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Enregistrement W4392791286 · doi:10.1093/eurpub/ckae042

Where did public health go wrong? Seven lessons from the COVID-19 pandemic

2024· editorial· en· W4392791286 sur OpenAlexafffund
Shehzad Ali, Maxwell J. Smith, Saverio Stranges

Notice bibliographique

RevueEuropean Journal of Public Health · 2024
Typeeditorial
Langueen
DomaineHealth Professions
ThématiqueFood Security and Health in Diverse Populations
Établissements canadiensWestern University
Organismes subventionnairesCanada Research Chairs
Mots-clésCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthBetacoronavirusVirologyPolitical scienceMedicineOutbreakInfectious disease (medical specialty)Nursing

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,026
score de la tête « metaresearch » (Gemma)0,076
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,135

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0260,076
Méta-épidémiologie (sens strict)0,0040,002
Méta-épidémiologie (sens large)0,0050,003
Bibliométrie0,0050,003
Études des sciences et des technologies0,0070,007
Communication savante0,0190,010
Science ouverte0,0050,004
Intégrité de la recherche0,0430,049
Charge utile insuffisante (le modèle a refusé de juger)0,0110,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,421
Tête enseignante GPT0,507
Écart entre enseignants0,085 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2024
Routes d'admission2
Résumé présentnon

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