Ontario is starting to collect race-based COVID-19 data. Some worry it could do more harm than good

Sigh. Yes, groups should be consulted, yes, the data should be made public, but hard to see that minorities will be worse off with data than without.

Having better data facilitates discussion of current realities and possible policy options to address disparities:

With Ontario’s race-based COVID-19 data collection beginning “imminently,” health experts say crucial unresolved questions will determine whether those efforts help alleviate the pandemic’s brutal disparities, or cause more harm.

Regulatory changes came into effect last Friday that mandate the collection of information on race for all newly reported COVID-19 cases province-wide, along with data on income, household size and languages spoken. Data collection is beginning once training for public health units and changes to data entry systems are complete, according to a health ministry spokesperson.

Community organizations, researchers, doctors and public health experts have called for the collection of this data, pointing to the disproportionate burden of COVID-19 in areas with more racialized, low-income and newly immigrated residents.

But health researchers said the question of how this data is managed and used is even more important than whether it is collected.

“The collection of race-based data is not the outcome,” said Camille Orridge, a senior fellow at the Wellesley Institute and longtime advocate for health equity data collection. “The outcome is to have the information and use the information to reduce disparities. That’s the goal.

“We need to be clear with people who are collecting the data — government, etc. — that there are a number of things that must be answered before we come to the table to give up the data,” she said.

Orridge cited a list of questions, including whether the data will stay in Canada, whether it will be sold in any form to the private sector, how artificial intelligence will be used with the resulting databases. And most importantly, for her: whether the racialized communities most affected will have oversight and input on whether the data is being used to answer questions and create policies that counter the pandemic’s unequal toll.

She cited a phrase often used in the world of Indigenous policy: “Nothing about us, without us.”

Alexandra Hilkene, the health ministry spokesperson, said “We’re currently in the process of finalizing the terms of reference for the working group that will report to the ministry and help ensure we interpret the data accurately. The group will include policy experts from racialized communities.”

In Toronto, some of the neighbourhoods most affected by COVID-19 have case rates 14 times higher than the least affected neighbourhoods. Those hard-hit neighbourhoods are all clustered in the northwest of the city, an area that has been historically underserviced and has higher rates of poverty, inadequate housing, and other symptoms of systemic disadvantage.

The city’s most affected areas also have significantly higher percentages of Black residents than the least-affected areas, and higher percentages of Southeast Asian and other racialized groups. But health experts say these area-based analyses, which rely on matching the postal codes of known cases to census data, are less revealing than collecting the data directly from individuals.

Toronto, Peel Region and some other health units have already begun collecting this data, but officials argued that it should be mandated province-wide to provide a complete picture. After weeks of urging, the province made regulatory changes to the Health Protection and Promotion Act to mandate the collection of race and sociodemographic information for COVID-19.

But now that the government is about to begin collecting that data, it shouldn’t be exclusively available to them, said Arjumand Siddiqi.

“I would worry that if the data stays in the domain of the government, or if they handpick a small group of people to use it and no one else sees it, we have to rely on what those people tell us,” said Siddiqi, Canada Research Chair in population health equity and a professor at the University of Toronto’s Dalla Lana School of Public Health.

Making the data available more broadly ensures that independent researchers can check the work of others, rebut flawed analyses and conclusions, and ask different kinds of questions.

But Orridge said it’s also important to ensure that the researchers who do get access to race-based COVID-19 data have real relationships in and accountability to the communities that are most affected.

“We have researchers who have no connection to the communities having access to the data, and making their careers on the use of that data,” said Orridge.

“We’ve got to make sure that the data, when it’s being used and published, always has a context, so that we don’t further stigmatize communities.”

LLana James, a doctoral candidate at the University of Toronto Faculty of Medicine who researches race-ethnicity, health data, privacy, AI and the law, noted that Ontario and Canada collect health data in a legal framework that has failed to catch up to the massive technological changes that have occurred, especially in the last decade with the rise of machine learning.

