10 Lauriault).
  3. Analyze data standards and protocols to support data management, interoperability and cross-jurisdictional reporting (11 GeoConnections).
  4. Publish case-studies, resources, an archives of official reporting, and a glossary and
  5. Rapidly conduct expert analysis, peer review, knowledge mobilization and provide evidence-based recommendations to improve data reporting.

The rationale for this research is as follows:

  1. Official COVID-19 public health data are inconsistently reported, impeding comparability, and the ability to assess impact and target actions. Also, predictions missed seniors’ homes, precarious labour, and Indigenous communities and social determinants (12 Global News, NCCDH), resulting in an increase in cases and deaths. Currently job classifications and Indigenous, Black, and Racialized people classifications (13 CTV News) remain absent. This research will create a corpus of F/P/T and Indigenous Communities’ official reports, compare results, identify gaps.
  2. Framework data are standard information infrastructures upon which other analysis can consistently be done (14 Toronto Star). When this is lacking analysis is impeded, for example there is no national reporting by health region since no national framework dataset exists (15 Lauriault), and mitigating the digital divide is thwarted with a lack of broadband maps (16 Potter & Lauriault et al.). Other missing national datasets include senior care facilities, homeless shelters, precarious labour, and Indigenous Communities (17 Gaetz et al.). Needed framework datasets will be identified and if necessary coordinate their building (18 SPCOStatCan LODE), advocacy for the opening of public datasets such as corporate registries may be carried out (19 Fed. Registry,  Open Corporate, Open Contracting), and experts from public health , social planning, and Indigenous Communities will help identify localized frameworks.
  3. Consistent COVID-19 reporting requires an interoperable infrastructure which builds upon standards developed through consensus processes (20 CIHI, PHAC). Current uneven reporting may be attributed to a lack of standards adoption and formalization in terms of data flows. This research will develop a repository of standards and protocols and share these with decision-makers to improve interoperability (i.e. Data Standards for the Identification and Monitoring of Systemic Racism (21 ON Govt) and FNIGC OCAP Principles (22 FNIGC)).
  4. Rapidly mobilizing knowledge is important to improve reporting and manage data, and to build a crisis data reporting infrastructure for the future. This project will compile, and archive information, rapidly assess and peer review results with experts and report results on DataLibre.ca and other websites, will produce infographics and policy briefs, deliver online webinars, and help administrators and Indigenous Communities improve their data and technology policies.

A CDS framework recognizes that data have social and material shaping qualities and that they are never politically neutral while also being inseparable from the people and institutions who create them including practices, techniques, and infrastructures. This involves a team of data, technology, legal, social and health, and Indigenous experts to rapidly assess official COVID-19 data assemblages and to act as technological citizens by applying knowledge in real time and mobilize results to mitigate the data shortfalls witnessed during this crisis and support decision makers to respond with a data humanitarian and rights-based approach for now and to better respond in the future.

Expected Impact:

The target audience for this rapid response data and technology reporting is F/P/T public officials and Indigenous Community Leaders who manage public health, socio-economic, statistical and official record data flows; and civil society actors and the public involved in open data, open government and open contracting, transparency and accountability. This includes C-class executives, chief technology, information data, and digital officers.

The outcome of this research is to standardize and improve humanitarian crisis data management and data reporting in the short term to ensure consistent reporting, and in the long term establish standardized data workflows and operationalize data infrastructures for this pandemic in preparation for the next.

The timing to compile, inventory and build an open access archives of official data reporting is now as the fractures in the system have become apparent in real-time and have had negative consequences. It is important to monitor the response as it evolves so as to be able to improve it while our collective institutional memory is fresh and to have the evidence available as a reminder for if and when we forget, but also to build more robust systems.

The results of this research will be continuously reported and made openly accessible as it becomes available and will lead to the formation of a new research team.

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TracingCOVIDbanners-08The following data and information were collected and analyzed by Tracey P. Lauriault, and Sam Shields a recent Carleton University Critical Data Studies graduate.

We set out to answer a very simple question inspired by a Twitter stream calling for COVID-19 reporting to include Indigenous, Black and Racialized characteristics. The following guided our activities:

  • What kind of demographic data are reported in official COVID19 reports?

