Opinion

Why equity indicators are essential in COVID-19 monitoring

The COVID-19 pandemic has profoundly challenged governments’ abilities to effectively respond to public health threats. Careful analysis of what happened and what should have happened undoubtedly will reveal problems ranging from weaknesses in disease surveillance to limited supplies of personal protective equipment and challenges in how emerging evidence is communicated and used by researchers and decision-makers.

Unarguably, a profound weakness has been the equity-blind approach that numerous jurisdictions adopted when the pandemic began despite widespread recognition of the inequitable ways infectious diseases impact societies.

Early in the pandemic, Canada was unprepared for both the rapid spread of SARS-CoV-2 and its impact on health systems. Once its spread became undeniable, many provinces’ pandemic responses were blunt, with the single goal of reducing contact between people and, by extension, virus transmission. Although these measures helped reduce infection rates and likely saved thousands of lives, some researchers and public health officials advocated early on for a focus on the pandemic’s inequitable impact, underscored by statistics from virtually every jurisdiction that consistently showed an overrepresentation of COVID-19 among racialized, low-income and other marginalized populations. These populations are a significant portion of the essential workforce who, directly or indirectly, impact all society members’ lives and health. This overrepresentation underlined the disconnect between governments’ goals of reducing the impact of COVID-19 and its disruption to society.

Many governments at all levels have been late to introduce policies that address such disparities.

There is no universal definition of health equity. As such, we define it here as the minimization of avoidable health disparities and their determinants across groups based on social determinants of health or privilege from socioeconomic status, gender identity, race, education and other categories of difference. Across Canada, some researchers have applied a health-equity lens to analyze and address COVID-19. However, variations exist in how health-equity lenses are applied, if at all, to disease reduction. This is compounded by the disparate approaches and frameworks to incorporating health equity into these efforts despite the connection between improving social determinants and health.

These variations have led to disease rates concentrated in racialized and low-income neighbourhoods. Many governments at all levels have been late to introduce policies that address such disparities. For example, early into the pandemic, the Ontario government made COVID-19 a reportable disease in January 2020 and introduced broad public health measures that spring. Although researchers noted disparities across socio-economic strata early in the pandemic, equity focused provincial responses only began midway through the second wave, save for a moratorium on housing evictions announced in the first wave. These delayed and inconsistent responses prompted groups such as hospitals, community health centres and grassroots organizations to develop their own interventions to address disparities within the communities they serve.

Canadian COVID-19 case data makes it clear that any health-equity approach or framework must address the factors that render racialized and low-income populations at heightened risk of exposure to and death from COVID-19.

The need for this approach is apparent in Ontario, the province with the highest COVID-19 case numbers in the country.

Without a standardized province-wide approach to collecting high-quality data on social factors, a number of public health units have taken the initiative to extrapolate COVID-19’s impact on vulnerable populations. For instance, the province’s largest public health unit, Toronto Public Health (TPH), collects data on some of the key social determinants of health (e.g., racial identity, income, household size). Data collection foci (e.g., asking sociodemographic questions) are informed by disease evidence from other locales and these foci greatly impact other health outcomes. Findings are intended to inform TPH, the City of Toronto as well as city health care and collaborators in the community on efforts to address COVID-19 disparities, particularly in neighbourhoods and populations hardest hit by the virus. 

Toronto’s dashboard appears to be one of the few Canadian COVID-19 dashboards to explicitly report on equity and race-based data.

Data have revealed startling, yet unsurprising disease trends. For instance, most positive COVID-19 cases (72 per cent) have come from racialized populations and 44 per cent are from low-income households. Racialized and newcomer communities are frequently overrepresented in low-wage, front-facing essential services, where they face higher risk of transmission because of housing, the need to continue (essential) work and difficulties in accessing critical resources such as personal protective equipment. These factors, combined with comorbidities, exposure to racism, social exclusion and neighbourhood deprivation increase their COVID-19 risk.