“We have one of the lowest thresholds for legal use of data in the developed world,” said James, noting that technology companies see Ontario as an attractive market for lucrative health-care data, and contrasting Canada’s poor data privacy protections with Europe’s robust framework.

James provided critical comments on the province’s proposed regulatory changes to begin collected race-based COVID-19 data, and believes the current, government-driven data efforts will not help Black, Indigenous and other racialized communities.

Race-based data assumes that “we need to know the race of the person, not how racism is functioning. Those are two completely different scientific questions,” James said.

“We have 400 years of data about what happens to Black people during pandemics,” said James. “We have hundreds of years of race-based data, and it’s changed very little. It’s the will to act (that’s missing), not the will to collect more stuff.”

Like Orridge, however, she believes that any data collection that avoids harm must be centred in and directed by communities. James is the co-lead of REDE4BlackLives, a research and data collection protocol that provides a framework for the ethical engagement of Black communities in Canada.

“Black communities, like Indigenous communities, know exactly what they need,” says James. “They know who advocates for them. They know who shows up for them. And they know who to trust, because they see it with their own eyes.”

Beware of COVID-19 projections based on flawed global comparisons

Continuing on the data question, found this to be a good explainer given the variances in how data is collected across jurisdictions:

As the COVID-19 pandemic unfolds, every day we are bombarded with numbers. Never before has the public been exposed to so much statistical information. You have been told that “shelter in place” measures are needed to flatten the curve of infections so that local healthcare systems have the capacity to deal with them. On the other hand, you hear that available statistics will not show if and when the curve of infections is flattening, and that existing projections are unreliable because input data are unsuitable for forecasting. Meanwhile, the issue of data and the pandemic fuels a debate in Canada over the release of federal and provincial forecasts of a COVID-19 death toll.

Should we then lose faith in the numbers altogether? The answer is no, but it is important to understand what statistics are available, what they measure, and which ones we should be looking at as the virus continues to spread around the world. One of the key areas where we need to exercise caution is especially when we compare ourselves with the situation in other countries.

As overwhelming as the flow of daily pandemic statistics might seem, data on COVID-19 around the world come from one source: health facilities’ administrative reporting about the number of positive cases, hospitalizations, intensive therapies, deaths, and recoveries. Most countries including Canada follow the guidelines of the World Health Organization and only test individuals with fever, cough, and/or difficulty breathing. Reported data on COVID-19 thus generally refer to symptomatic individuals who have presented themselves at health facilities and have met the established testing criteria.

One of the main indicators derived from these data is the overall case-fatality rate (CFR), which is the ratio between the total number of COVID-19-related deaths and the total number of confirmed positive cases. The CFR is an important indicator in an emerging pandemic because it measures the severity of the disease (how many infected people die from it). As of March 24, the CFR varied substantially across countries, ranging from 0.4 percent in Germany to 7.7 percent in Italy. In Canada and Quebec, it stands at 1.3 percent and 0.7 percent respectively.

It is well understood that different testing strategies for COVID-19 are responsible for a good part of the observed differences in the overall case-fatality rate across countries. For instance, South Korea, Germanyand Iceland adopted a large-scale testing strategy since the beginning of the outbreak, focusing on individuals in the wider population regardless of whether they were high risk or showing symptoms of COVID-19. Most other countries including Canada are following the recommendations of the World Health Organization to test only for COVID-19 symptomatic individuals.

These different testing strategies have a direct impact on the overall CFR because its value is smaller if asymptomatic individuals are included in the calculation, since the total number of positive cases (the denominator) increases. This is the first reason why the CFR is not immediately comparable across countries and should not be used as a measure of whether certain healthcare systems are dealing better with COVID-19 than others.