On Thursday April 16, 2020 we spent the day searching the content of official government COVID-19 reporting sites. We compiled our data into a Google Spreadsheet, conferred over Skype, chatted in FB, and verified each other’s work. Official COVID-19 reporting dynamically changes as the pandemic evolves, and as institutions collect more data and build the capacity to report, they report more and they do so in a better way. I also consult experts in my network who comment and suggest resources. We will take another look next week to see if anything has changed. The following were our data sources

  1. British Columbia: COVID Dashboard & BCCCD PHSA Surveillance Report (15/04/2020)
  2. Yukon: Information about COVID-19
  3. Alberta: COVID-19 in Alberta
  4. North West Territories: Coronavirus Disease (COVID-19)
  5. Saskatchewan: Cases and Risk of COVID-19 in Saskatchewan
  6. Manitoba: COVID-19 Updates
  7. Nunavut: COVID-19 (Novel Coronavirus)
  8. Ontario: The 2019 Novel Coronavirus (COVID-19) Status of cases in Ontario & Daily Epidemiologic Summary (15/04/2020)
  9. Québec: Données COVID-19 au Québec & Situation du coronavirus (COVID-19) au Québec
  10. New Brunswick: COVID-19 Testing by the Numbers
  11. Prince Edward Island: PEI COVID-19 Testing Data
  12. Nova Scotia: Novel coronaviris (COVID-19) cases in Nova Scotia: data visualization
  13. Newfoundland: Newfoundland and Labrador Pandemic Update Data Hub
  14. Federal: PHAC Coronavirus disease (COVID-19): Outbreak update & Full Daily Epidemiology Update (April 16, 2020)

We found an incredible amount of information and overall, each province, territory and the Federal government make their data readily available and these are disseminated in charts, tables, maps, and dynamic dashboards and in daily surveillance reports. The data and indicators are explained, and data sources are generally provided.

In terms official COVID-19 reporting, there was very little reporting cases and outcomes with demographic variables and when there was, it is not standardized, making it difficult to do any national comparative analysis.  Below is what we found.

1. Age

  • COVID-19 Cases by Age were reported by all provinces and the Federal Government. Age was not reported by all 3 Territories.
  • Those who did report, provided case counts and some percentages.
  • Only British Columbia, Alberta and Quebec reported Deaths by age groups.
  • Quebec reports age in 4 different ways.
  • There are no Age Range Reporting standards, and this impedes comparability.

The following is how COVID-19 Age data are reported, we ordered the results by similar reporting styles.

  • British Columbia: <10, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+, Unknown
  • New Brunswick: <10, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-80, 90+
  • Manitoba: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99, 100+
  • Quebec: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+, Unknown
  •                 0-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+
  •                 30-49, 50-69, 70-79, 80-89, 90+
  •                 <30, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90, Unknown
  • Alberta: <1, 1-4, 5-9, 10-19, 20-29, 30-39 ,40-49, 50-59, 60-69, 70-79, 80+
  • Saskatchewan: <19, 20-44, 45-65, 65+
  • Ontario: <19, 20-39, 40-59, 60-79, 80+
  • Federal: 19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+
  • Nova Scotia: 0-19, 20-44, 45-64, 65+
  • PEI: <20, 20-39, 40-59, 60-79, 80+
  • Newfoundland: <20, 20-39, 40-49, 50-59, 60-69, 70+
  • Yukon:  No Reporting by Age
  • North West Territories: No Reporting By Age
  • Nunavut: No Reporting By Age

Age range variable reporting recommendations:

a) Standardize age ranges reporting systems across jurisdictions to enable comparison.

b) Social-determinant of health variables, such as occupation, income, the type of dwelling a person lives in, where one lives, are variables being reported as being related to COVID-19. The Census reports age by quintile although it start at 0-14, in Canada vital statistics are reported by age quintile and the World Health Organization (WHO) also reports by quintile. Linking to other aggregated demographic, health and vital statistical data can inform the planning, and the managing of health outcomes.

