Where did public health go wrong? Seven lessons from the COVID-19 pandemic
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has been the public health moment of the century. While there is much to celebrate about the public health response, this is a good time to take stock. In this editorial, we pinpoint seven areas for a better public health response to a potential future pandemic. Evidence of the disproportionate impact of COVID-19 emerged early in the pandemic. However, early health system responses, including testing and vaccination services, were built on the pre-pandemic inequitably-distributed infrastructure, often in White middleto-upper class neighborhoods, creating ‘access deserts’ that exacerbated ethno-racial, socioeconomic and rural/urban disparities.1 Recommendation 1: Public health services, including those contracted to commercial pharmacies, must be planned based on historic and spatial inequities in infrastructure. Most infectious disease (ID) models used to predict the course of the pandemic and forecast spatiotemporal and epidemiological trends used a population-averaged approach that did not sufficiently account for social and structural factors, unequal risk distribution and disproportionate policy impacts.2 Policy decisions based on these models may have exacerbated health inequities. Pandemic response necessitates a delicate equilibrium between the imperative to promptly curb infection transmission and the long-term societal impact of public health measures. While COVID-19 policy tables widely considered this difficult balance, there was significant heterogeneity between countries in decisionmaking approaches and whether long-term trade-offs were quantitatively incorporated in ID models to explicitly reflect social preferences. Relatedly, ID models rarely considered the interaction between public health and financial/social policies (such as changes in interest rates and increase in shared housing), resulting in inequitable burden of infection. Recommendation 2: ID and decision modeling guidelines should be improved and societal preferences should be elicited to facilitate models that explicitly consider a long-term societal perspective and tradeoffs between health and non-health outcomes. The pandemic and its response were associated with an increase in mental health and addictions challenges related to disease burden (e.g. illness and death) as well as stay-at-home orders, loss of employment and financial worries, school closures, domestic violence, loneliness and heightened concern of health outcomes. For many, home simultaneously became ‘castle and cage’, compounded by closure of mental health services. Implementation of virtual care was patchy, uncoordinated, and fraught with technical and implementation challenges, leading to a ‘digital divide’ that exacerbated inequities. Recommendation 3: Digital interventions and virtual delivery of mental healthcare should be equity-conscious and integrated with social services to address the underlying cause(s) of mental health challenges. COVID-19 vaccine coverage was inequitable, both within and between countries. In a review of 117 studies, Bergen et al.3 found that 86% of studies reported coverage disparity based on race/ethnicity, culture, language and/or country of birth. Vaccine solidarity failed once the most advantaged no longer perceived themselves as being at significant risk. Globally, vaccine nationalism and patent protectionism led to advance purchase agreements, vaccine-hoarding, pricing-out of low-income countries and failure of patent waiver proposals.4 Global vaccine scarcity was compounded by structural barriers, resulting in significant inequities in pandemic deaths. Recommendation 4: Global initiatives, such as the World Health Organization ‘mRNA Vaccine Technology Transfer Hub’, should be supported with infrastructure investment and technology sharing, to produce timely, affordable, and patent-free vaccines for the Global South. Pandemic-related misinformation, fueled by conspiracy theorists, pseudoscientists and polarized political camps on social media, exacerbated vaccine hesitancy, displaying a strong social gradient that was correlated with disparity in health literacy. Public health counter-messaging was often overly technical, failing to address key concerns of vaccine-skeptics. Additionally, popular media platforms were under-utilized and population heterogeneity was overlooked, contributing to communication inequalities. Recommendation 5: Public health misinformation on social media should be better regulated. Countermessaging efforts should involve community leaders and social media influencers, adopting a nontechnical approach tailored to the audience. Beyond factual information, public health campaigns should incorporate storytelling, visuals and emotional appeals. While health and social care workers, bearing infection risk for public safety, were praised globally, their relentless work was not always compensated. Other ‘customer-facing’ professions, such as public transport workers, received neither praise nor compensation. Reciprocal obligations also apply to safety (i.e. proper protective equipment) and health and social support for physical and mental health; numerous accounts reported inadequacy of both.5 Recommendation 6: Prioritize investment in health, safety and well-being of frontline workers, and compensate high-risk jobs with dollars, not just praise, particularly for low-income workers. The pandemic was associated with an increase in racial, political and religious hatred and intolerance. Cases of Sinophobic hate crimes, including discrimination, verbal harassment and physical violence, were common in the West. In other countries, far-right groups targeted religious minorities, scapegoating them for the pandemic to foster divisions and incite violence. Recommendation 7: Hate crimes should be judicially recorded and systematically addressed, ensuring comprehensive data collection to inform legislation, targeted policies, law enforcement strategies, and societal initiatives to combat such offenses. The public health community should critically evaluate its response to the COVID-19 pandemic to be better prepared for future health emergencies. The pandemic has unraveled structural inequities, health system inadequacies and individual vulnerabilities. It has, however, also presented opportunities to enhance societal adaptability and resilience, community solidarity, health system innovation and understanding of social injustices. Conflicts of interest: None declared. Dr. Ali is funded through the Canada Research Chair program; however, the funders did not have any role in this work. No new data were generated or analysed in support of this research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.043 | 0.049 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".