The second reason is that different testing strategies across countries also matter for the demographics of confirmed positive cases. As it can be seen in the figure below, because of widespread testing in Iceland, the age distribution of COVID-19 positive cases is much younger than in the Netherlands. This does not mean that younger people in Iceland are not respecting social distancing measures, or that the Netherlands has been more effective than Iceland in identifying infections among vulnerable elderly people. On the contrary, countries like Iceland that have effectively tested for COVID-19 early on have been able to identify and isolate clusters of potential infections before they spread to the more vulnerable segments of the population. By doing so, they have limited the number of COVID-19-related deaths and thus reduced the numerator in the calculation of the overall CFR. This is why the demographics of positive cases needs to be considered in the calculation of the overall case-fatality rate to make appropriate comparisons across countries.

The different demographics of COVID-19 positive cases underscore the importance of comparable data that are disaggregated by the patients’ most basic characteristics, notably age and sex. However, these data are only available for a handful of countries, because national health agencies release mainly aggregate figures on the total number of cases, hospitalizations, deaths and recoveries.

We all want to know how the COVID-19 pandemic will evolve. Considering the deep economic implications of the current worldwide standstill, there is a strong pressure to produce projections of the course of the pandemic and its human toll. Yet our efforts will continue to be misguided if we do not coordinate efforts to improve our understanding of where it is across countries through comparable statistics. This could be easily achieved by tracing the evolution not just of the total number of infections and the overall CFR, but also across age groups and for men and women separately.

National health agencies have been disseminating data and indicators about COVID-19 as they see fit because there is no global coordination about how to do so. The World Health Organization has not fulfilled its mandate to facilitate this coordination. Canada, thanks to its longstanding tradition of excellence in statistical reporting, is ideally placed to fill this gap and lead countries around the world to coordinate their monitoring efforts of the pandemic through comparable statistics. This may be one of the crucial steps to win the war against COVID-19.

Source: Beware of COVID-19 projections based on flawed global comparisons

No time to fly blind: To beat COVID 19, Canada needs better data

As we always do! Bit surprised no discussion of what role the Canadian Institute of Health Information (CIHI) could play:

Accurate information is critical to fight a health emergency like the COVID-19 pandemic. Robust data identifies the scale of the problem. It enables the prioritization of human, financial and material resources for an effective and efficient response. It allows for public scrutiny, advocacy and accountability. It builds trust. It provides authorities with tools to counter misinformation. It will enable us to slowly and safely return to economic and social activity.

In short, good data can mean the difference between life and death – or in the case of a pandemic, tens of thousands of deaths. Yet in the face of the greatest international health crisis in a generation, Canada is falling short.

Prime Minister Trudeau promised better data. To deliver on this promise, the Public Health Agency must mandate standardized information reporting for provincial and district public health authorities. These standardized templates would outline the data and information to be reported, how it should be collected and how it should be shared. Moreover, the Agency should urgently provide financial and technical resources to improve information management at all levels of the public health response.

At first glance COVID-19 data appears to be plentiful – case numbers and graphs are splashed across news reports and public health websites. Public health agencies produce epidemiological reports with colourful graphs and charts. Officials quote modelling estimates of projected case numbers and fatalities.

But in reality the value  of this information is limited. Efforts to fill in information gaps with modelling is a short-term and imperfect substitute for real-time data.  The data that does exist is of questionable validity given low testing numbers within the population and delays in receiving test results. Moreover, the data is not gathered, compiled or presented in a consistent manner by health authorities across the country. Different case definitions make comparison within and across provinces difficult. Sex and age disaggregated figures are not always provided. Some areas report hospitalization and intensive care unit numbers, some do not. Warnings of medical equipment and personal protective equipment (PPE) shortages are widespread, yet inventories of PPE stockpiles are frequently not given.

Moreover, public health officials report cases but do not discuss population context. They do not present important statistics about communities including age, sex and socio-economic data and specific vulnerabilities. Authorities rarely provide information on the number of health workers employed in the response, hospital beds available or PPE stockpiles. Officials cite testing numbers with little concrete data on laboratory capacity or efforts to expand it.

It is confusing. Overwhelming. And unhelpful.