2. Sex

  •  Sex is Not reported as a COVID-19 attribute, by 4 Canadian jurisdictions, namely the Territories and  Newfoundland and Labrador.
  • For jurisdictions that do report COVID-19 data by sex, only binary classifications are used, Female and Male.
  • Only British Columbia, Alberta and Manitoba report Sex and Age as attributes.
  • Only Quebec and The Federal Government report Sex and Death.

Sex Variable Reporting Recommendations:

a) It is advisable to report COVID-19 indicators by sex such as Female, Male and Gender Diverse.

b) Sex disaggregated data are important in terms of informing testing; health interventions and it is associated with health outcomes. Knowing can inform planning.

c) Reporting age and sex is important as these are distinguishing characteristics in vital statistics, health, wellbeing, for longevity and death rates.  Also, reports suggest that the virus affects men more negatively than it does women, especially older men. In terms of the labour force and COVID-19, nurses, doctors, elder care and home care professionals, those who work with people who live in group homes for the disabled and provide home care for these people, and people who clean these places tend to be women. Higher numbers of women are becoming afflicted by COVID-19 in Canada and this may be associated with their occupations. Age and sex are standard labour force statistical variables and reporting these attributes with COVID-19 will inform if health outcomes are related to those attributes.

3. Labour Classification

  • In official COVID-19 reporting, only the Provinces of Saskatchewan and Quebec reported any labour category and respectively they reported Case Counts for Health Care Workers for Saskatchewan and Cases Count and Death Count of Staff in hospitals and long-term care homes for Quebec.

Labour Force Reporting Recommendations:

a) Canadian Labour Forces Characteristics such as employed full or part-time, and the North American Industry Classification System and National Occupation Classification (NOC) system are standardized. For example, see the NAICS Health Care and Social Services or the classification and search for cleaner in NOCS.

b) The Canadian Institute for Health Information (CIHI) health workforce database includes standardized job classifications and data tables by job classification. They also have methodological guides comparing provincial systems. Harmonizing classifications across the provinces and the territories would go a long way to facilitating comparable analysis.

4. Indigenous, Black and Racialized People

  • No official government COVID-19 sites report data by any of these groups.
  • Race and ethnicity may or may not biologically predispose people to COVID-19 health outcomes.  We are assuming that these data are being tracked but are not reported as there is a concern about how to report these data.
  • Indigenous, Black and Racialized people may also have preexisting health conditions that are socially and economically determined, and these preexisting conditions may disproportionally affect this group more than others. Furthermore, reports suggest that Indigenous, Black and Racialized People have been infected more than others, and their health outcomes are more dire. Evidence informed decisions can lead to better outcomes for some groups, reporting the numbers can advance better and more targeted practices in community, hospital and in our cities.

Recommendation on the Reporting with Indigenous, Black and Racialized People categories:

a) The Province of Ontario Anti-Racism Directorate publishes a Data Standards for the Identification and Monitoring of Systemic Racism that includes

“guidance for race-based data collection for government and other public sector organizations, including steps to follow for data collection, management and use”.

Table 1. Valid Values for Race Categories on P.26 provides a useful classification system.  The Standard also includes protocols for the collection of self reported or observed data.

b) First Nation, Metis and Inuit in Canada may be collecting these data in their communities.  I will consult to see if that is the case and report back.

Final Remarks:

Health outcomes are intersectional, and age, sex, workforce and equity data provided additional insight about who is being affected, and knowing who and where can inform decisions about determinants of health, testing, improvement of health outcomes and planning. We have provided some insight in this post, about what is being reported and provided some recommendations. We will provide updates as more information is collected. We hope you find this useful and we welcome your comments and suggestions by email: tracey.lauriault@carleton.ca or on Twitter @TraceyLauriault.

 

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Reporting is becoming more sophisticated. The BC Centre for Disease Control (BCCCD) went from this landing page on the 13 of April, 3 days ago with data, maps, and charts as images on the page.