In the fall of 2020, TPH was tasked with creating a set of COVID-19 equity indicators that are embedded into the city’s COVID-19 monitoring dashboard. This dashboard appears to be one of the few Canadian COVID-19 dashboards to explicitly report on equity and race-based data. It also has indicators on: virus spread and containment; laboratory testing; health-care system capacity; and public health. The dashboard provides information on the current state of the pandemic and monitors variations in the city’s disease rates over time. The equity category centres on reporting income, race and neighbourhood inequities connected to COVID-19 vaccination and case rates. These indicators can highlight areas in need of additional attention, scrutiny and action. Further, they help researchers, policy makers, officials and community members monitor populations at heightened risk of exposure, progress at tackling COVID-19 infection rates and infection rate disparities.

Public health actions informed by these data may have enhanced the equity of these interventions and helped reduce COVID-19 rates in these populations. For instance, the first analysis of socio-demographic data by TPH in July 2020 revealed that Blacks and Latinos had 6 to 11 times higher COVID-19 case rates than whites. These data led the city to promptly implement several interventions in hard hit areas and hold community consultations to improve equity in the city’s disease response. Interventions ranged from multi-lingual public health advertising to free emergency childcare.

The recognition of inequity shaped the city’s COVID-19 policies and helped protect the lives of populations at increased risk of disease exposure and infection. This impact reconfirms the need to move away from aggregated data collection, a commonly used collection approach in Canadian health institutions. Making this disaggregated indicator data readily available ensures that health resources can be distributed in a way that better reflects populations’ needs. As noted by a study assessing health-equity focused COVID-19 reporting in Canada, failure to report data based on at-risk settings or social markers may hide larger COVID-19 related disparities.  

Stating that equity is a component of a COVID-19 response is insufficient without explicitly stating how it is being assessed.

Toronto’s dashboard offers other municipal and national health institutions a model of how they can embed equity into their COVID-19 reporting and planning. This can be done in two key ways:

Include equity indicators in COVID-19 dashboards: Stating that equity is a component of a COVID-19 response is insufficient without explicitly stating how it is being assessed, measured and acted upon. This information can help health institutions better understand disease spread and potentially identify and work toward addressing underlying inequities. To gather pertinent information, researchers must critically examine the pre-existing factors that exacerbate poor health outcomes and use them to inform their indicators.

In addition, indicators must also be used to assess the equity of health-care institutions’ responses. This assessment is important as the City of Toronto states that its equity indicators do not identify strategies to address inequities, but these data can be used to evaluate the state and/or extent of equity across different strategies. Doing so may help ensure strategies are critically examined for their ability to provide high-quality, effective and accessible services that match population needs.

Another call for disaggregated data collection in health care: Although this suggestion is not new and some Canadian health-care scientists have been calling for a concerted effort to collect these data for years, COVID-19 has further underscored its need alongside other significant, preventable health-care knowledge gaps. If done correctly and supported by community agencies and/or stakeholders that can contextualize data, they may be used to enhance health care as a whole. 

As health-care organizations continue to be forced to evolve their COVID-19 responses, having a COVID-19 dashboard that points to emerging disease inequities in real time is vital. This type of dashboard may not only show trends but help save the lives of those often overlooked by traditional approaches to disease monitoring.

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Authors

Nakia K. Lee-Foon

Contributor

Dr. Nakia K. Lee-Foon (PhD) is a postdoctoral fellow at the Dalla Lana School of Public Health at the University of Toronto whose work ranges from examining the state of equity in health-care systems to exploring the sexual health literacy of young, self-identified Black men who have sex with other men in Toronto.

Adalsteinn Brown

Contributor

Dr. Adalsteinn Brown is the Dean and a Professor at the Dalla Lana School of Public Health, University of Toronto. His work focuses on health policy, health-system performance measurement and enhanced training programs for PhD students. 

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