Without accurate data and information, authorities cannot identify and manage human, financial and material resources to engage in the fight against COVID-19.  Nor can they monitor the effectiveness of interventions and stop its spread.

We can do better. During humanitarian crises, which often occur in data-scarce contexts, central coordinating bodies prioritize the collection and transparent dissemination of information. They develop standardized “Situation Reports” at multiple levels – the community, the region and the country – to identify need, prioritize interventions and target scarce human, material and financial resources. In the health sector, reports include population size disaggregated by sex, age and vulnerability; the number of health facilities in operation; key causes and rates of illness and mortality; medical procedures and treatment courses. These reports are published openly and disseminated widely. Information is critical for an effective and efficient response in complex and rapidly changing environments. It allows resources to be targeted to save lives.

COVID-19 warrants something similar. We need to understand the progress of the disease and our response – in real time. Proper information management will not only improve the effectiveness of our interventions, but it will also enable the safe resumption of economic and social activity.

A standardized reporting template would include case numbers and hospitalizations (sex and age disaggregated).  But counting the numbers of outbreak cases is only one piece of the information puzzle. Reports should include community baseline data. Important information includes population demographics (age, sex and particular vulnerabilities), neighbourhoods with higher risks, and the number of vulnerable institutions (retirement and nursing homes, corrections facilities). Authorities would identify financial, human and material resources available and required. Reports should document laboratories with COVID-19 testing capacity and provide inventories of PPE.

Better data would allow us to identify critical intervention points to stop the spread of COVID-19 and to slowly get our lives and economy back on track. The lack of prioritization on testing is both a symptom and a consequence of Canada’s failure to prioritize information management. Given testing capacity, public health officials discourage tests for those with mild symptoms. This undermines the validity of most of the numbers used by public officials to track the COVID-19 outbreak. Without the total ‘real’ numbers of individuals infected, we lack an accurate denominator, which undermines the accurate calculation of hospitalization or case fatality rates. Lag times in test results also make accurate contact tracing very difficult.

More critically, without expanded testing, we lack the ability to quickly test health workers and those employed in other essential services (such as retirement homes) to protect them, their co-workers, patients or residents and the public. Nor do we have the ability to test people to gradually and safely scale up economic and social activity. Instead we are told to wait for testing innovations while COVID rates numbers rise. Yet many private labs as well as lab facilities in university and colleges remain unutilized over three weeks into Canada’s full scale COVID-19 response. With better information would come increased accountability for mobilizing such capacity.

COVID-19 has sparked one innovation in information production – the use of outbreak models to guide public health responses to COVID-19, often funded by public health authorities. The federal government recently provided $192 million to BlueDot – a Toronto based digital health firm, not a university research department – to support its modelling activities. After calls to release modelling estimates, some provincial governments have provided projections of case and mortality numbers.

But transparency warrants more. Modelling in general is extremely challenging and COVID-19 modelling is particularly complex. Population demographic characteristics appear to determine the speed of COVID-19 transmission as well as severe illness, hospitalization and fatality rates. While the professionalization of the modellers is not in question, research driving policy decisions should be published openly and subject to scrutiny. The lack of clarity contrasts unfavourably to models published in scientific journals, or those published online by Professor Neil Ferguson of Imperial College, University of London. If governments release model estimates, they should release the assumptions and data that inform these estimates.

Moreover, modelling is an imperfect and flawed substitute for real data and concrete information about the response. Policy makers urgently need to pay attention to the generation and management of accurate and valid data, mandate standardized reporting from all public health authorities and provide public funds to make it happen.

We are in unprecedented public policy territory. Yet we lack the information needed to effectively navigate the COVID-19 pandemic and get our economy and our lives back on track. Prime Minister Trudeau’s commitment to better data and improved information management provides Canada’s Public Health Agency with the opportunity to exercise leadership. It is time to up our game.

Source: No time to fly blind: To beat COVID 19, Canada needs better data