BC_CaseCountsPressStatement_BCCDC_13042020

To this page today 16 of April and data are now reported in an ESRI dashboard, and some data available for download! I think it is easier to read. I hope they will continue to report their excellent Surveillance Reports, here is an example from April 15, 2020. You can access those reports at the bottom of the landing page. What is great about the dashboard is that it is a collaboration between a number of Provincial Agencies BCCDC, PHSA, B.C. Ministry of Health and GeoBC Production. Below the image I have also pasted what they include on their Terms of Use, Disclaimer and Limitations of Liability page from the Dashboard.  The one issue with the dashboard, is you cannot download or link to specific pages.

BC-BCCDC_LandingPage_16042020
BC_COVID19_Dashboard_16042020

Below I copied and pasted the information directly from the Dashboard at 9:45 AM EST, 16 April 2020. It is useful to have this all in one place, including access to data, data sources and notes about the indicators. This comes from the Dashboard, and unfortunately I cannot hyperlink directly to this information.

Terms of use, disclaimer and limitations of liability

Although every effort has been made to provide accurate information, the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health makes no representation or warranties regarding the accuracy of the information in the dashboard and the associated data, nor will it accept responsibility for errors or omissions. Data may not reflect the current situation, and therefore should only be used for reference purposes. Access to and/or content of this dashboard and associated data may be suspended, discontinued, or altered, in part or in whole, at any time, for any reason, with or without prior notice, at the discretion of the Province of British Columbia.

Anyone using this information does so at his or her own risk, and by using such information agrees to indemnify the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health and its content providers from any and all liability, loss, injury, damages, costs and expenses (including legal fees and expenses) arising from such person’s use of the information on this website.

BCCDC/PHSA/B.C. Ministry of Health data sources are available at the links below:

Dashboard Usage Tips:

  • Hover over charts to see additional information.
  • Click the top right corner of any chart/window to make it full screen. Click again to return to the dashboard view.

Data Sources:

  • Case Details and Laboratory Information Data are updated daily Monday through Friday at 5:00 pm.
  • Data on cases is collected by Health Authorities during public health follow-up.
  • Confirmed cases include laboratory positive cases.
  • Laboratory data is supplied by the B.C. Centre for Disease Control Public Health Laboratory; tests performed for other provinces have been excluded.
  • Data on intensive care unit (ICU) admissions is provided by the PHSA Critical Care Working Group.
  • Test and case values may differ between amalgamated Health Authorities and B.C. as site locations are confirmed.

Data Over Time:

  • The number of laboratory tests performed and positivity rate over time are reported by the date of test result. On March 16, testing recommendations changed to focus on hospitalized patients, healthcare workers, long term care facility staff and residents, and those part of a cluster or outbreak who are experiencing respiratory symptoms. The current day is excluded from all laboratory indicators.
  • The number of new cases over time are reported by the date they are notified to public health.

Epidemiologic Indicators:

  • Cases are considered recovered after two lab-confirmed negative swabs taken 24 hours apart or when removed from isolation 10 days after symptom onset.
  • New cases are those reported daily in the PHO press briefing and reflect the difference in counts between one day and the next as of 10:00 am. This may not be equal to the number of cases reported by day, as cases reported prior to 10:00 am would have been included as New Cases in the previous day’s count. Because of the 10:00 am cut-off, the most recent day in time series graphs may contain only partial information. On Mondays, the number of new cases includes the number of new cases from Saturday and Sunday.
  • ICU values include the number of COVID-19 patients in all critical care beds (e.g., intensive care units; high acuity units; and other surge critical care spaces as they become available and/or required).

Laboratory Indicators:

  • Total tests represent the cumulative number of COVID-19 tests since testing began mid-January. Only tests for residents of B.C. are included.
  • New tests represent the number of COVID-19 tests performed in the 24 hour period prior to date of the dashboard update.
  • COVID-19 positivity rate is calculated as the number of positive specimens that day/total number of specimens tested (positive, negative, and indeterminate) that day.
  • Turn-around time is calculated as the daily average time (in hours) between specimen collection and report of a test result. Turn-around time includes the time to ship specimens to the lab; patients who live farther away are expected to have slightly longer average turn around times.
  • The rate of COVID-19 testing is defined as the cumulative number of people tested for COVID-19/BC population x 1,000,000 population. B.C. and Canadian rates are obtained from the Public Health Agency of Canada’s Daily Epidemiologic update site.

Health Authority Assignment:

  • Health Authority is assigned by place of residence; when not available, by location of the provider ordering the lab test.

Please direct questions and feedback to the BCCDC: Admininfo@bccdc.ca

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TracingCOVIDbanners-08It is very odd that national health organizations are not reporting COVID-19 cases aggregated into health regions even though provinces and territories are mostly reporting them in that way. And where is the national health framework datasets?

Framework data are a “set of continuous and fully integrated geospatial data that provide context and reference information for the country. Framework data are expected to be widely used and generally applicable, either underpinning or enabling geospatial applications” P.7.

Federal Electoral Districts for example, are the official framework data for Elections Canada and these data are updated for each election.  They are used to administer elections, report the results of exit polls during the elections, and show the results after an election.  Framework data are available in multiple formats as well as in cartographic or mapping products for Geographic Information Systems (GIS) such as ESRI, MapInfo or Tableau (Shapefiles), in KML formats for GoogleMaps, and in standardized online mapping GML Formats which also happens to also be a Treasury Board Secretariat of Standard for Geospatial Data. Election result data are aggregated into these framework data along with other socio-economic data, and once these data are mapped we can compare and can tell a more nuanced local, regional and national story, we can see patterns across the country.  The benefit of framework data are many, what is also great is they are created once by an authoritative source, they are updated and reliable, they are used many times, they are open data and everyone knows where to get them.

Considering that health care spending is one of the largest expenditures we have as a nation state, and it would be expected that in an era of accountability and transparency and where outcomes based management is the norm, it is astonishing that health data including its social determinants data are not disseminated in this way.  Yes, there are privacy issues, but we are capable of addressing those with the Census and Elections, which means we can also do so for health. We need to have an evidence based conversation about population health now more than ever, and we will need these data to tell a socio-economic story as well. Could we have done better? Who is doing great and why and who is not doing so great and why, what can we learn and what is the remedy?

Numerous useful and insightful interactive maps were published after the elections (CBC, CTV, Macleans, ESRI and many others), and these generated much discussion, people could see the results, they could situate themselves, they could see what friends and family in other places were experiencing.  Analysts and policy makers also had what they needed to understand and plan a new context. This is what democratic evidence based data journalism and policy making is all aboutt!

Natural Resources Canada is normally the producer of Canada’s framework data but it does not produce a health region framework dataset for Canada.  Arguably, these data would not only be useful during a pandemic, but also for administering and reporting health associated with natural resources such as allergies in the spring and fall, food insecurity, health and farming, or health after a natural disaster such as flooding and fires.  They data would also be useful to see where money is spent providing Canadians with the evidence they require to advocate for change.

So why no national heath reporting by their administrative boundaries and where is the health region framework dataset?

National Health Reporting Canada:

Virihealth.com and ESRI Canada produced the the first National ge0-COVID-19 reporting:

https://virihealth.com/

https://virihealth.com/

https://resources-covid19canada.hub.arcgis.com/app/eb0ec6ffdb654e71ab3c758726c55b68

https://resources-covid19canada.hub.arcgis.com/app/eb0ec6ffdb654e71ab3c758726c55b68

Federal Government:

Canada as a federation has jurisdictional divisions of power, and one of those jurisdictional  divides is health. We have the Canada Health Care Act (CHA) that

“establishes criteria and conditions related to insured health services and extended health care services that the provinces and territories must fulfill to receive the full federal cash contribution under the Canada Health Transfer (CHT)”.

The Canada Health Transfer (CHT) provides long-term predictable funding for health care, on a per capital basis and

“supports the principles of the Canada Health Act which are: universality; comprehensiveness; portability; accessibility; and, public administration”.

The provinces and territories receive cash transfers to deliver health care to Canadians and health care data reporting is done by the each province and territory separately. This alone justifies the creation of a national health region framework dataset. Which organization should be responsible for it?

There are three main organizations which are part of the Canada Health Portfolio  that currently report official COVID-19 cases. At the moment, they do not publish COVID-19 case data by health regions.

Health Canada “is the Federal department responsible for helping Canadians maintain and improve their health, while respecting individual choices and circumstances.” Health Canada is an official and authoritative national source of COVID-19 data and it publishes the Coronavirus disease (COVID-19): Outbreak update. Reporting includes an interactive map and a line graph of data by Province and Territory.

https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

Public Health Agency of Canada (PHAC) promotes and protects the health of Canadians through leadership, partnership, innovation and action in public health and it does so by: Promoting health; Preventing and controlling chronic diseases and injuries; Preventing and controlling infectious diseases; Preparing for and responding to public health emergencies; Serving as a central point for sharing Canada’s expertise with the rest of the world; Applying international research and development to Canada’s public health programs; and Strengthening intergovernmental collaboration on public health and facilitate national approaches to public health policy and planning. PHAC now disseminates an excellent interactive dashboard entitled the National Epidemiological Summary of COVID-19 Cases in Canada. Their data sources are: Public Health Agency of Canada, Surveillance and Risk Assessment, Epidemiology update; Natural Resources Canada – Grey basemap with Credit: COVID-19 Situational Awareness tiger team Powered by ESRI-Canada and COVID-19 Canadian Geostatistical Platform, a collaboration between Public Health Agency of Canada, Statistics Canada and Natural Resources Canada.

https://phac-aspc.maps.arcgis.com/apps/opsdashboard/index.html#/e968bf79f4694b5ab290205e05cfcda6

https://phac-aspc.maps.arcgis.com/apps/opsdashboard/index.html#/e968bf79f4694b5ab290205e05cfcda6

Canadian Institute for Health Research (CIHR) is the Government of Canada’s health research investment agency and its mandate is to “excel, according to internationally accepted standards of scientific excellence, in the creation of new knowledge and its translation into improved health for Canadians, more effective health services and products and a strengthened Canadian health care system.” Although a research funding organization, CIHR could publish a national framework dataset of health units to help researchers in Canada and to also to disseminate the findings of research either about COVID-19 or any other research according to those administrative boundaries. (Update 07/04/2020 CIHR does not have a framework data file)

A national non-governmental organization, the Canadian Institute for Health Information (CIHI) also disseminates national comparative health data, mostly about the administration of health and it would make sense for them to also publish data by health units and to have such a framework dataset. CIHI is an independent, not-for-profit organization that provides essential information on Canada’s health system and the health of Canadians. (Update 07/04/2020 CIHI does not have a framework data file). CIHI’s mandate is

“to deliver comparable and actionable information to accelerate improvements in health care, health system performance and population health across the continuum of care”.

Natural Resources Canada is the producer of most of Canada’s Framework data, and it could with the help of the Canadian Council on Geomatics Provincial and Territorial Accord could create this framework file and this was discussed at the 4th Annual SDI Summit meetings hosted in Quebec City in the Fall of 2019.

Statistics Canada produces Provincial and Territorial Health Geographies and it does seem to have a national GIS Health Regions: Boundaries and Correspondence with Census Geography file for 2018, and if that is the case, why are health geographies not reported by these boundaries? (Update 07/04/2020 StatCan has a 2018 GIS national health geography file).  Here is a PDF version of the 2018 map.

https://www150.statcan.gc.ca/n1/pub/82-402-x/2018001/maps-cartes/rm-cr14-eng.htm

https://www150.statcan.gc.ca/n1/pub/82-402-x/2018001/maps-cartes/rm-cr14-eng.htm

Provincial and Territorial Official COVID-19 Case Reports and health geographies:

Below I have compiled a list of official COVID-19 Case reporting by province and territory, and when I could find them, I included a link to health administration geographies. That does not mean that data are reported in maps, but data are generally tabulated according to health administration geographies.

Alberta

British Columbia

Manitoba (Updated RHA and Map info. 07/04/2020)

Newfoundland and Labrador (Updated RHA and Map info. 07/04/2020)

New Brunswick (Updated RHA and Map info. 07/04/2020)

North West Territories

Nova Scotia

Nunavut

Ontario

Prince Edward Island (Updated Health PEI info. 07/04/2020)

Quebec (Updated Map info 08/04/2020)

Saskatchewan

Yukon (Updated Health Region info. 07/04/2020)

I have emailed each of the Provincial and Territorial governments to confirm that I have the latest heath geography framework data.  I have received updates from Yukon, Quebec,  PEI, New Brunswick, and Manitoba, and have updated map data accordingly. I have also received correspondence from Statistics Canada, and CIHI.

For the moment ESRI Canada and some of the Provinces and Territories are reporting Official COVID-19 Cases by health region geographies.  Why aren’t Health Canada and the Public Health Agency of Canada doing so?  And where is the National Health Region Framework Data file?

TracingCOVIDbanners-08Across the country adhoc open data groups are meeting, holding hackathons online, they are making all sorts of apps, they are asking for data and want current data channels improved, they are making maps and deploying platforms, but also they are concerned about tracking and surveillance. These groups involve people from all levels of government, civic technology, open data, and the private sector. People are involved for all kinds of reasons and what is notable is that these are people who have agency, knowledge, and power combined with the capacity to act – the key ingredients for what Andrew Feenberg would call, technological citizenship. Doing technological citizenship is one way for people to engage in a technological society such as Canada, and in a very sophisticated and complicated information and technology situation such as a pandemic.

People’s intentions are good, but as the saying goes ‘the road to hell is paved with good intentions’ and caution and level headedness is required.

People involved in humanitarian work know this, and we have much to learn from them, and Patrick Meir is one of these great people. He shares in Digital Humanitarians: How Big Data is Changing the Face of Humanitarian Response what he learned during the 2010 Earthquake disaster in Haiti, and in other contexts. He is not alone, there is much to learn from the Signal Program on Human Security and Technology at the Harvard Humanitarian Initiative (HHI) at the Harvard T.H. Chan School of Public Health, the Responsible Data project and the Protection Information Management (PIM) initiative.

It is time to bring this overseas humanitarian crisis work home!

This is an exceptional time and right now we are witnessing the erosion of basic rights in exchange public good, as the situation is ‘evolving’, while a new form of data politics emerges with little or no discussion of data governance. The changes comes with an increase in surveillance and control, which might stay longer than we had thought and hoped for.

This too is not new, and the Signal Code work was developed precisely for this type of situation. These researchers advocate for a rights based approach for humanitarian information activities (HIA) work during a crisis, a pandemic is arguably a crisis, and they refer to the Humanitarian Charter and Minimum Standards in Humanitarian Response that starts with an understanding of dignity as being:

…more than physical well-being; it demands respect for the whole person, including the values and beliefs of individuals and affected communities, and respect for their human rights, including liberty, freedom of conscience and religious observance.

They also argue for a duty of care to be operationalized during the crisis, and I would argue that this should be done by us and our governors and administrators, so that we do not use this pandemic as reasoning to violate rights, to circumvent the law and to be negligent in our data and technology work. The COVID-19 pandemic is temporary, but the data collected and the technologies built will live beyond the crisis. There therefore a duty to be responsible now and to develop data governance strategies for the future.

The goal of the Signal Code is to develop ethical obligations for humanitarian actors including minimum technical standards for the safe, ethical, and responsible conduct of humanitarian information activities (HIAs) before, during, and after disasters strike. They provide the following five rights when conducting HIAs:

1. The Right to Information

Access to information during crisis, as well as the means to communicate it, is a basic humanitarian need. Thus, all people and populations have a fundamental right to generate, access, acquire, transmit, and benefit from information during crisis. The right to information during crisis exists at every phase of a crisis, regardless of the geographic location, political, cultural, or operational context or its severity

2. The Right to Protection

All people have a right to protection of their life, liberty, and security of person from potential threats and harms resulting directly or indirectly from the use of ICTs or data that may pertain to them. These harms and threats include factors and instances that impact or may impact a person’s safety, social status, and respect for their human rights. Populations affected by crises, in particular armed conflict and other violent situations, are fundamentally vulnerable. HIAs have the potential to cause and magnify unique types of risks and harms that increase the vulnerability of these at-risk populations, especially by the mishandling of sensitive data.

3. The Right to Privacy and Security

All people have a right to have their personal information treated in ways consistent with internationally accepted legal, ethical, and technical standards of individual privacy and data protection. Any exception to data privacy and protection during crises exercised by humanitarian actors must be applied in ways consistent with international human rights and humanitarian law and standards.

4. The Right to Data Agency

Everyone has the right to agency over the collection, use, and disclosure of their personally identifiable information (PII) and aggregate data that includes their personal information, such as demographically identifiable information (DII). Populations have the right to be reasonably informed about information activities during all phases of information acquisition and use. asbestos removal sydney | electric hoverboard | To know more on Direct2hr follow the links

5. The Right to Rectification and Redress

All people have the right to rectification of demonstrably false, inaccurate, or incompletedata collected about them. As part of this right, individuals and communities have a right to establish the existence of and access to personal data collected about themselves. All people have a right to redress from relevant parties when harm was caused as a result of either data collected about them or the way in which data pertaining to them were collected, processed, or used.

These are important to consider. I will come back to these in the coming days and I will point to insight provided by other who have first hand experience of doing data work during a time of crisis. I hope this is food for thought.

TracingCOVIDbanners-0814 days later!

Both Hugh and I agree, that it is time to use this platform again.

COVID-19 and cell phone data tracking is a Privacy Paradox par excellence! The the concept originally encapsulated how we were willing to trade-off the sharing of one’s data for the use of a ‘free’ social media platform.  We kinda’ knew that our data were being sold off to third parties, and traded by data brokers, and we sorta let it go, so we reacted by setting up some add blockers, adjusting our settings, using VPNs, or changing our browsers to things like DuckDuckGo. As imperfect as that situation was and is, that is what we did and it is what we do.

But cell phone tracking is something quite different.

Helen Nissembaum‘s Contextual Integrity (CI) is a very useful framework to think this through, for her “privacy, defined as CI, is preserved when information flows generated by an action or practice conform to legitimate contextual informational norms; it is violated when they are breached“. There are four CI theses as follows:

  • Thesis 1: Privacy is the Appropriate Flow of Personal Information
  • Thesis 2: Appropriate Flows Conform with Contextual Informational Norms (“Privacy Norms”)
  • Thesis 3: Five Parameters Define Privacy (Contextual Informational) Norms: Subject, Sender, Recipient, Information Type, and Transmission Principle
  • Thesis 4: The Ethical Legitimacy of Privacy Norms is Evaluated in Terms of: A) Interests of Affected Parties, B) Ethical and Political Values, and C) Contextual Functions, Purposes, and Values.

In terms of norms, social media is one thing, we do get upset when we find out that our photos are being used for facial recognition by our law enforcement institutions, when behaviour is tracked for targeted marketing purposes by data brokers or worse when scurrilous actors use our data to disrupt democracy. But our cell phone data, that is another level! We also know about UBER and smart phone provider transgressions but we seem to know very little about the Murkyness of Telecom Surveillance.  Furthermore, we are beginning to realize, that we cannot Privacy By Design (PbD) our way out of this, nor is cybersecurity enough, and that institutional and technological solutionism, falls short! We need to figure out how to govern these data practices right now.

These are exceptional times, circumstances are exceptional, the stakes are high, and the norms they are a changin’ . CI helps frame our thinking, although, Nissembaum also realizes that her thesis may need to reconsider how technology is an actor, while Teresa Scassa in Private Sector Data, Privacy and Pandemics and Michael Geist both warn us about the new normal, they also define and categorize types of data in a pandemic situation to help us out, frame their analysis with issues pertaining to law, policy and governance, and provide ways to circumscribe how these data might be shared to serve the public good or interest at this time.

But, who will govern this, and for long will this ‘sharing’ & tracking go on for?

What is for sure, just like 911 set new benchmarks in terms of what kind of surveillance we wound up ‘living with’,  COVID-19 will change data and technological monitoring norms. This may also be a time where we might change the course what surveillance we will accept, as presumably we are smarter now! There are perils and there are opportunities. How will we govern ourselves and our data during and post the pandemic era!

Below is a smattering of news articles on the topic:

LF_Census   Recensement_Long

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